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mlperf_tra
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60 changed files with 3242 additions and 287 deletions
1
.gitignore
vendored
1
.gitignore
vendored
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@ -11,6 +11,7 @@ notebooks
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*.txt
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build
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!examples/tinychat/assets/cdn.jsdelivr.net/npm/purecss@3.0.0/build/
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!examples/mlperf/training_submission_*/**/*.txt
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/dist
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*.egg-info
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/env
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@ -358,11 +358,43 @@ def train_retinanet():
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config, target_metric = {}, 0.34
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config["SEED"] = SEED = getenv("SEED", random.SystemRandom().randint(0, 2**32 - 1))
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Tensor.manual_seed(SEED)
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NUM_CLASSES = len(MLPERF_CLASSES)
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BASEDIR = getenv("BASEDIR", BASEDIR)
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BENCHMARK = getenv("BENCHMARK")
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# INITMLPERF = getenv("INITMLPERF")
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INITMLPERF = getenv("INITMLPERF")
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RUNMLPERF = getenv("RUNMLPERF")
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if getenv("LOGMLPERF"):
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from mlperf_logging import mllog
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import mlperf_logging.mllog.constants as mllog_constants
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mllog.config(filename=f"result_retinanet_{SEED}.log")
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mllog.config(root_dir=Path(__file__).parents[3].as_posix())
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MLLOGGER = mllog.get_mllogger()
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MLLOGGER.logger.propagate = False
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if INITMLPERF:
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assert BENCHMARK, "BENCHMARK must be set for INITMLPERF"
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MLLOGGER.event(key=mllog_constants.SUBMISSION_ORG, value="tinycorp")
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MLLOGGER.event(key=mllog_constants.SUBMISSION_PLATFORM, value=getenv("SUBMISSION_PLATFORM", "tinybox"))
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MLLOGGER.event(key=mllog_constants.SUBMISSION_DIVISION, value=mllog_constants.CLOSED)
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MLLOGGER.event(key=mllog_constants.SUBMISSION_STATUS, value=mllog_constants.ONPREM)
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MLLOGGER.event(key=mllog_constants.SUBMISSION_BENCHMARK, value=mllog_constants.RETINANET)
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diskcache_clear()
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MLLOGGER.event(key=mllog_constants.CACHE_CLEAR, value=True)
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MLLOGGER.start(key=mllog_constants.INIT_START)
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if RUNMLPERF:
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MLLOGGER.start(key=mllog_constants.RUN_START)
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MLLOGGER.event(key=mllog_constants.SEED, value=SEED)
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else:
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MLLOGGER = None
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config["gpus"] = GPUS = [f"{Device.DEFAULT}:{i}" for i in range(getenv("GPUS", 6))]
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for x in GPUS: Device[x]
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@ -415,24 +447,21 @@ def train_retinanet():
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return out.to(GPUS[0]).realize()
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# ** hyperparameters **
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config["seed"] = SEED = getenv("SEED", random.SystemRandom().randint(0, 2**32 - 1))
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config["bs"] = BS = getenv("BS", 16 * len(GPUS) if dtypes.default_float == dtypes.float16 else 12 * len(GPUS))
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config["eval_bs"] = EVAL_BS = getenv("EVAL_BS", BS)
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config["epochs"] = EPOCHS = getenv("EPOCHS", 4)
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config["train_beam"] = TRAIN_BEAM = getenv("TRAIN_BEAM", BEAM.value)
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config["eval_beam"] = EVAL_BEAM = getenv("EVAL_BEAM", BEAM.value)
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config["lr"] = lr = getenv("LR", 9.5e-5 * (BS / 96))
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config["loss_scaler"] = loss_scaler = getenv("LOSS_SCALER", 2**11 if dtypes.default_float == dtypes.float16 else 1.0)
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config["default_float"] = dtypes.default_float.name
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config["eval_freq"] = eval_freq = getenv("EVAL_FREQ", 1)
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config["BS"] = BS = getenv("BS", 16 * len(GPUS) if dtypes.default_float == dtypes.float16 else 12 * len(GPUS))
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config["EVAL_BS"] = EVAL_BS = getenv("EVAL_BS", BS)
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config["EPOCHS"] = EPOCHS = getenv("EPOCHS", 4)
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config["TRAIN_BEAM"] = TRAIN_BEAM = getenv("TRAIN_BEAM", BEAM.value)
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config["EVAL_BEAM"] = EVAL_BEAM = getenv("EVAL_BEAM", BEAM.value)
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config["LR"] = lr = getenv("LR", 9.5e-5 * (BS / 96))
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config["LOSS_SCALER"] = loss_scaler = getenv("LOSS_SCALER", 2**11 if dtypes.default_float == dtypes.float16 else 1.0)
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config["DEFAULT_FLOAT"] = dtypes.default_float.name
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config["EVAL_FREQ"] = eval_freq = getenv("EVAL_FREQ", 1)
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# ** initialize wandb **
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if (WANDB:=getenv("WANDB")):
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import wandb
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wandb.init(config=config, project="MLPerf-RetinaNet")
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if SEED: Tensor.manual_seed(SEED)
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# ** model initializers **
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resnet.BatchNorm = FrozenBatchNorm2dRetinaNet
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resnet.Linear = Linear
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@ -465,8 +494,24 @@ def train_retinanet():
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optim = Adam(params, lr=lr)
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# ** dataset **
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config["steps_in_train_epoch"] = steps_in_train_epoch = round_up(get_dataset_count((base_dir_path:=Path(BASEDIR)), False), BS) // BS
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config["steps_in_val_epoch"] = steps_in_val_epoch = (round_up(get_dataset_count(base_dir_path, True), EVAL_BS) // EVAL_BS)
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config["STEPS_IN_TRAIN_EPOCH"] = steps_in_train_epoch = round_up(get_dataset_count((base_dir_path:=Path(BASEDIR)), False), BS) // BS
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config["STEPS_IN_VAL_EPOCH"] = steps_in_val_epoch = (round_up(get_dataset_count(base_dir_path, True), EVAL_BS) // EVAL_BS)
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# log mlperf hparams
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if MLLOGGER:
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if RUNMLPERF:
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MLLOGGER.event(key=mllog_constants.GLOBAL_BATCH_SIZE, value=config["BS"])
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MLLOGGER.event(key=mllog_constants.TRAIN_SAMPLES, value=config["STEPS_IN_TRAIN_EPOCH"])
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MLLOGGER.event(key=mllog_constants.EVAL_SAMPLES, value=config["STEPS_IN_VAL_EPOCH"])
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MLLOGGER.event(key=mllog_constants.EPOCH_COUNT, value=config["EPOCHS"])
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MLLOGGER.event(key=mllog_constants.FIRST_EPOCH_NUM, value=start_epoch)
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MLLOGGER.event(key=mllog_constants.OPT_NAME, value=mllog_constants.ADAM)
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MLLOGGER.event(key=mllog_constants.OPT_BASE_LR, value=config["LR"])
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MLLOGGER.event(key=mllog_constants.OPT_WEIGHT_DECAY, value=0)
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MLLOGGER.event(key=mllog_constants.OPT_LR_WARMUP_EPOCHS, value=0)
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MLLOGGER.event(key=mllog_constants.OPT_LR_WARMUP_FACTOR, value=0)
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MLLOGGER.event(key=mllog_constants.GRADIENT_ACCUMULATION_STEPS, value=1)
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if RUNMLPERF:
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train_dataset = COCO(download_dataset(BASEDIR, "train"))
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@ -477,13 +522,16 @@ def train_retinanet():
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for e in range(start_epoch, EPOCHS):
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# ** training loop **
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if MLLOGGER and RUNMLPERF:
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MLLOGGER.start(key=mllog_constants.EPOCH_START, value=e + 1, metadata={"epoch_num": e + 1})
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BEAM.value = TRAIN_BEAM
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if not RUNMLPERF:
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i, proc = 0, _fake_data_get(BS)
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else:
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train_dataloader = batch_load_retinanet(train_dataset, False, base_dir_path, batch_size=BS, seed=SEED)
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it = iter(tqdm(train_dataloader, total=steps_in_train_epoch, desc=f"epoch {e}", disable=BENCHMARK))
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it = iter(tqdm(train_dataloader, total=steps_in_train_epoch, desc=f"epoch {e + 1}", disable=BENCHMARK))
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i, proc = 0, _data_get(it)
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prev_cookies = []
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@ -545,8 +593,14 @@ def train_retinanet():
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if (TRAIN_BEAM or EVAL_BEAM) and e == start_epoch: break
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return
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if MLLOGGER and RUNMLPERF:
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MLLOGGER.event(key=mllog_constants.EPOCH_STOP, value=e + 1, metadata={"epoch_num": e + 1})
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# ** eval loop **
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if (e + 1) % eval_freq == 0:
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if MLLOGGER and RUNMLPERF:
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MLLOGGER.start(key=mllog_constants.EVAL_START, value=e + 1, metadata={"epoch_num": e + 1})
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BEAM.value = EVAL_BEAM
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if getenv("RESET_STEP", 1): _train_step.reset()
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@ -594,12 +648,15 @@ def train_retinanet():
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proc, next_proc = next_proc, None
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i += 1
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if i == BENCHMARK:
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return
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et = time.time()
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eval_times.append(et - st)
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if i == BENCHMARK:
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# assume INITMLPERF has BENCHMARK set
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if MLLOGGER and INITMLPERF:
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MLLOGGER.event(key=mllog_constants.INIT_STOP)
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return
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if getenv("RESET_STEP", 1): _eval_step.reset()
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total_fw_time = sum(eval_times) / len(eval_times)
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@ -617,8 +674,16 @@ def train_retinanet():
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if WANDB:
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wandb.log({"eval/forward_time": total_fw_time, "eval/metric": val_metric, "epoch": e + 1})
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if MLLOGGER:
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MLLOGGER.event(key=mllog_constants.EVAL_ACCURACY, value=val_metric, metadata={"epoch_num": e + 1}, clear_line=True)
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MLLOGGER.end(key=mllog_constants.EVAL_STOP, value=e + 1, metadata={"epoch_num": e + 1})
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if val_metric >= target_metric:
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print(colored(f"target metric reached: {val_metric:.2f}/{target_metric:.2f}", color="green"))
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if MLLOGGER:
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MLLOGGER.end(key=mllog_constants.RUN_STOP, metadata={"status": mllog_constants.SUCCESS})
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break
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def train_unet3d():
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@ -1,15 +0,0 @@
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#!/bin/bash
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export PYTHONPATH="." AMD=1
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export MODEL="bert"
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export DEFAULT_FLOAT="HALF" GPUS=1 BS=128 EVAL_BS=128
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export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
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export IGNORE_JIT_FIRST_BEAM=1
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# export BEAM_LOG_SURPASS_MAX=1
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# export BASEDIR="/raid/datasets/wiki"
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export RESET_STEP=1
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export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
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python3 examples/mlperf/model_train.py
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@ -0,0 +1,69 @@
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# 1. Problem
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This problem uses BERT for NLP.
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## Requirements
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Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
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```
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git clone https://github.com/tinygrad/tinygrad.git
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python3 -m pip install -e ".[mlperf]"
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```
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Also install gdown (for dataset), numpy, tqdm and tensorflow.
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```
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pip install gdown numpy tqdm tensorflow
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```
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### tinybox_green
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Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
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This is the default on production tinybox green.
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# 2. Directions
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## Steps to download and verify data
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### 1. Download raw data
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```
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BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
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```
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### 2. Preprocess train and validation data
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Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended.
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#### Training:
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```
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BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
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```
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Generating a specific topic (Between 0 and 499)
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```
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BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
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```
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#### Validation:
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```
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BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
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```
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## Running
|
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|
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### tinybox_green
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|
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#### Steps to run benchmark
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```
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examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
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```
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### tinybox_red
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|
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#### Steps to run benchmark
|
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```
|
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examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
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```
|
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### tinybox_8xMI300X
|
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|
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#### Steps to run benchmark
|
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```
|
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examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
|
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```
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|
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@ -4,14 +4,14 @@ This problem uses BERT for NLP.
|
|||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging from master.
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
Also install tqdm and tensorflow.
|
||||
Also install gdown (for dataset), numpy, tqdm and tensorflow.
|
||||
```
|
||||
pip install tqdm tensorflow
|
||||
pip install gdown numpy tqdm tensorflow
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
|
|
@ -52,12 +52,18 @@ BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
|
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|
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#### Steps to run benchmark
|
||||
```
|
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examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
|
||||
### tinybox_red
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
### tinybox_8xMI300X
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
|
||||
```
|
||||
|
|
@ -4,14 +4,14 @@ This problem uses BERT for NLP.
|
|||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging from master.
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
Also install tqdm and tensorflow.
|
||||
Also install gdown (for dataset), numpy, tqdm and tensorflow.
|
||||
```
|
||||
pip install tqdm tensorflow
|
||||
pip install gdown numpy tqdm tensorflow
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
|
|
@ -52,12 +52,18 @@ BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
|
|||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
|
||||
### tinybox_red
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
### tinybox_8xMI300X
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
|
||||
```
|
||||
|
|
@ -7,7 +7,7 @@ export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
|
|||
|
||||
export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
|
||||
|
||||
export BEAM=5 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
|
|
|
|||
|
|
@ -1,50 +0,0 @@
|
|||
# 1. Problem
|
||||
|
||||
This problem uses the ResNet-50 CNN to do image classification.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging from master.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
### tinybox_red
|
||||
Disable cwsr
|
||||
This is the default on production tinybox red.
|
||||
```
|
||||
sudo vi /etc/modprobe.d/amdgpu.conf
|
||||
cat <<EOF > /etc/modprobe.d/amdgpu.conf
|
||||
options amdgpu cwsr_enable=0
|
||||
EOF
|
||||
sudo update-initramfs -u
|
||||
sudo reboot
|
||||
|
||||
# validate
|
||||
sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
|
||||
```
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
```
|
||||
IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
|
||||
```
|
||||
|
||||
## Steps for one time setup
|
||||
|
||||
### tinybox_red
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
|
||||
```
|
||||
|
||||
## Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=10 DEBUG=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
|
||||
|
||||
export EVAL_START_EPOCH=3 EVAL_FREQ=4
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="resnet"
|
||||
export SUBMISSION_PLATFORM="tinybox_green"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="resnet_green_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
|
|
@ -1,50 +0,0 @@
|
|||
# 1. Problem
|
||||
|
||||
This problem uses the ResNet-50 CNN to do image classification.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging from master.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
### tinybox_red
|
||||
Disable cwsr
|
||||
This is the default on production tinybox red.
|
||||
```
|
||||
sudo vi /etc/modprobe.d/amdgpu.conf
|
||||
cat <<EOF > /etc/modprobe.d/amdgpu.conf
|
||||
options amdgpu cwsr_enable=0
|
||||
EOF
|
||||
sudo update-initramfs -u
|
||||
sudo reboot
|
||||
|
||||
# validate
|
||||
sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
|
||||
```
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
```
|
||||
IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
|
||||
```
|
||||
|
||||
## Steps for one time setup
|
||||
|
||||
### tinybox_red
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
|
||||
```
|
||||
|
||||
## Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=10 DEBUG=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export EVAL_START_EPOCH=3 EVAL_FREQ=4
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="resnet"
|
||||
export SUBMISSION_PLATFORM="tinybox_red"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="resnet_red_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
rocm-smi --setprofile compute
|
||||
rocm-smi --setmclk 3
|
||||
rocm-smi --setperflevel high
|
||||
|
||||
# power cap to 350W
|
||||
echo "350000000" | sudo tee /sys/class/drm/card{1..6}/device/hwmon/hwmon*/power1_cap
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
# 1. Problem
|
||||
|
||||
This problem uses RetinaNet for SSD.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
|
||||
Also install the following dependencies:
|
||||
```
|
||||
pip install tqdm numpy pycocotools boto3 pandas torch torchvision
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download data
|
||||
|
||||
Run the following:
|
||||
```
|
||||
BASEDIR=/raid/datasets/openimages python3 extra/datasets/openimages.py
|
||||
```
|
||||
|
||||
## Running
|
||||
|
||||
### tinybox_green
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." NV=1
|
||||
export MODEL="retinanet"
|
||||
export SUBMISSION_PLATFORM="tinybox_green"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
|
||||
export TRAIN_BEAM=2 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="retinanet_green_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
|
|
@ -1,14 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="retinanet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=5 DEBUG=2
|
||||
|
||||
python examples/mlperf/model_train.py
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." AMD=1
|
||||
export MODEL="retinanet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
export RUNMLPERF=1
|
||||
|
||||
python examples/mlperf/model_train.py
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745530925055, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745530925071, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_8xMI300X", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745530925071, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745530925071, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745530925071, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745530925111, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745530925112, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532610164, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532618573, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532618590, "event_type": "POINT_IN_TIME", "key": "seed", "value": 28597, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631107, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 1024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631108, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631108, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631108, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631108, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631108, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631108, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.60466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631108, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.85437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631109, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631109, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631109, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631109, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631109, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631109, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 3900, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631109, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631109, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10240, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532631109, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3993600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745532663595, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745533321994, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149504, "step_num": 146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745533363168, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149504, "step_num": 146, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745533363168, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37238641977310183, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149504, "masked_lm_accuracy": 0.37238641977310183}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745533638843, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299008, "step_num": 292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745533643497, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299008, "step_num": 292, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745533643497, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3876396149396896, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299008, "masked_lm_accuracy": 0.3876396149396896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745533919587, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 448512, "step_num": 438}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745533924272, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 448512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 448512, "step_num": 438, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745533924272, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4356461137533188, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 448512, "masked_lm_accuracy": 0.4356461137533188}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745534200765, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 598016, "step_num": 584}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745534205421, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 598016, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 598016, "step_num": 584, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745534205422, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.523741614818573, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 598016, "masked_lm_accuracy": 0.523741614818573}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745534481862, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 747520, "step_num": 730}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745534486501, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 747520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 747520, "step_num": 730, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745534486501, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6327109396457672, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 747520, "masked_lm_accuracy": 0.6327109396457672}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745534762710, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 897024, "step_num": 876}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745534767349, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 897024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 897024, "step_num": 876, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745534767349, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6953712999820709, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 897024, "masked_lm_accuracy": 0.6953712999820709}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535043273, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1046528, "step_num": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535047907, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1046528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1046528, "step_num": 1022, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535047907, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7062251627445221, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1046528, "masked_lm_accuracy": 0.7062251627445221}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535324088, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1196032, "step_num": 1168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535328759, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1196032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1196032, "step_num": 1168, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535328760, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7097240626811981, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1196032, "masked_lm_accuracy": 0.7097240626811981}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535604822, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1345536, "step_num": 1314}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535609479, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1345536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1345536, "step_num": 1314, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535609479, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7112975955009461, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1345536, "masked_lm_accuracy": 0.7112975955009461}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535885084, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1495040, "step_num": 1460}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535889738, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1495040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1495040, "step_num": 1460, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745535889738, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.711781257390976, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1495040, "masked_lm_accuracy": 0.711781257390976}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745536165354, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1644544, "step_num": 1606}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745536170008, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1644544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1644544, "step_num": 1606, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745536170009, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7138555943965912, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1644544, "masked_lm_accuracy": 0.7138555943965912}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745536445570, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1794048, "step_num": 1752}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745536450183, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1794048, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1794048, "step_num": 1752, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745536450183, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7145828008651733, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1794048, "masked_lm_accuracy": 0.7145828008651733}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745536725721, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1943552, "step_num": 1898}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745536730380, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1943552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1943552, "step_num": 1898, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745536730381, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7148040235042572, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1943552, "masked_lm_accuracy": 0.7148040235042572}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537006570, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2093056, "step_num": 2044}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537011215, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2093056, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2093056, "step_num": 2044, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537011215, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7158548653125762, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2093056, "masked_lm_accuracy": 0.7158548653125762}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537289257, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2242560, "step_num": 2190}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537293941, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2242560, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2242560, "step_num": 2190, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537293941, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7160437941551209, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2242560, "masked_lm_accuracy": 0.7160437941551209}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537580273, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2392064, "step_num": 2336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537584925, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2392064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2392064, "step_num": 2336, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537584926, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7169912159442902, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2392064, "masked_lm_accuracy": 0.7169912159442902}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537860132, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2541568, "step_num": 2482}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537864770, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2541568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2541568, "step_num": 2482, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745537864770, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7176614046096802, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2541568, "masked_lm_accuracy": 0.7176614046096802}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538142023, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2691072, "step_num": 2628}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538146643, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2691072, "step_num": 2628, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538146644, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7182221412658691, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2691072, "masked_lm_accuracy": 0.7182221412658691}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538425213, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2840576, "step_num": 2774}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538429916, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2840576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2840576, "step_num": 2774, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538429916, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7186778724193573, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2840576, "masked_lm_accuracy": 0.7186778724193573}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538705412, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2990080, "step_num": 2920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538710041, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2990080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2990080, "step_num": 2920, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538710041, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190635979175568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2990080, "masked_lm_accuracy": 0.7190635979175568}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538992202, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3139584, "step_num": 3066}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538996846, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3139584, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3139584, "step_num": 3066, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745538996846, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7196638345718384, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3139584, "masked_lm_accuracy": 0.7196638345718384}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539272384, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3289088, "step_num": 3212}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539277037, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3289088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3289088, "step_num": 3212, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539277037, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7201055765151978, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3289088, "masked_lm_accuracy": 0.7201055765151978}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539277038, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3289088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3289088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539277038, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,87 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745539327854, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539327869, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_8xMI300X", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539327870, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539327870, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539327870, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539327912, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745539327912, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540911909, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540920744, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540920760, "event_type": "POINT_IN_TIME", "key": "seed", "value": 15393, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933147, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 1024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933148, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933148, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933148, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933148, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933148, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933148, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.60466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933148, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.85437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933148, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933148, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933149, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933149, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933149, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933149, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 3900, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933149, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933149, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10240, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540933149, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3993600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745540962253, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745541620433, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149504, "step_num": 146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745541661192, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149504, "step_num": 146, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745541661193, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37288502752780917, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149504, "masked_lm_accuracy": 0.37288502752780917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745541940327, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299008, "step_num": 292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745541944936, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299008, "step_num": 292, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745541944936, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3860586792230606, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299008, "masked_lm_accuracy": 0.3860586792230606}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542224659, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 448512, "step_num": 438}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542229287, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 448512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 448512, "step_num": 438, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542229287, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.429127961397171, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 448512, "masked_lm_accuracy": 0.429127961397171}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542510137, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 598016, "step_num": 584}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542514771, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 598016, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 598016, "step_num": 584, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542514771, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.529323160648346, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 598016, "masked_lm_accuracy": 0.529323160648346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542796386, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 747520, "step_num": 730}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542801034, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 747520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 747520, "step_num": 730, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542801034, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6631237685680389, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 747520, "masked_lm_accuracy": 0.6631237685680389}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543082494, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 897024, "step_num": 876}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543087128, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 897024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 897024, "step_num": 876, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543087128, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7024946749210358, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 897024, "masked_lm_accuracy": 0.7024946749210358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543367964, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1046528, "step_num": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543372586, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1046528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1046528, "step_num": 1022, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543372587, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.708392471075058, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1046528, "masked_lm_accuracy": 0.708392471075058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543653532, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1196032, "step_num": 1168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543658174, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1196032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1196032, "step_num": 1168, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543658174, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.711252635717392, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1196032, "masked_lm_accuracy": 0.711252635717392}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543939454, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1345536, "step_num": 1314}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543944107, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1345536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1345536, "step_num": 1314, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543944107, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7130857110023499, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1345536, "masked_lm_accuracy": 0.7130857110023499}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544225356, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1495040, "step_num": 1460}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544230002, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1495040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1495040, "step_num": 1460, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544230002, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7136700868606567, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1495040, "masked_lm_accuracy": 0.7136700868606567}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544512872, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1644544, "step_num": 1606}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544517504, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1644544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1644544, "step_num": 1606, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544517504, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7149688005447388, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1644544, "masked_lm_accuracy": 0.7149688005447388}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544801071, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1794048, "step_num": 1752}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544805699, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1794048, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1794048, "step_num": 1752, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544805700, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.715419614315033, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1794048, "masked_lm_accuracy": 0.715419614315033}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545085900, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1943552, "step_num": 1898}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545090549, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1943552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1943552, "step_num": 1898, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545090549, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7160871505737305, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1943552, "masked_lm_accuracy": 0.7160871505737305}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545375103, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2093056, "step_num": 2044}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545379758, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2093056, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2093056, "step_num": 2044, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545379759, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7170936107635498, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2093056, "masked_lm_accuracy": 0.7170936107635498}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545660444, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2242560, "step_num": 2190}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545665097, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2242560, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2242560, "step_num": 2190, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545665097, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717720341682434, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2242560, "masked_lm_accuracy": 0.717720341682434}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545946029, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2392064, "step_num": 2336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545950685, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2392064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2392064, "step_num": 2336, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545950686, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7183034479618072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2392064, "masked_lm_accuracy": 0.7183034479618072}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546233082, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2541568, "step_num": 2482}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546237727, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2541568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2541568, "step_num": 2482, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546237727, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7182737410068512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2541568, "masked_lm_accuracy": 0.7182737410068512}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546517916, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2691072, "step_num": 2628}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546522570, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2691072, "step_num": 2628, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546522571, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191624820232392, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2691072, "masked_lm_accuracy": 0.7191624820232392}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546806258, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2840576, "step_num": 2774}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546810904, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2840576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2840576, "step_num": 2774, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546810904, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200840294361115, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2840576, "masked_lm_accuracy": 0.7200840294361115}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546810904, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2840576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2840576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546810905, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,90 @@
|
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:::MLLOG {"namespace": "", "time_ms": 1745546859441, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546859457, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_8xMI300X", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546859457, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546859457, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546859457, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546859498, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546859498, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548472183, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548480461, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548480478, "event_type": "POINT_IN_TIME", "key": "seed", "value": 16206, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492724, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 1024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492725, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492725, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492725, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492725, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492725, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492725, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.60466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492725, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.85437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492726, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492726, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492726, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492726, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492726, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492726, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 3900, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492726, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492726, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10240, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548492726, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3993600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548524140, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549187391, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149504, "step_num": 146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549229147, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149504, "step_num": 146, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549229147, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3727019250392914, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149504, "masked_lm_accuracy": 0.3727019250392914}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549513996, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299008, "step_num": 292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549518578, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299008, "step_num": 292, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549518578, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3877880424261093, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299008, "masked_lm_accuracy": 0.3877880424261093}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549804473, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 448512, "step_num": 438}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549809036, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 448512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 448512, "step_num": 438, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549809036, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4411086171865463, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 448512, "masked_lm_accuracy": 0.4411086171865463}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550093860, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 598016, "step_num": 584}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550098426, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 598016, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 598016, "step_num": 584, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550098426, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5526047289371491, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 598016, "masked_lm_accuracy": 0.5526047289371491}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550383034, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 747520, "step_num": 730}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550387588, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 747520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 747520, "step_num": 730, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550387589, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6731186151504517, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 747520, "masked_lm_accuracy": 0.6731186151504517}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550672196, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 897024, "step_num": 876}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550676767, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 897024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 897024, "step_num": 876, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550676767, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7031940758228302, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 897024, "masked_lm_accuracy": 0.7031940758228302}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550961962, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1046528, "step_num": 1022}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745550966515, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1046528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1046528, "step_num": 1022, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745550966515, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7088134288787842, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1046528, "masked_lm_accuracy": 0.7088134288787842}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745551251075, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1196032, "step_num": 1168}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745551255650, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1196032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1196032, "step_num": 1168, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745551255650, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7112709581851959, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1196032, "masked_lm_accuracy": 0.7112709581851959}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745551539957, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1345536, "step_num": 1314}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745551544529, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1345536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1345536, "step_num": 1314, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745551544530, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7128494203090667, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1345536, "masked_lm_accuracy": 0.7128494203090667}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745551828589, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1495040, "step_num": 1460}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745551833167, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1495040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1495040, "step_num": 1460, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745551833167, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7135251462459564, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1495040, "masked_lm_accuracy": 0.7135251462459564}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745552120068, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1644544, "step_num": 1606}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745552124628, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1644544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1644544, "step_num": 1606, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745552124628, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7147472620010376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1644544, "masked_lm_accuracy": 0.7147472620010376}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745552408212, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1794048, "step_num": 1752}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745552412794, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1794048, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1794048, "step_num": 1752, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745552412794, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7153081774711609, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1794048, "masked_lm_accuracy": 0.7153081774711609}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745552697710, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1943552, "step_num": 1898}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745552702247, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1943552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1943552, "step_num": 1898, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745552702247, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7163431167602539, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1943552, "masked_lm_accuracy": 0.7163431167602539}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745552985842, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2093056, "step_num": 2044}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745552990399, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2093056, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2093056, "step_num": 2044, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745552990399, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7166160047054291, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2093056, "masked_lm_accuracy": 0.7166160047054291}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745553277481, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2242560, "step_num": 2190}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745553282034, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2242560, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2242560, "step_num": 2190, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745553282035, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7172852098941803, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2242560, "masked_lm_accuracy": 0.7172852098941803}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745553566684, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2392064, "step_num": 2336}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745553571258, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2392064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2392064, "step_num": 2336, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745553571259, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7169495701789856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2392064, "masked_lm_accuracy": 0.7169495701789856}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745553854981, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2541568, "step_num": 2482}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745553859558, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2541568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2541568, "step_num": 2482, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745553859559, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7182259142398835, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2541568, "masked_lm_accuracy": 0.7182259142398835}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745554143398, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2691072, "step_num": 2628}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745554147970, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2691072, "step_num": 2628, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745554147970, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718964672088623, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2691072, "masked_lm_accuracy": 0.718964672088623}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554435137, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2840576, "step_num": 2774}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554439700, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2840576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2840576, "step_num": 2774, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554439700, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7193355560302734, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2840576, "masked_lm_accuracy": 0.7193355560302734}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554725252, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2990080, "step_num": 2920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554729828, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2990080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2990080, "step_num": 2920, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554729828, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7201014399528504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2990080, "masked_lm_accuracy": 0.7201014399528504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554729829, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2990080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2990080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554729829, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,93 @@
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|||
:::MLLOG {"namespace": "", "time_ms": 1745554777762, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554777778, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_8xMI300X", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554777778, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554777778, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554777778, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554777819, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554777819, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556404094, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556412199, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556412215, "event_type": "POINT_IN_TIME", "key": "seed", "value": 20163, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424820, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 1024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424820, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424821, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424821, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424821, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424821, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424821, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.60466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424821, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.85437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424821, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424821, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424821, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424822, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424822, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424822, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 3900, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424822, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424822, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10240, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556424822, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3993600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556457286, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557115312, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149504, "step_num": 146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557156018, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149504, "step_num": 146, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557156018, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3721550852060318, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149504, "masked_lm_accuracy": 0.3721550852060318}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557435176, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299008, "step_num": 292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557439784, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299008, "step_num": 292, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557439784, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3927359789609909, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299008, "masked_lm_accuracy": 0.3927359789609909}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557719263, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 448512, "step_num": 438}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557723910, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 448512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 448512, "step_num": 438, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557723910, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.44803847670555114, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 448512, "masked_lm_accuracy": 0.44803847670555114}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745558003706, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 598016, "step_num": 584}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745558008350, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 598016, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 598016, "step_num": 584, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745558008350, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5591910660266877, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 598016, "masked_lm_accuracy": 0.5591910660266877}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745558288347, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 747520, "step_num": 730}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745558292987, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 747520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 747520, "step_num": 730, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745558292987, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6610698461532593, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 747520, "masked_lm_accuracy": 0.6610698461532593}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745558573332, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 897024, "step_num": 876}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745558578000, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 897024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 897024, "step_num": 876, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745558578000, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7006066799163818, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 897024, "masked_lm_accuracy": 0.7006066799163818}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745558858774, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1046528, "step_num": 1022}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745558863431, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1046528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1046528, "step_num": 1022, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745558863431, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7073053598403931, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1046528, "masked_lm_accuracy": 0.7073053598403931}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559143329, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1196032, "step_num": 1168}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745559147961, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1196032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1196032, "step_num": 1168, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745559147962, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7103267848491669, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1196032, "masked_lm_accuracy": 0.7103267848491669}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559427612, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1345536, "step_num": 1314}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559432260, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1345536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1345536, "step_num": 1314, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559432260, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7116473376750946, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1345536, "masked_lm_accuracy": 0.7116473376750946}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559711261, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1495040, "step_num": 1460}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559715880, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1495040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1495040, "step_num": 1460, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559715881, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7130024552345275, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1495040, "masked_lm_accuracy": 0.7130024552345275}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559994602, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1644544, "step_num": 1606}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559999235, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1644544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1644544, "step_num": 1606, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559999235, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7140777409076691, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1644544, "masked_lm_accuracy": 0.7140777409076691}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560278151, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1794048, "step_num": 1752}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560282795, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1794048, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1794048, "step_num": 1752, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560282796, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.714939397573471, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1794048, "masked_lm_accuracy": 0.714939397573471}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560561092, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1943552, "step_num": 1898}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560565689, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1943552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1943552, "step_num": 1898, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560565690, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7154468119144439, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1943552, "masked_lm_accuracy": 0.7154468119144439}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560849874, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2093056, "step_num": 2044}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560854474, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2093056, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2093056, "step_num": 2044, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560854474, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7161401331424713, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2093056, "masked_lm_accuracy": 0.7161401331424713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561132673, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2242560, "step_num": 2190}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745561137319, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2242560, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2242560, "step_num": 2190, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561137319, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7163729786872863, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2242560, "masked_lm_accuracy": 0.7163729786872863}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561418781, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2392064, "step_num": 2336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561423392, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2392064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2392064, "step_num": 2336, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561423393, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717225980758667, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2392064, "masked_lm_accuracy": 0.717225980758667}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561701170, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2541568, "step_num": 2482}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561705798, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2541568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2541568, "step_num": 2482, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561705798, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181021451950074, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2541568, "masked_lm_accuracy": 0.7181021451950074}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561988084, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2691072, "step_num": 2628}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561992722, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2691072, "step_num": 2628, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561992722, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191600739955902, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2691072, "masked_lm_accuracy": 0.7191600739955902}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562273977, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2840576, "step_num": 2774}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562278658, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2840576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2840576, "step_num": 2774, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562278659, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.719371110200882, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2840576, "masked_lm_accuracy": 0.719371110200882}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562559865, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2990080, "step_num": 2920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562564486, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2990080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2990080, "step_num": 2920, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562564486, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718993628025055, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2990080, "masked_lm_accuracy": 0.718993628025055}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562845123, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3139584, "step_num": 3066}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562849755, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3139584, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3139584, "step_num": 3066, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562849756, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200249254703521, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3139584, "masked_lm_accuracy": 0.7200249254703521}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562849756, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3139584, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3139584}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562849756, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,96 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745562899619, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562899635, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_8xMI300X", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562899635, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562899635, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562899635, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562899676, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562899676, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564587425, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564596874, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564596890, "event_type": "POINT_IN_TIME", "key": "seed", "value": 13352, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609291, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 1024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609292, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609292, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609292, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609292, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609292, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609292, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.60466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609292, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.85437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609293, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609293, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609293, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609293, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745564609293, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745564609293, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 3900, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609293, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609293, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10240, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564609293, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3993600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564642816, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745565297838, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149504, "step_num": 146}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745565339344, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149504, "step_num": 146, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745565339345, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3725235253572464, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149504, "masked_lm_accuracy": 0.3725235253572464}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745565617369, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299008, "step_num": 292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745565622024, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299008, "step_num": 292, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745565622025, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38939482867717745, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299008, "masked_lm_accuracy": 0.38939482867717745}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745565900583, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 448512, "step_num": 438}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745565905232, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 448512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 448512, "step_num": 438, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745565905233, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.43388367593288424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 448512, "masked_lm_accuracy": 0.43388367593288424}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745566183301, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 598016, "step_num": 584}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745566187916, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 598016, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 598016, "step_num": 584, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745566187917, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5344274997711181, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 598016, "masked_lm_accuracy": 0.5344274997711181}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745566466251, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 747520, "step_num": 730}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745566470898, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 747520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 747520, "step_num": 730, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745566470898, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6529988288879395, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 747520, "masked_lm_accuracy": 0.6529988288879395}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745566749556, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 897024, "step_num": 876}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745566754223, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 897024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 897024, "step_num": 876, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745566754223, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6987890124320983, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 897024, "masked_lm_accuracy": 0.6987890124320983}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567033420, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1046528, "step_num": 1022}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567038073, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1046528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1046528, "step_num": 1022, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567038073, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7056617558002471, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1046528, "masked_lm_accuracy": 0.7056617558002471}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567316228, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1196032, "step_num": 1168}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567320871, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1196032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1196032, "step_num": 1168, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567320871, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7096020996570587, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1196032, "masked_lm_accuracy": 0.7096020996570587}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567599127, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1345536, "step_num": 1314}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567603785, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1345536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1345536, "step_num": 1314, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567603785, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7113405406475067, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1345536, "masked_lm_accuracy": 0.7113405406475067}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745567881894, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1495040, "step_num": 1460}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567886538, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1495040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1495040, "step_num": 1460, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745567886539, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7122747898101807, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1495040, "masked_lm_accuracy": 0.7122747898101807}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745568164600, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1644544, "step_num": 1606}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568169238, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1644544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1644544, "step_num": 1606, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568169239, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7133678615093231, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1644544, "masked_lm_accuracy": 0.7133678615093231}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568447131, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1794048, "step_num": 1752}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568451771, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1794048, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1794048, "step_num": 1752, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568451771, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7142562866210938, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1794048, "masked_lm_accuracy": 0.7142562866210938}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568731625, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1943552, "step_num": 1898}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568736289, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1943552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1943552, "step_num": 1898, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568736290, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7150763928890228, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1943552, "masked_lm_accuracy": 0.7150763928890228}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569015501, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2093056, "step_num": 2044}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569020163, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2093056, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2093056, "step_num": 2044, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569020163, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7155498623847961, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2093056, "masked_lm_accuracy": 0.7155498623847961}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569298438, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2242560, "step_num": 2190}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569303066, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2242560, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2242560, "step_num": 2190, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569303066, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7160784900188446, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2242560, "masked_lm_accuracy": 0.7160784900188446}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569581278, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2392064, "step_num": 2336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569585933, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2392064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2392064, "step_num": 2336, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569585933, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7170640766620636, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2392064, "masked_lm_accuracy": 0.7170640766620636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569866592, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2541568, "step_num": 2482}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569871241, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2541568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2541568, "step_num": 2482, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569871241, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7173652410507202, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2541568, "masked_lm_accuracy": 0.7173652410507202}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570152468, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2691072, "step_num": 2628}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570157114, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2691072, "step_num": 2628, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570157115, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718361359834671, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2691072, "masked_lm_accuracy": 0.718361359834671}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570435938, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2840576, "step_num": 2774}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570440644, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2840576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2840576, "step_num": 2774, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570440644, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7186611413955688, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2840576, "masked_lm_accuracy": 0.7186611413955688}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570726442, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2990080, "step_num": 2920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570731082, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2990080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2990080, "step_num": 2920, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570731082, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191152453422547, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2990080, "masked_lm_accuracy": 0.7191152453422547}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571012781, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3139584, "step_num": 3066}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571017427, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3139584, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3139584, "step_num": 3066, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571017427, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195845186710358, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3139584, "masked_lm_accuracy": 0.7195845186710358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571300015, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3289088, "step_num": 3212}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571304681, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3289088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3289088, "step_num": 3212, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571304682, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200073778629303, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3289088, "masked_lm_accuracy": 0.7200073778629303}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571304682, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3289088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3289088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571304682, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,96 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745571354572, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571354587, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_8xMI300X", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571354587, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571354587, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571354587, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571354629, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571354629, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573082356, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573090559, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573090575, "event_type": "POINT_IN_TIME", "key": "seed", "value": 29862, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103073, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 1024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103073, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103073, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103074, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103074, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103074, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103074, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.60466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103074, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.85437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103074, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103074, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103074, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103074, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103074, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103075, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 3900, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103075, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103075, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10240, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573103075, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3993600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573134049, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573811088, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149504, "step_num": 146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573852243, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149504, "step_num": 146, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745573852243, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3729754567146301, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149504, "masked_lm_accuracy": 0.3729754567146301}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574135466, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299008, "step_num": 292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574140258, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299008, "step_num": 292, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745574140258, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38697501420974734, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299008, "masked_lm_accuracy": 0.38697501420974734}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574423983, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 448512, "step_num": 438}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574428791, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 448512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 448512, "step_num": 438, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574428791, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.42467189133167266, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 448512, "masked_lm_accuracy": 0.42467189133167266}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574712426, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 598016, "step_num": 584}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574717211, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 598016, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 598016, "step_num": 584, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574717212, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5411350011825562, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 598016, "masked_lm_accuracy": 0.5411350011825562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575000437, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 747520, "step_num": 730}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575005233, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 747520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 747520, "step_num": 730, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575005233, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6546417593955993, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 747520, "masked_lm_accuracy": 0.6546417593955993}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575287460, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 897024, "step_num": 876}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575292227, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 897024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 897024, "step_num": 876, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575292227, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7013923466205597, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 897024, "masked_lm_accuracy": 0.7013923466205597}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575574252, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1046528, "step_num": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575579038, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1046528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1046528, "step_num": 1022, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575579038, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7082416176795959, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1046528, "masked_lm_accuracy": 0.7082416176795959}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575860880, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1196032, "step_num": 1168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575865649, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1196032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1196032, "step_num": 1168, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575865649, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7109045207500457, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1196032, "masked_lm_accuracy": 0.7109045207500457}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745576147355, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1345536, "step_num": 1314}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745576152107, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1345536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1345536, "step_num": 1314, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745576152108, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7121940672397613, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1345536, "masked_lm_accuracy": 0.7121940672397613}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745576433426, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1495040, "step_num": 1460}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745576438176, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1495040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1495040, "step_num": 1460, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745576438176, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.713076388835907, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1495040, "masked_lm_accuracy": 0.713076388835907}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745576719437, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1644544, "step_num": 1606}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745576724186, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1644544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1644544, "step_num": 1606, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745576724186, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7147435486316681, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1644544, "masked_lm_accuracy": 0.7147435486316681}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577005513, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1794048, "step_num": 1752}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577010268, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1794048, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1794048, "step_num": 1752, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577010268, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7152598202228546, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1794048, "masked_lm_accuracy": 0.7152598202228546}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577294417, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1943552, "step_num": 1898}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577299176, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1943552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1943552, "step_num": 1898, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577299177, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7158841907978057, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1943552, "masked_lm_accuracy": 0.7158841907978057}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577583196, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2093056, "step_num": 2044}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577587938, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2093056, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2093056, "step_num": 2044, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577587938, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7162841558456421, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2093056, "masked_lm_accuracy": 0.7162841558456421}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577872125, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2242560, "step_num": 2190}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577876872, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2242560, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2242560, "step_num": 2190, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745577876872, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7171514630317688, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2242560, "masked_lm_accuracy": 0.7171514630317688}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578157392, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2392064, "step_num": 2336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578162167, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2392064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2392064, "step_num": 2336, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578162167, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717557144165039, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2392064, "masked_lm_accuracy": 0.717557144165039}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578442695, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2541568, "step_num": 2482}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578447477, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2541568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2541568, "step_num": 2482, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578447478, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181805670261383, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2541568, "masked_lm_accuracy": 0.7181805670261383}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578732198, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2691072, "step_num": 2628}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578736960, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2691072, "step_num": 2628, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578736960, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7193208992481231, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2691072, "masked_lm_accuracy": 0.7193208992481231}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579017075, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2840576, "step_num": 2774}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579021830, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2840576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2840576, "step_num": 2774, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579021831, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190930187702179, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2840576, "masked_lm_accuracy": 0.7190930187702179}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579308467, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2990080, "step_num": 2920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579313199, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2990080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2990080, "step_num": 2920, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579313199, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7193685650825501, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2990080, "masked_lm_accuracy": 0.7193685650825501}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579595974, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3139584, "step_num": 3066}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579600728, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3139584, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3139584, "step_num": 3066, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579600728, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198175132274628, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3139584, "masked_lm_accuracy": 0.7198175132274628}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579881098, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3289088, "step_num": 3212}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579885849, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3289088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3289088, "step_num": 3212, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579885849, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.720059609413147, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3289088, "masked_lm_accuracy": 0.720059609413147}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579885849, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3289088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3289088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579885849, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745579935971, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579935986, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_8xMI300X", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579935987, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579935987, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579935987, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579936029, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579936029, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581719259, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581728446, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581728462, "event_type": "POINT_IN_TIME", "key": "seed", "value": 7754, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740992, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 1024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740993, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740993, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740993, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740993, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740993, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740993, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.60466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740994, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.85437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740994, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740994, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740994, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740994, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740994, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740994, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 3900, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740994, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740994, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10240, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581740994, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3993600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745581773454, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745582423629, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149504, "step_num": 146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745582464633, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149504, "step_num": 146, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745582464633, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37272038757801057, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149504, "masked_lm_accuracy": 0.37272038757801057}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745582740508, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299008, "step_num": 292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745582745067, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299008, "step_num": 292, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745582745068, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38925057649612427, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299008, "masked_lm_accuracy": 0.38925057649612427}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583021215, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 448512, "step_num": 438}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583025776, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 448512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 448512, "step_num": 438, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583025776, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.43427990674972533, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 448512, "masked_lm_accuracy": 0.43427990674972533}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583301981, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 598016, "step_num": 584}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583306560, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 598016, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 598016, "step_num": 584, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583306561, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5278923571109772, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 598016, "masked_lm_accuracy": 0.5278923571109772}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583582767, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 747520, "step_num": 730}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583587345, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 747520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 747520, "step_num": 730, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583587345, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6390926957130432, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 747520, "masked_lm_accuracy": 0.6390926957130432}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583863550, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 897024, "step_num": 876}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583868135, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 897024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 897024, "step_num": 876, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583868136, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6956972718238831, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 897024, "masked_lm_accuracy": 0.6956972718238831}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584144322, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1046528, "step_num": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584148929, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1046528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1046528, "step_num": 1022, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584148929, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.705748564004898, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1046528, "masked_lm_accuracy": 0.705748564004898}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584425116, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1196032, "step_num": 1168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584429704, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1196032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1196032, "step_num": 1168, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584429704, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7098638355731964, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1196032, "masked_lm_accuracy": 0.7098638355731964}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584705931, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1345536, "step_num": 1314}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584710512, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1345536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1345536, "step_num": 1314, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584710512, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7110268771648407, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1345536, "masked_lm_accuracy": 0.7110268771648407}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584986656, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1495040, "step_num": 1460}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584991214, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1495040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1495040, "step_num": 1460, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584991214, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.712305212020874, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1495040, "masked_lm_accuracy": 0.712305212020874}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585267330, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1644544, "step_num": 1606}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585271920, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1644544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1644544, "step_num": 1606, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585271921, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7135731339454651, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1644544, "masked_lm_accuracy": 0.7135731339454651}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585551669, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1794048, "step_num": 1752}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585556247, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1794048, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1794048, "step_num": 1752, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585556248, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7142563343048096, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1794048, "masked_lm_accuracy": 0.7142563343048096}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585833849, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1943552, "step_num": 1898}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585838410, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1943552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1943552, "step_num": 1898, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585838410, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7152255415916443, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1943552, "masked_lm_accuracy": 0.7152255415916443}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586114185, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2093056, "step_num": 2044}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586118755, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2093056, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2093056, "step_num": 2044, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586118755, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7159430027008057, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2093056, "masked_lm_accuracy": 0.7159430027008057}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586394477, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2242560, "step_num": 2190}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586399048, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2242560, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2242560, "step_num": 2190, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586399048, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7162509083747863, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2242560, "masked_lm_accuracy": 0.7162509083747863}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586674813, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2392064, "step_num": 2336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586679413, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2392064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2392064, "step_num": 2336, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586679414, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7167587816715241, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2392064, "masked_lm_accuracy": 0.7167587816715241}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586958455, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2541568, "step_num": 2482}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586963029, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2541568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2541568, "step_num": 2482, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586963029, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7172225117683411, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2541568, "masked_lm_accuracy": 0.7172225117683411}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587244889, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2691072, "step_num": 2628}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587249485, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2691072, "step_num": 2628, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587249485, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7183384358882904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2691072, "masked_lm_accuracy": 0.7183384358882904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587524929, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2840576, "step_num": 2774}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587529566, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2840576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2840576, "step_num": 2774, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745587529566, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7180015325546265, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2840576, "masked_lm_accuracy": 0.7180015325546265}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587808591, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2990080, "step_num": 2920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587813157, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2990080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2990080, "step_num": 2920, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745587813158, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7184844195842743, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2990080, "masked_lm_accuracy": 0.7184844195842743}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588093001, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3139584, "step_num": 3066}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588097586, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3139584, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3139584, "step_num": 3066, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745588097587, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718977439403534, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3139584, "masked_lm_accuracy": 0.718977439403534}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588375341, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3289088, "step_num": 3212}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588379922, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3289088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3289088, "step_num": 3212, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745588379922, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192779302597045, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3289088, "masked_lm_accuracy": 0.7192779302597045}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588655781, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3438592, "step_num": 3358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588660364, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3438592, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3438592, "step_num": 3358, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745588660364, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.719474196434021, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3438592, "masked_lm_accuracy": 0.719474196434021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588936187, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3588096, "step_num": 3504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588940778, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3588096, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3588096, "step_num": 3504, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588940778, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198640763759613, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3588096, "masked_lm_accuracy": 0.7198640763759613}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589216640, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3737600, "step_num": 3650}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589221228, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3737600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3737600, "step_num": 3650, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589221228, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7199562847614288, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3737600, "masked_lm_accuracy": 0.7199562847614288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589497055, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3887104, "step_num": 3796}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589501645, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3887104, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3887104, "step_num": 3796, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589501646, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7206668496131897, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3887104, "masked_lm_accuracy": 0.7206668496131897}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589501696, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3887104, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3887104}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589501696, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
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|
|
@ -0,0 +1,105 @@
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|||
:::MLLOG {"namespace": "", "time_ms": 1745589550561, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589550577, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_8xMI300X", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589550577, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589550577, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589550577, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589550621, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745589550622, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745591179400, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591187432, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745591187448, "event_type": "POINT_IN_TIME", "key": "seed", "value": 25385, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199679, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 1024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199680, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199680, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199680, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199680, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199680, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199680, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.60466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199681, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.85437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199681, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199681, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199681, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199681, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199681, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199681, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 3900, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199681, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199681, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10240, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591199681, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3993600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591230921, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591901120, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149504, "step_num": 146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591941186, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149504, "step_num": 146, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745591941187, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37098502218723295, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149504, "masked_lm_accuracy": 0.37098502218723295}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745592221446, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299008, "step_num": 292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745592226014, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299008, "step_num": 292, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745592226015, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38992435932159425, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299008, "masked_lm_accuracy": 0.38992435932159425}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745592506825, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 448512, "step_num": 438}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745592511386, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 448512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 448512, "step_num": 438, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745592511386, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4360688954591751, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 448512, "masked_lm_accuracy": 0.4360688954591751}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745592792597, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 598016, "step_num": 584}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745592797166, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 598016, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 598016, "step_num": 584, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745592797167, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5389649093151092, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 598016, "masked_lm_accuracy": 0.5389649093151092}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745593078507, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 747520, "step_num": 730}}
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:::MLLOG {"namespace": "", "time_ms": 1745593083063, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 747520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 747520, "step_num": 730, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745593083064, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6623023927211762, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 747520, "masked_lm_accuracy": 0.6623023927211762}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745593364383, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 897024, "step_num": 876}}
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:::MLLOG {"namespace": "", "time_ms": 1745593368960, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 897024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 897024, "step_num": 876, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745593368960, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6989677131175995, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 897024, "masked_lm_accuracy": 0.6989677131175995}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745593650221, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1046528, "step_num": 1022}}
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:::MLLOG {"namespace": "", "time_ms": 1745593654771, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1046528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1046528, "step_num": 1022, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745593654771, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7055331587791442, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1046528, "masked_lm_accuracy": 0.7055331587791442}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745593935981, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1196032, "step_num": 1168}}
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:::MLLOG {"namespace": "", "time_ms": 1745593940552, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1196032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1196032, "step_num": 1168, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745593940552, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7088817238807679, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1196032, "masked_lm_accuracy": 0.7088817238807679}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745594221979, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1345536, "step_num": 1314}}
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:::MLLOG {"namespace": "", "time_ms": 1745594226554, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1345536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1345536, "step_num": 1314, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745594226554, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7105159401893616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1345536, "masked_lm_accuracy": 0.7105159401893616}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745594507867, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1495040, "step_num": 1460}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745594512457, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1495040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1495040, "step_num": 1460, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745594512457, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7124811410903931, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1495040, "masked_lm_accuracy": 0.7124811410903931}}
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:::MLLOG {"namespace": "", "time_ms": 1745594793979, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1644544, "step_num": 1606}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745594798544, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1644544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1644544, "step_num": 1606, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745594798545, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7134620428085328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1644544, "masked_lm_accuracy": 0.7134620428085328}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745595079851, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1794048, "step_num": 1752}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745595084445, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1794048, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1794048, "step_num": 1752, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745595084445, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7141174793243408, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1794048, "masked_lm_accuracy": 0.7141174793243408}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745595368793, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1943552, "step_num": 1898}}
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:::MLLOG {"namespace": "", "time_ms": 1745595373357, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1943552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1943552, "step_num": 1898, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745595373357, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7149844408035279, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1943552, "masked_lm_accuracy": 0.7149844408035279}}
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:::MLLOG {"namespace": "", "time_ms": 1745595656391, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2093056, "step_num": 2044}}
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:::MLLOG {"namespace": "", "time_ms": 1745595660943, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2093056, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2093056, "step_num": 2044, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745595660944, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7157506585121155, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2093056, "masked_lm_accuracy": 0.7157506585121155}}
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:::MLLOG {"namespace": "", "time_ms": 1745595946346, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2242560, "step_num": 2190}}
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:::MLLOG {"namespace": "", "time_ms": 1745595950902, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2242560, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2242560, "step_num": 2190, "samples_count": 10240}}
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:::MLLOG {"namespace": "", "time_ms": 1745595950902, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7160208821296692, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2242560, "masked_lm_accuracy": 0.7160208821296692}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596234396, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2392064, "step_num": 2336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596238968, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2392064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2392064, "step_num": 2336, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596238968, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7169745147228241, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2392064, "masked_lm_accuracy": 0.7169745147228241}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596519214, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2541568, "step_num": 2482}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596523783, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2541568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2541568, "step_num": 2482, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596523784, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7170299768447876, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2541568, "masked_lm_accuracy": 0.7170299768447876}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596804022, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2691072, "step_num": 2628}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596808588, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2691072, "step_num": 2628, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596808589, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7186152815818787, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2691072, "masked_lm_accuracy": 0.7186152815818787}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597088735, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2840576, "step_num": 2774}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597093297, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2840576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2840576, "step_num": 2774, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597093297, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7184043228626251, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2840576, "masked_lm_accuracy": 0.7184043228626251}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597376858, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2990080, "step_num": 2920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597381430, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2990080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2990080, "step_num": 2920, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597381430, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7186801016330719, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2990080, "masked_lm_accuracy": 0.7186801016330719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597661697, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3139584, "step_num": 3066}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597666257, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3139584, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3139584, "step_num": 3066, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597666257, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191139340400696, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3139584, "masked_lm_accuracy": 0.7191139340400696}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597949669, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3289088, "step_num": 3212}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597954226, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3289088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3289088, "step_num": 3212, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597954226, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192700445652008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3289088, "masked_lm_accuracy": 0.7192700445652008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598236392, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3438592, "step_num": 3358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598240947, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3438592, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3438592, "step_num": 3358, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598240948, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7196909546852112, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3438592, "masked_lm_accuracy": 0.7196909546852112}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598521853, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3588096, "step_num": 3504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598526430, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3588096, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3588096, "step_num": 3504, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598526431, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7199203789234161, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3588096, "masked_lm_accuracy": 0.7199203789234161}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598807367, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3737600, "step_num": 3650}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598811951, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3737600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3737600, "step_num": 3650, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598811951, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200918376445771, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3737600, "masked_lm_accuracy": 0.7200918376445771}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598811952, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3737600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3737600}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598811952, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,102 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745598860595, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598860610, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_8xMI300X", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598860610, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598860610, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598860610, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598860653, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598860653, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600478970, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600487253, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600487271, "event_type": "POINT_IN_TIME", "key": "seed", "value": 32629, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500186, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 1024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500186, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500186, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500186, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.60466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.85437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 3900, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500187, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500188, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10240, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600500188, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3993600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600532785, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601205060, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149504, "step_num": 146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601247392, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149504, "step_num": 146, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601247392, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3731185048818588, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149504, "masked_lm_accuracy": 0.3731185048818588}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601525682, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299008, "step_num": 292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601530299, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299008, "step_num": 292, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745601530300, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.39272683262825014, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299008, "masked_lm_accuracy": 0.39272683262825014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601809277, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 448512, "step_num": 438}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601813902, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 448512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 448512, "step_num": 438, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745601813902, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.43582180738449094, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 448512, "masked_lm_accuracy": 0.43582180738449094}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745602092985, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 598016, "step_num": 584}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745602097585, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 598016, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 598016, "step_num": 584, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745602097585, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5279603898525238, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 598016, "masked_lm_accuracy": 0.5279603898525238}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602376585, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 747520, "step_num": 730}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602381222, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 747520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 747520, "step_num": 730, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745602381222, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6447000563144684, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 747520, "masked_lm_accuracy": 0.6447000563144684}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602660370, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 897024, "step_num": 876}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602664997, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 897024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 897024, "step_num": 876, "samples_count": 10240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745602664997, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6944801509380341, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 897024, "masked_lm_accuracy": 0.6944801509380341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602944009, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1046528, "step_num": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602948636, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1046528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1046528, "step_num": 1022, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602948636, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7051402449607849, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1046528, "masked_lm_accuracy": 0.7051402449607849}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603227727, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1196032, "step_num": 1168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603232331, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1196032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1196032, "step_num": 1168, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603232331, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7089028596878052, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1196032, "masked_lm_accuracy": 0.7089028596878052}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603511699, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1345536, "step_num": 1314}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603516332, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1345536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1345536, "step_num": 1314, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603516332, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7109102308750153, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1345536, "masked_lm_accuracy": 0.7109102308750153}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603795809, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1495040, "step_num": 1460}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603800431, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1495040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1495040, "step_num": 1460, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603800432, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7122457921504974, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1495040, "masked_lm_accuracy": 0.7122457921504974}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604080028, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1644544, "step_num": 1606}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604084645, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1644544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1644544, "step_num": 1606, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604084645, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7131429493427277, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1644544, "masked_lm_accuracy": 0.7131429493427277}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604364612, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1794048, "step_num": 1752}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604369261, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1794048, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1794048, "step_num": 1752, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604369261, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7144218623638153, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1794048, "masked_lm_accuracy": 0.7144218623638153}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604649086, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1943552, "step_num": 1898}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604653711, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1943552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1943552, "step_num": 1898, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604653711, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7148768424987793, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1943552, "masked_lm_accuracy": 0.7148768424987793}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604935309, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2093056, "step_num": 2044}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604939938, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2093056, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2093056, "step_num": 2044, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604939938, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7154791951179504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2093056, "masked_lm_accuracy": 0.7154791951179504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605219927, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2242560, "step_num": 2190}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605224572, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2242560, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2242560, "step_num": 2190, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605224572, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7161833882331848, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2242560, "masked_lm_accuracy": 0.7161833882331848}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605510499, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2392064, "step_num": 2336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605515142, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2392064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2392064, "step_num": 2336, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605515142, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7165493428707123, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2392064, "masked_lm_accuracy": 0.7165493428707123}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605794599, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2541568, "step_num": 2482}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605799240, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2541568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2541568, "step_num": 2482, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605799241, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7170698583126068, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2541568, "masked_lm_accuracy": 0.7170698583126068}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606082218, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2691072, "step_num": 2628}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606086859, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2691072, "step_num": 2628, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606086860, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7182413637638092, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2691072, "masked_lm_accuracy": 0.7182413637638092}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606370703, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2840576, "step_num": 2774}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606375349, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2840576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2840576, "step_num": 2774, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606375350, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7183951079845429, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2840576, "masked_lm_accuracy": 0.7183951079845429}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606655495, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2990080, "step_num": 2920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606660147, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2990080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2990080, "step_num": 2920, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606660147, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7185700833797455, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2990080, "masked_lm_accuracy": 0.7185700833797455}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606941853, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3139584, "step_num": 3066}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606946474, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3139584, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3139584, "step_num": 3066, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606946474, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.719360601902008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3139584, "masked_lm_accuracy": 0.719360601902008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607229955, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3289088, "step_num": 3212}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607234597, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3289088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3289088, "step_num": 3212, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607234597, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198850989341736, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3289088, "masked_lm_accuracy": 0.7198850989341736}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607515289, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3438592, "step_num": 3358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607519928, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3438592, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3438592, "step_num": 3358, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607519928, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198968529701233, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3438592, "masked_lm_accuracy": 0.7198968529701233}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607800552, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3588096, "step_num": 3504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607805193, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3588096, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3588096, "step_num": 3504, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607805194, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7202662408351899, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3588096, "masked_lm_accuracy": 0.7202662408351899}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607805194, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3588096, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3588096}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607805194, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,84 @@
|
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:::MLLOG {"namespace": "", "time_ms": 1745607853432, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607853448, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_8xMI300X", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607853448, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607853448, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607853448, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607853489, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607853490, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609448260, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609456393, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609456411, "event_type": "POINT_IN_TIME", "key": "seed", "value": 24956, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468886, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 1024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468886, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468886, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468886, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468887, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468887, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468887, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.60466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468887, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.85437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468887, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468887, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468887, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468887, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468887, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468887, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 3900, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468888, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468888, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10240, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609468888, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3993600, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609497729, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610146211, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149504, "step_num": 146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610186645, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149504, "step_num": 146, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610186646, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37167071998119355, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149504, "masked_lm_accuracy": 0.37167071998119355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610462149, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299008, "step_num": 292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610466711, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299008, "step_num": 292, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610466711, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3860401749610901, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299008, "masked_lm_accuracy": 0.3860401749610901}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610742915, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 448512, "step_num": 438}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610747488, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 448512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 448512, "step_num": 438, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610747488, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5218028664588928, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 448512, "masked_lm_accuracy": 0.5218028664588928}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611023698, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 598016, "step_num": 584}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611028274, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 598016, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 598016, "step_num": 584, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611028275, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.673934280872345, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 598016, "masked_lm_accuracy": 0.673934280872345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611304381, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 747520, "step_num": 730}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611308983, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 747520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 747520, "step_num": 730, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611308983, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7041513025760651, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 747520, "masked_lm_accuracy": 0.7041513025760651}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611585202, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 897024, "step_num": 876}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611589795, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 897024, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 897024, "step_num": 876, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611589795, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7099382877349854, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 897024, "masked_lm_accuracy": 0.7099382877349854}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611866035, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1046528, "step_num": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611870610, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1046528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1046528, "step_num": 1022, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611870610, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7122330486774444, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1046528, "masked_lm_accuracy": 0.7122330486774444}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612146933, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1196032, "step_num": 1168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612151528, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1196032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1196032, "step_num": 1168, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612151529, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7136648654937744, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1196032, "masked_lm_accuracy": 0.7136648654937744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612428104, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1345536, "step_num": 1314}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612432695, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1345536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1345536, "step_num": 1314, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612432695, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7144280135631561, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1345536, "masked_lm_accuracy": 0.7144280135631561}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612709067, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1495040, "step_num": 1460}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612713674, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1495040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1495040, "step_num": 1460, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612713675, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7147453427314758, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1495040, "masked_lm_accuracy": 0.7147453427314758}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612989926, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1644544, "step_num": 1606}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612994505, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1644544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1644544, "step_num": 1606, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612994505, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7162890911102295, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1644544, "masked_lm_accuracy": 0.7162890911102295}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613274460, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1794048, "step_num": 1752}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613279032, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1794048, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1794048, "step_num": 1752, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613279032, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7166337609291077, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1794048, "masked_lm_accuracy": 0.7166337609291077}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613554938, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1943552, "step_num": 1898}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613559537, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1943552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1943552, "step_num": 1898, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613559537, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7172951698303223, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1943552, "masked_lm_accuracy": 0.7172951698303223}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613842482, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2093056, "step_num": 2044}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613847063, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2093056, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2093056, "step_num": 2044, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613847063, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718510490655899, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2093056, "masked_lm_accuracy": 0.718510490655899}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614126049, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2242560, "step_num": 2190}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614130639, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2242560, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2242560, "step_num": 2190, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614130639, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7184054613113403, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2242560, "masked_lm_accuracy": 0.7184054613113403}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614406167, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2392064, "step_num": 2336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614410769, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2392064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2392064, "step_num": 2336, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614410769, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189517140388488, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2392064, "masked_lm_accuracy": 0.7189517140388488}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614686010, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2541568, "step_num": 2482}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614690621, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2541568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2541568, "step_num": 2482, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614690622, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191794335842132, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2541568, "masked_lm_accuracy": 0.7191794335842132}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614968159, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2691072, "step_num": 2628}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614972735, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2691072, "step_num": 2628, "samples_count": 10240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614972736, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7202518999576568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2691072, "masked_lm_accuracy": 0.7202518999576568}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614972736, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2691072, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2691072}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614972736, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745542295813, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542295826, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542295827, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542295827, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542295827, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542296498, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745542296499, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543895182, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543905464, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543905478, "event_type": "POINT_IN_TIME", "key": "seed", "value": 13785, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921082, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921082, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921082, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921083, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921083, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921083, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921083, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921083, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921083, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921083, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921084, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921084, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921084, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921084, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921084, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921084, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543921084, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745543978728, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544850167, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544889032, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745544889032, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37353726426760353, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.37353726426760353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545487519, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545498684, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745545498684, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.41237829072134835, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.41237829072134835}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546090548, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546100134, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546100135, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5034303157102494, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.5034303157102494}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546693196, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546702828, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745546702828, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6373415260087876, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6373415260087876}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745547295519, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745547305142, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745547305142, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6955064086686997, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.6955064086686997}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745547896140, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745547905833, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745547905833, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7047913125583104, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7047913125583104}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548498633, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548508143, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745548508143, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7078574929918562, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7078574929918562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549101227, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549110866, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549110866, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7101279389290582, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7101279389290582}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549701586, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549711083, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745549711084, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7115791564895994, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7115791564895994}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550303748, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550313288, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550313288, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.713118288630531, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.713118288630531}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550906395, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550916128, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745550916128, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7131774215471177, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7131774215471177}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745551506889, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745551516739, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745551516739, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7145353862217494, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7145353862217494}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745552113873, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745552123576, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745552123576, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7154489999725705, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7154489999725705}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745552716142, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745552725770, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745552725771, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7163924120721363, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7163924120721363}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745553324307, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745553333814, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745553333815, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716705474399385, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.716705474399385}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745553926317, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745553936094, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745553936094, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7176795959472656, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7176795959472656}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554531890, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554542845, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745554542846, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7178676713080633, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7178676713080633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745555133528, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745555143101, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745555143101, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7184804496311006, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7184804496311006}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745555747973, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745555757481, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745555757481, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718643741948264, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.718643741948264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556357940, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556367552, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556367552, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190265468188695, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7190265468188695}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556962657, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3148992, "step_num": 32802}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556972166, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3148992, "step_num": 32802, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745556972166, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7194553653399149, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3148992, "masked_lm_accuracy": 0.7194553653399149}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557564941, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3298944, "step_num": 34364}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557574552, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3298944, "step_num": 34364, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557574552, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.720384947458903, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3298944, "masked_lm_accuracy": 0.720384947458903}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557574552, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3298944}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557574552, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,96 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745557594256, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557594270, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557594270, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557594270, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557594270, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557594941, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745557594941, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559214897, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559225413, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559225427, "event_type": "POINT_IN_TIME", "key": "seed", "value": 32312, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240479, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240480, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240480, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240480, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240480, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240480, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240480, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240480, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240481, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240481, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240481, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240481, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240481, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240481, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240481, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240482, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559240482, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745559294576, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560166054, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560205220, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560205220, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37391537456285384, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.37391537456285384}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560805893, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560815463, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745560815463, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4109735344137464, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4109735344137464}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561410351, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561419975, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745561419975, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.49219877805028645, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.49219877805028645}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562014334, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562023781, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562023781, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6128720873878115, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6128720873878115}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562618009, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562627536, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745562627537, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.697400647117978, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.697400647117978}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745563220282, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745563229809, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745563229810, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.705579203651065, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.705579203651065}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745563823608, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745563833062, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745563833062, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7082960753213792, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7082960753213792}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564427147, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745564436605, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745564436605, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7107288156236921, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7107288156236921}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745565028811, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745565038436, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745565038436, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7119181950887045, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7119181950887045}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745565632265, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745565641826, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745565641827, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7127573898860387, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7127573898860387}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745566234178, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745566245324, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745566245325, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7137119378362383, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7137119378362383}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745566837891, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745566847554, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745566847554, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7144972681999207, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7144972681999207}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745567441551, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745567451141, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745567451141, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7152219454447428, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7152219454447428}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568051665, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745568061105, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568061106, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7158037361644564, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7158037361644564}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745568652783, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745568662536, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745568662536, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7160673771585737, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7160673771585737}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569255983, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745569265537, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745569265537, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716984482606252, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.716984482606252}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569865307, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745569874760, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745569874760, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7171691741262164, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7171691741262164}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570473145, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570482762, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745570482763, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7180932062012809, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7180932062012809}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571082302, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571091949, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571091950, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7185394008954366, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7185394008954366}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571701179, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571710616, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745571710616, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718915491444724, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.718915491444724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572308022, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3148992, "step_num": 32802}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572317478, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3148992, "step_num": 32802, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572317479, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7197886319387526, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3148992, "masked_lm_accuracy": 0.7197886319387526}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572911332, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3298944, "step_num": 34364}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572920792, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3298944, "step_num": 34364, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572920792, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7201146880785624, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3298944, "masked_lm_accuracy": 0.7201146880785624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572920793, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3298944}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572920793, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,93 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745572941616, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572941629, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572941630, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572941630, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572941630, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572942291, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745572942291, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574879961, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574890440, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574890454, "event_type": "POINT_IN_TIME", "key": "seed", "value": 6986, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906135, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906135, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906135, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906135, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906135, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906136, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906136, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906136, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906136, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906136, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906136, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906136, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906136, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906137, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906137, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906137, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574906137, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745574955636, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575830554, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745575870376, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745575870377, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3737282611074902, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3737282611074902}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745576471845, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745576481412, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745576481413, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4033156321162269, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4033156321162269}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745577076023, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745577085523, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745577085523, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4883529495625269, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.4883529495625269}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745577681373, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745577691036, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745577691036, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6172424344789414, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6172424344789414}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578286633, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745578296292, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745578296292, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6984787407375518, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.6984787407375518}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745578891885, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745578901678, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745578901679, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7075154764311654, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7075154764311654}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745579495449, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745579505011, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745579505011, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7099487849644253, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7099487849644253}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745580100680, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745580110198, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745580110198, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7115290715580895, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7115290715580895}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745580703932, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745580713443, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745580713443, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7128627572740828, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7128627572740828}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745581308511, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745581318215, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745581318215, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7135378882998512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7135378882998512}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745581913204, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745581922724, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745581922724, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7148078146434965, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7148078146434965}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745582522356, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745582531872, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745582531872, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7152938303493318, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7152938303493318}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583125261, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583134766, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583134767, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7161467688424247, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7161467688424247}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583737025, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583746516, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745583746516, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.71663555928639, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.71663555928639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584340025, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584350995, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584350996, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7171961858159019, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7171961858159019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584955665, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584965146, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745584965146, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175660905383882, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7175660905383882}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585565937, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585575442, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745585575443, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181211761065892, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7181211761065892}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586170179, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586179685, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586179685, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7188269053186689, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7188269053186689}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586785370, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586794992, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745586794993, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191171118191311, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7191171118191311}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587394486, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587403988, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587403988, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7196645231474014, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7196645231474014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745587999303, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3148992, "step_num": 32802}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588008978, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3148992, "step_num": 32802, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588008978, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200902217910403, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3148992, "masked_lm_accuracy": 0.7200902217910403}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588008978, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3148992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588008979, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745588028932, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588028946, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588028946, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588028946, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588028946, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588029621, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745588029622, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589719583, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589729896, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589729909, "event_type": "POINT_IN_TIME", "key": "seed", "value": 4120, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745143, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745144, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745144, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745144, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745144, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745144, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745144, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745145, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745145, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745145, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745145, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745145, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745145, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745145, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745146, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745146, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589745146, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745589790515, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745590662059, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745590701366, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745590701367, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3738187574204944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3738187574204944}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591300740, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591310217, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591310217, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.407555852049873, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.407555852049873}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591904870, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591914359, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745591914359, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5020121426809402, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.5020121426809402}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745592506867, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745592517809, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745592517809, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6646601739383879, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6646601739383879}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745593110284, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745593120055, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745593120055, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.703122741835458, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.703122741835458}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745593713739, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745593723210, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745593723211, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7083731492360433, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7083731492360433}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745594317185, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745594326671, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745594326671, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7107717264266241, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7107717264266241}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745594919068, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745594928715, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745594928715, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7129231032871065, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7129231032871065}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745595522298, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745595531920, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745595531920, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7140601788248334, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7140601788248334}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596125286, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596134779, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596134779, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7149945645105271, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7149945645105271}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596732267, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596741919, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596741920, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7153055503254845, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7153055503254845}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597342010, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597351718, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597351718, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7162052637054807, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7162052637054807}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597945037, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597954497, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745597954497, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7168858408927917, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7168858408927917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598553278, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598562756, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598562756, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181580322129386, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7181580322129386}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745599155685, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745599165357, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745599165357, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7184280049233209, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7184280049233209}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745599763495, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745599773182, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745599773182, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189048784119743, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7189048784119743}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600378094, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600387579, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600387579, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.719234371752966, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.719234371752966}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745600994209, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601003674, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601003674, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200033778236026, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7200033778236026}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601003674, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2699136}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601003675, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745601024651, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601024665, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601024665, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601024665, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601024665, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601025341, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745601025341, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602641535, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602651782, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602651795, "event_type": "POINT_IN_TIME", "key": "seed", "value": 31501, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667014, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667014, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667014, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667014, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667014, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667015, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667015, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667015, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667015, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667015, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667015, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667016, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667016, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667016, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667016, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667016, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602667016, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745602720684, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603593793, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603633838, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745603633838, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3756687879562378, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3756687879562378}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604234034, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604243699, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604243700, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.41726759388333273, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.41726759388333273}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604836741, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604846207, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745604846207, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6361151451156253, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.6361151451156253}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605440504, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605449980, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605449981, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7018973787625631, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.7018973787625631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606043898, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606053400, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606053400, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7083071765445528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.7083071765445528}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606645367, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606656313, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745606656313, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7105425567854018, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7105425567854018}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607248432, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607258080, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607258080, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7116558080627805, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7116558080627805}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607851555, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607861222, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745607861223, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7134888819285802, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7134888819285802}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745608454820, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745608464280, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745608464280, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7140375443867275, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7140375443867275}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609055972, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609065428, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609065428, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7148039085524422, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7148039085524422}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609658543, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609668034, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745609668035, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7156602161271232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7156602161271232}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610261103, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610270737, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610270737, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716008236294701, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.716008236294701}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610862227, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610871748, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610871748, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7165316888264247, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7165316888264247}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611475921, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611485374, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745611485374, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7171947257859367, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7171947257859367}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612083788, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612094912, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612094912, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181121451514108, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7181121451514108}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612686207, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612695807, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745612695807, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7187516025134495, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7187516025134495}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613288639, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613298492, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613298493, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7186400470279511, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7186400470279511}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613892142, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613901611, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745613901611, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7196106592814128, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7196106592814128}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614498093, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614507551, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745614507551, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.719801956699008, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.719801956699008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615109551, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615119233, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615119233, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7204833462124779, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7204833462124779}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615119234, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2999040}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615119234, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745615138505, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615138519, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615138519, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615138520, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615138520, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615139198, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745615139199, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616751147, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616761345, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616761359, "event_type": "POINT_IN_TIME", "key": "seed", "value": 10057, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776690, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776691, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776691, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776691, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776691, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776691, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776691, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776691, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776692, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776692, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776692, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776692, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776692, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776692, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776693, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776693, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616776693, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616820038, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745617693199, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745617733683, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745617733683, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37397653659184776, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.37397653659184776}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745618335201, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745618344856, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745618344856, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3990937738191514, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.3990937738191514}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745618939487, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745618948957, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745618948957, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.46658501823743187, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.46658501823743187}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745619545017, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745619554509, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745619554509, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6130311591284615, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6130311591284615}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745620150097, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745620159777, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745620159777, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.699844073113941, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.699844073113941}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745620753981, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745620764905, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745620764906, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7078652035622369, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7078652035622369}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745621358745, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745621368403, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745621368404, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7104341376395452, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7104341376395452}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745621963465, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745621973218, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745621973218, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7125999070349194, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7125999070349194}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745622568888, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745622578553, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745622578553, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7138977993102301, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7138977993102301}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745623172627, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745623182469, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745623182469, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7147745728492737, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7147745728492737}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745623788549, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745623798248, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745623798248, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7157551918710982, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7157551918710982}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745624393638, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745624403132, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745624403132, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7161134055682591, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7161134055682591}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745625005439, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745625014940, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745625014940, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7176308717046466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7176308717046466}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745625609773, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745625619252, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745625619252, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7178728024164835, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7178728024164835}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745626226550, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745626236225, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745626236226, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7182088261558897, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7182088261558897}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745626829609, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745626839220, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745626839221, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189385493596395, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7189385493596395}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745627434203, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745627443687, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745627443688, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195644435428438, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7195644435428438}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628036848, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628047895, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628047895, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7202766804468064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7202766804468064}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628047896, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2699136}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628047896, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745628067289, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628067303, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628067303, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628067303, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628067303, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628067966, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628067966, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629666209, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629677049, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629677063, "event_type": "POINT_IN_TIME", "key": "seed", "value": 12465, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691974, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691974, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691975, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691975, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691975, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691975, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691975, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691975, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691975, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691976, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691976, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691976, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691976, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691976, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691976, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691976, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629691976, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745629740990, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745630621098, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745630660651, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745630660651, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37397750247092476, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.37397750247092476}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745631266361, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745631276039, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745631276039, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4039459753604162, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4039459753604162}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745631876011, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745631885494, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745631885494, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.46332124216215953, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.46332124216215953}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745632483680, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745632493164, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745632493164, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5909533494994754, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.5909533494994754}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745633092727, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745633102224, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745633102224, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.695507520153409, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.695507520153409}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745633701690, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745633711166, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745633711166, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.70634758018312, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.70634758018312}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745634308886, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745634318381, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745634318382, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7093000508490063, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7093000508490063}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745634917081, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745634926554, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745634926554, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7113453558513096, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7113453558513096}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745635523693, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745635534738, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745635534738, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7130639910697937, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7130639910697937}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745636131809, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745636141329, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745636141330, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7139686294964381, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7139686294964381}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745636739800, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745636749545, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745636749545, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7148919718606132, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7148919718606132}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745637359823, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745637369305, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745637369306, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7157319557099115, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7157319557099115}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745637971557, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745637981047, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745637981047, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7163103591828119, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7163103591828119}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745638579005, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745638588492, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745638588493, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7172297625314622, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7172297625314622}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745639192542, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745639202337, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745639202338, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7177095975194658, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7177095975194658}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745639798352, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745639807842, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745639807842, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181210790361677, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7181210790361677}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745640406137, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745640415674, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745640415674, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7187060095015027, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7187060095015027}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745641012089, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745641021561, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745641021562, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191783354395912, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7191783354395912}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745641631830, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745641641317, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745641641317, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195633859861464, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7195633859861464}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642245529, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642255025, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642255026, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7203016593342736, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7203016593342736}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642255026, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2999040}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642255026, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,102 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745642274895, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642274909, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642274909, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642274909, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642274909, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642275592, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745642275592, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643905216, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643915672, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643915685, "event_type": "POINT_IN_TIME", "key": "seed", "value": 11730, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930970, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930970, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930970, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930971, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930971, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930971, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930971, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930971, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930971, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930971, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930972, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930972, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930972, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930972, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930972, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930972, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643930972, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745643991209, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745644864276, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745644905184, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745644905184, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3781838553292411, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3781838553292411}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745645504653, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745645514115, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745645514116, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4123739058063144, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4123739058063144}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745646105727, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745646115516, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745646115516, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4941163951442355, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.4941163951442355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745646708029, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745646717701, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745646717701, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5998574114981152, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.5998574114981152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745647310461, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745647319938, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745647319938, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6882066153344654, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.6882066153344654}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745647910916, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745647920440, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745647920441, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7018242018563406, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7018242018563406}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745648513412, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745648522931, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745648522932, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7060379794665745, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7060379794665745}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745649115751, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745649125216, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745649125217, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7098249503544398, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7098249503544398}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745649715710, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745649725226, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745649725226, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.710793800013406, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.710793800013406}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745650317598, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745650327086, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745650327086, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7119041085243225, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7119041085243225}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745650919471, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745650928949, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745650928949, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7127983939080011, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7127983939080011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745651519561, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745651529185, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745651529186, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7138419349988302, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7138419349988302}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745652122200, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745652131782, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745652131782, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7147189242499216, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7147189242499216}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745652730615, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745652740312, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745652740312, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7152118898573376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7152118898573376}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745653331444, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745653340998, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745653340998, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.715751085962568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.715751085962568}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745653933389, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745653942898, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745653942898, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.71591789268312, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.71591789268312}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745654540794, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745654550615, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745654550616, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716970343816848, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.716970343816848}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745655142498, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745655151972, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745655151972, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7177020226206098, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7177020226206098}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745655742995, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745655752627, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745655752627, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181210052399408, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7181210052399408}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745656351799, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745656361268, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745656361268, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181035654885428, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7181035654885428}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745656967639, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3148992, "step_num": 32802}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745656978577, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3148992, "step_num": 32802, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745656978577, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189842661221822, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3148992, "masked_lm_accuracy": 0.7189842661221822}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745657575734, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3298944, "step_num": 34364}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745657585375, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3298944, "step_num": 34364, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745657585375, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7194626552718026, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3298944, "masked_lm_accuracy": 0.7194626552718026}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658178120, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3448896, "step_num": 35926}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745658187683, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3448896, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3448896, "step_num": 35926, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658187683, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7197951980999537, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3448896, "masked_lm_accuracy": 0.7197951980999537}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658780502, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3598848, "step_num": 37488}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658789976, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3598848, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3598848, "step_num": 37488, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658789976, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200545878637404, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3598848, "masked_lm_accuracy": 0.7200545878637404}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658789977, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3598848, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3598848}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658789977, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745658809591, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658809604, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658809604, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658809604, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658809604, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658810272, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745658810273, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660394086, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660404397, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660404410, "event_type": "POINT_IN_TIME", "key": "seed", "value": 30006, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419324, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419324, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419324, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419324, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419325, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419325, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419325, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419325, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419325, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419325, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419325, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419326, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419326, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419326, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419326, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419326, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660419326, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745660468452, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745661348515, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745661388786, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745661388787, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37456812262535094, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.37456812262535094}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745661996315, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745662005821, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745662005821, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4560752814724332, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4560752814724332}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745662609218, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745662618735, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745662618736, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6749432524045308, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.6749432524045308}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745663219268, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745663228798, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745663228798, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7049068002473741, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.7049068002473741}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745663830866, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745663840454, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745663840455, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7094300275757199, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.7094300275757199}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745664440883, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745664450546, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745664450546, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7118306568690709, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7118306568690709}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745665052620, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745665062331, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745665062331, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7131405120804196, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7131405120804196}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745665664579, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745665674079, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745665674079, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7141566656884692, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7141566656884692}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745666274721, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745666285824, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745666285824, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.714853200458345, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.714853200458345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745666886316, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745666895839, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745666895839, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7154801947729929, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7154801947729929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745667505514, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745667515034, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745667515034, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7160271604855856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7160271604855856}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745668117399, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745668126911, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745668126912, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7161095375106448, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7161095375106448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745668727427, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745668736920, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745668736921, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7172707217080253, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7172707217080253}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745669347896, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745669357386, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745669357386, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181751222837539, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7181751222837539}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745669959126, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745669968787, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745669968787, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718704723176502, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.718704723176502}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745670573606, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745670583206, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745670583206, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7194259950092861, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7194259950092861}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671185025, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671194623, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671194623, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.71981109891619, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.71981109891619}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671799743, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671810731, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671810731, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7204012291772025, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7204012291772025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671810731, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2699136}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671810732, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,90 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745671829193, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671829206, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671829207, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671829207, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671829207, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671829888, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745671829889, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673412514, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673423084, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673423098, "event_type": "POINT_IN_TIME", "key": "seed", "value": 27130, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438047, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438047, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438047, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438047, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438048, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438048, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438048, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438048, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438048, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438048, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438048, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438049, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438049, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438049, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438049, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438049, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673438049, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745673490539, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745674363852, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745674404525, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745674404526, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37596466257458644, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.37596466257458644}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745675008663, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745675018130, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745675018130, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4192629811309633, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4192629811309633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745675616281, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745675625972, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745675625973, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5021054284913199, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.5021054284913199}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745676222268, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745676232040, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745676232041, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6380213788577489, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6380213788577489}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745676829655, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745676839323, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745676839324, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7022705288160415, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.7022705288160415}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745677435100, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745677446056, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745677446057, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7073904724348159, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7073904724348159}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745678041778, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745678051320, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745678051320, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7095591738110497, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7095591738110497}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745678648680, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745678658179, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745678658180, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7115420869418553, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7115420869418553}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745679256309, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745679265943, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745679265943, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7130387618428184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7130387618428184}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745679861819, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745679871292, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745679871292, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7135955617541359, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7135955617541359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745680468796, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745680478297, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745680478297, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7146279448554629, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7146279448554629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745681075639, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745681085107, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745681085107, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7151510505449205, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7151510505449205}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745681680758, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745681690230, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745681690230, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7165615944635301, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7165615944635301}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745682292031, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745682301481, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745682301482, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7166609871955145, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7166609871955145}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745682896874, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745682907952, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745682907952, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717455704439254, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.717455704439254}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745683523972, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745683533688, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745683533689, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7179811846642267, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7179811846642267}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745684130394, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745684139891, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745684139891, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7185609034129552, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7185609034129552}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745684736989, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745684746439, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745684746439, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191170454025269, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7191170454025269}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745685345638, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745685355274, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745685355275, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.71960460628782, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.71960460628782}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745685966333, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745685976054, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745685976054, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200535161154611, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7200535161154611}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745685976055, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2999040}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745685976055, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745596628137, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 382}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596628178, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 383}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596628178, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 384}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596628178, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 385}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596628178, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "retinanet", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 387}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596629954, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 390}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745596629955, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 391}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598065772, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 658}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598081470, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 394}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598081512, "event_type": "POINT_IN_TIME", "key": "seed", "value": 23282, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 395}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088273, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088274, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 12191, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 505}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088274, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 259, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 506}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088274, "event_type": "POINT_IN_TIME", "key": "epoch_count", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 507}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088274, "event_type": "POINT_IN_TIME", "key": "first_epoch_num", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 508}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088274, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adam", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 510}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088274, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 9.5e-05, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 511}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088274, "event_type": "POINT_IN_TIME", "key": "opt_weight_decay", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 512}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088274, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_epochs", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 513}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088275, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_factor", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 514}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598088275, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 515}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745598144406, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 527, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605078062, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 598, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745605078063, "event_type": "INTERVAL_START", "key": "eval_start", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 603, "epoch_num": 1}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610378469, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.2608930553164607, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 679, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610378469, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 680, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745610378469, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 527, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616941326, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 598, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745616941327, "event_type": "INTERVAL_START", "key": "eval_start", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 603, "epoch_num": 2}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745622185857, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.31207695716564665, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 679, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745622185858, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 680, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745622185858, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 527, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628713800, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 598, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745628713800, "event_type": "INTERVAL_START", "key": "eval_start", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 603, "epoch_num": 3}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745633828548, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.32695300496649193, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 679, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745633828548, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 680, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745633828549, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 527, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745640403678, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 598, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745640403679, "event_type": "INTERVAL_START", "key": "eval_start", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 603, "epoch_num": 4}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745645485614, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.34190927146960864, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 679, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745645485615, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 680, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745645485615, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 686, "status": "success"}}
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745708052929, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 380}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708052970, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 381}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708052970, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 382}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708052970, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 383}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708052970, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "retinanet", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 385}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708055312, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 388}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708055312, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 389}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709484510, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 656}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709499880, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 392}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709499922, "event_type": "POINT_IN_TIME", "key": "seed", "value": 3218, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 393}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506804, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 502}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506805, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 12191, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 503}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506805, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 259, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506805, "event_type": "POINT_IN_TIME", "key": "epoch_count", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 505}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506806, "event_type": "POINT_IN_TIME", "key": "first_epoch_num", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 506}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506806, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adam", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 508}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506806, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 9.5e-05, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 509}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506806, "event_type": "POINT_IN_TIME", "key": "opt_weight_decay", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 510}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506806, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_epochs", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 511}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506806, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_factor", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 512}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709506806, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 513}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745709564057, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745716423332, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745716423333, "event_type": "INTERVAL_START", "key": "eval_start", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 1}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745721892086, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.2644758301871188, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745721892087, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745721892087, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745728717917, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745728717918, "event_type": "INTERVAL_START", "key": "eval_start", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 2}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745734129092, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3183940553292647, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745734129092, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745734129092, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745740758848, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745740758849, "event_type": "INTERVAL_START", "key": "eval_start", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 3}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745746017219, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.330829179299047, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745746017219, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745746017219, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745752685505, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745752685506, "event_type": "INTERVAL_START", "key": "eval_start", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 4}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745757915230, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3430538198992862, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745757915231, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745757915231, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 684, "status": "success"}}
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745757942370, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 380}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745757942411, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 381}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745757942411, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 382}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745757942411, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 383}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745757942411, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "retinanet", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 385}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745757943058, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 388}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745757943059, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 389}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759379793, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 656}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759394363, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 392}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759394404, "event_type": "POINT_IN_TIME", "key": "seed", "value": 7068, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 393}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401265, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 502}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401265, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 12191, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 503}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401266, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 259, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401266, "event_type": "POINT_IN_TIME", "key": "epoch_count", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 505}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401266, "event_type": "POINT_IN_TIME", "key": "first_epoch_num", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 506}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401266, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adam", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 508}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401266, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 9.5e-05, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 509}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401266, "event_type": "POINT_IN_TIME", "key": "opt_weight_decay", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 510}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401266, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_epochs", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 511}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401267, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_factor", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 512}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759401267, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 513}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759458864, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745766229351, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745766229352, "event_type": "INTERVAL_START", "key": "eval_start", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 1}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745771664180, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.2618442233208197, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745771664180, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745771664181, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745778271730, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745778271731, "event_type": "INTERVAL_START", "key": "eval_start", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 2}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745783649281, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3050222595524408, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745783649281, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745783649282, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745790269694, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745790269695, "event_type": "INTERVAL_START", "key": "eval_start", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 3}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795622203, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.33438554461867026, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795622204, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795622204, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802201592, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802201593, "event_type": "INTERVAL_START", "key": "eval_start", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 4}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807409644, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3413173788267323, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807409644, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807409644, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 684, "status": "success"}}
|
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|
|
@ -0,0 +1,46 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745708740629, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 380}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708740670, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 381}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708740670, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 382}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708740670, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 383}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708740670, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "retinanet", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 385}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708753515, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 388}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745708753515, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 389}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710196875, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 656}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710211866, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 392}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710211906, "event_type": "POINT_IN_TIME", "key": "seed", "value": 1934, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 393}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219928, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 502}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219929, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 12191, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 503}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219929, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 259, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219929, "event_type": "POINT_IN_TIME", "key": "epoch_count", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 505}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219929, "event_type": "POINT_IN_TIME", "key": "first_epoch_num", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 506}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219930, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adam", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 508}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219930, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 9.5e-05, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 509}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219930, "event_type": "POINT_IN_TIME", "key": "opt_weight_decay", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 510}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219930, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_epochs", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 511}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219930, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_factor", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 512}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710219930, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 513}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745710276595, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745717038732, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745717038733, "event_type": "INTERVAL_START", "key": "eval_start", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 1}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745722476155, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.24994336549495808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745722476156, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745722476156, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745729177485, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745729177486, "event_type": "INTERVAL_START", "key": "eval_start", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 2}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745734589630, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.30947442932060776, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745734589630, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745734589630, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745741107714, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745741107714, "event_type": "INTERVAL_START", "key": "eval_start", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 3}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745746523920, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3304143886715271, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745746523920, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745746523920, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745753134001, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745753134002, "event_type": "INTERVAL_START", "key": "eval_start", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 4}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745758428287, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3429861420134466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745758428288, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745758428288, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 684, "status": "success"}}
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745758455763, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 380}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745758455804, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 381}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745758455804, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 382}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745758455804, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 383}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745758455805, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "retinanet", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 385}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745758457940, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 388}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745758457941, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 389}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759900517, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 656}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759915495, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 392}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759915536, "event_type": "POINT_IN_TIME", "key": "seed", "value": 25159, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 393}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922365, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 502}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922366, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 12191, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 503}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922366, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 259, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922366, "event_type": "POINT_IN_TIME", "key": "epoch_count", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 505}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922366, "event_type": "POINT_IN_TIME", "key": "first_epoch_num", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 506}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922366, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adam", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 508}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922366, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 9.5e-05, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 509}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922367, "event_type": "POINT_IN_TIME", "key": "opt_weight_decay", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 510}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922367, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_epochs", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 511}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922367, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_factor", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 512}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759922367, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 513}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745759981024, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745766937876, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745766937877, "event_type": "INTERVAL_START", "key": "eval_start", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 1}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745772433927, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.25660616888772175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745772433927, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 1}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745772433927, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745779249804, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745779249805, "event_type": "INTERVAL_START", "key": "eval_start", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 2}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745784709047, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3114751446994825, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745784709048, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 2}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745784709048, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791366481, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791366482, "event_type": "INTERVAL_START", "key": "eval_start", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 3}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796796512, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.33395135022162803, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796796512, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 3}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796796512, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 525, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803562272, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 596, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803562273, "event_type": "INTERVAL_START", "key": "eval_start", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 601, "epoch_num": 4}}
|
||||
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745808971898, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3397162205764848, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 677, "epoch_num": 4}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745808971899, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 4, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 678, "epoch_num": 4}}
|
||||
|
|
@ -0,0 +1,93 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745787472660, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787472675, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787472675, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787472675, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787472675, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787472820, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787472820, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789021684, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789063002, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789063016, "event_type": "POINT_IN_TIME", "key": "seed", "value": 11341, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084054, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084054, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084054, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084054, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084055, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084055, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084055, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084055, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084055, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084055, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084056, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084056, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084056, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084056, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084056, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084056, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789084056, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789130103, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745790243120, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745790285403, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745790285404, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3725697540101551, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3725697540101551}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791130486, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791146651, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791146651, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4053410061768123, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4053410061768123}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791993483, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792009647, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792009647, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4546036266145252, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.4546036266145252}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792857040, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792873247, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792873248, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5833114096096583, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.5833114096096583}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745793720402, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745793736590, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745793736590, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6891417679332551, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.6891417679332551}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745794586055, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745794602267, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745794602267, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7027380545934041, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7027380545934041}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795451897, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795468074, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795468074, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7082311675662086, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7082311675662086}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796316509, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796332716, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796332716, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7108195407049996, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7108195407049996}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745797182147, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745797198362, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745797198363, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7119770498502822, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7119770498502822}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798047629, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798063852, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798063852, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7129385369164604, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7129385369164604}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798917116, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798933320, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798933320, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.713829753512428, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.713829753512428}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799781286, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799797515, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799797515, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7143420588402521, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7143420588402521}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745800650547, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745800666777, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745800666777, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7151911968276614, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7151911968276614}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745801513018, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745801529219, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745801529219, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7162183267729623, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7162183267729623}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802383777, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802399993, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802399993, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7163212441262745, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7163212441262745}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803252264, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803268480, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803268480, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175917211033049, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7175917211033049}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745804120412, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745804136630, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745804136630, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7178130229314168, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7178130229314168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745804990109, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805006323, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805006324, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7182639241218567, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7182639241218567}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805853485, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805869680, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805869680, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7185757029624212, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7185757029624212}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806722533, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806738689, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806738690, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192848898115612, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7192848898115612}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807591120, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3148992, "step_num": 32802}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807607349, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3148992, "step_num": 32802, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807607349, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200041702815465, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3148992, "masked_lm_accuracy": 0.7200041702815465}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807607350, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3148992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807607350, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745807635676, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807635691, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807635691, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807635691, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807635691, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807635800, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807635800, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809130305, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809170647, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809170661, "event_type": "POINT_IN_TIME", "key": "seed", "value": 9893, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187044, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187045, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187045, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187045, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187045, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187045, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187046, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187046, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187046, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187046, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187046, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187046, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187046, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187047, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187047, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187047, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809187047, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809235620, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745810359119, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745810401248, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745810401248, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3756656459399632, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3756656459399632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811253490, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811270105, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811270105, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.408587916692098, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.408587916692098}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745812123931, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745812140534, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745812140535, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.48328256692205157, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.48328256692205157}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745812994623, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813011247, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813011247, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6104218710036505, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6104218710036505}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813866035, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813882688, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813882689, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6986918545904613, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.6986918545904613}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745814739714, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745814756340, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745814756340, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7074495644796462, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7074495644796462}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745815613451, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745815630089, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745815630090, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7102503475688753, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7102503475688753}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745816485859, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745816502544, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745816502545, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7119106928507487, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7119106928507487}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745817359005, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745817375661, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745817375662, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7136906277565729, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7136906277565729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818232065, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818248716, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818248716, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7140245165143694, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7140245165143694}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819109151, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819125778, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819125778, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7150250207810175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7150250207810175}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819981082, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819997739, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819997739, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7156877937771026, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7156877937771026}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745820853355, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745820870012, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745820870012, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7163892950330462, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7163892950330462}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821728724, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821745361, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821745361, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7172291585377284, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7172291585377284}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745822604259, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745822620919, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745822620919, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717616525718144, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.717616525718144}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823480930, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823497590, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823497590, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718469910962241, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.718469910962241}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824350574, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824368623, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824368624, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718998844850631, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.718998844850631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745825222149, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745825238786, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745825238786, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7196502293859209, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7196502293859209}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826105033, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826121638, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826121638, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198895817711239, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7198895817711239}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826976460, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826993116, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826993116, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7203692260242643, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7203692260242643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826993117, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2999040}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826993117, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745827020398, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745827020413, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745827020413, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745827020413, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745827020413, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745827020525, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745827020525, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828557523, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828597922, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828597937, "event_type": "POINT_IN_TIME", "key": "seed", "value": 11581, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614084, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614084, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614084, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614085, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614085, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614085, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614085, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614085, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614085, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614085, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614086, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614086, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614086, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614086, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614086, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614086, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828614086, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828663687, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745829766682, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745829807504, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745829807505, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37525955580529713, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.37525955580529713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745830642144, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745830658143, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745830658143, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4122683204355694, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4122683204355694}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831494398, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831510372, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831510372, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.49358772947674706, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.49358772947674706}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745832347052, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745832363071, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745832363071, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6273789207140604, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6273789207140604}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833200443, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833217885, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833217885, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7020660332271031, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.7020660332271031}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745834055605, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745834071615, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745834071615, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7076224071638925, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7076224071638925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745834910861, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745834926878, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745834926878, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7098002570016043, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7098002570016043}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745835766033, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745835782039, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745835782039, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7117016775267465, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7117016775267465}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745836619573, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745836635604, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745836635604, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7135275136856806, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7135275136856806}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745837474097, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745837490110, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745837490110, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7144534780865623, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7144534780865623}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745838326894, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745838344267, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745838344267, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7147606486365908, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7147606486365908}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745839180844, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745839196883, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745839196883, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7152229150136312, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7152229150136312}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840040064, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840056101, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840056102, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7165876416932969, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7165876416932969}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840891976, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840909372, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840909372, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7169663355464027, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7169663355464027}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745841750085, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745841766074, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745841766074, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7178037592342922, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7178037592342922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745842606890, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745842622892, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745842622892, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7182614950906663, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7182614950906663}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843459937, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843475947, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843475948, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189727822939554, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7189727822939554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745844322719, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745844338697, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745844338698, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7194424441882542, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7194424441882542}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745845183319, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745845199341, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745845199342, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7199910112789699, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7199910112789699}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745846049798, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745846065808, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745846065809, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200804040545509, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7200804040545509}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745846065809, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2999040}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745846065809, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,93 @@
|
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:::MLLOG {"namespace": "", "time_ms": 1745766145092, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745766145106, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745766145106, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745766145106, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
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||||
:::MLLOG {"namespace": "", "time_ms": 1745766145107, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
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||||
:::MLLOG {"namespace": "", "time_ms": 1745766145229, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
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:::MLLOG {"namespace": "", "time_ms": 1745766145229, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
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:::MLLOG {"namespace": "", "time_ms": 1745767375299, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
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:::MLLOG {"namespace": "", "time_ms": 1745767395079, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
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:::MLLOG {"namespace": "", "time_ms": 1745767395094, "event_type": "POINT_IN_TIME", "key": "seed", "value": 27065, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411312, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411312, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411312, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411312, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411313, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745767411313, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411313, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411313, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411313, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411313, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745767411314, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411314, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745767411314, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411314, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745767411314, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
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:::MLLOG {"namespace": "", "time_ms": 1745767411314, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745767411314, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745767458799, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
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:::MLLOG {"namespace": "", "time_ms": 1745768572452, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
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:::MLLOG {"namespace": "", "time_ms": 1745768621526, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745768621527, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.373991007180441, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.373991007180441}}
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:::MLLOG {"namespace": "", "time_ms": 1745769469811, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
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:::MLLOG {"namespace": "", "time_ms": 1745769486194, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745769486194, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4115104800178891, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4115104800178891}}
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:::MLLOG {"namespace": "", "time_ms": 1745770335677, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
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:::MLLOG {"namespace": "", "time_ms": 1745770352069, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745770352069, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5021448158082508, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.5021448158082508}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745771201704, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
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:::MLLOG {"namespace": "", "time_ms": 1745771218087, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745771218087, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6750143703960237, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6750143703960237}}
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:::MLLOG {"namespace": "", "time_ms": 1745772067913, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
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:::MLLOG {"namespace": "", "time_ms": 1745772084343, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745772084343, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7019343614578247, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.7019343614578247}}
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:::MLLOG {"namespace": "", "time_ms": 1745772937902, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
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:::MLLOG {"namespace": "", "time_ms": 1745772954292, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745772954292, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7071203810828073, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7071203810828073}}
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:::MLLOG {"namespace": "", "time_ms": 1745773806065, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
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:::MLLOG {"namespace": "", "time_ms": 1745773822470, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745773822470, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7098041250592186, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7098041250592186}}
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:::MLLOG {"namespace": "", "time_ms": 1745774672651, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
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:::MLLOG {"namespace": "", "time_ms": 1745774689034, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745774689034, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7114683633758908, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7114683633758908}}
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:::MLLOG {"namespace": "", "time_ms": 1745775540181, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
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:::MLLOG {"namespace": "", "time_ms": 1745775556554, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745775556554, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7130643929753985, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7130643929753985}}
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:::MLLOG {"namespace": "", "time_ms": 1745776408843, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
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:::MLLOG {"namespace": "", "time_ms": 1745776425260, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745776425260, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7135605426061721, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7135605426061721}}
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:::MLLOG {"namespace": "", "time_ms": 1745777274643, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
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:::MLLOG {"namespace": "", "time_ms": 1745777291080, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745777291080, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7143973344848269, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7143973344848269}}
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:::MLLOG {"namespace": "", "time_ms": 1745778145622, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
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:::MLLOG {"namespace": "", "time_ms": 1745778161980, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745778161980, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7153716751507351, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7153716751507351}}
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:::MLLOG {"namespace": "", "time_ms": 1745779016330, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
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:::MLLOG {"namespace": "", "time_ms": 1745779032715, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745779032715, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7156423943383353, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7156423943383353}}
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:::MLLOG {"namespace": "", "time_ms": 1745779880722, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
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:::MLLOG {"namespace": "", "time_ms": 1745779897142, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745779897142, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7163417912664868, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7163417912664868}}
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:::MLLOG {"namespace": "", "time_ms": 1745780753157, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
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:::MLLOG {"namespace": "", "time_ms": 1745780769553, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745780769554, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7172225815909249, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7172225815909249}}
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:::MLLOG {"namespace": "", "time_ms": 1745781622729, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
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:::MLLOG {"namespace": "", "time_ms": 1745781639078, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745781639078, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175244842256818, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7175244842256818}}
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:::MLLOG {"namespace": "", "time_ms": 1745782486272, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
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:::MLLOG {"namespace": "", "time_ms": 1745782502649, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745782502649, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7186253524961925, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7186253524961925}}
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:::MLLOG {"namespace": "", "time_ms": 1745783352552, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
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:::MLLOG {"namespace": "", "time_ms": 1745783368943, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745783368944, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7186818900562468, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7186818900562468}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745784226008, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745784242458, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745784242459, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7187245652789161, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7187245652789161}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745785100858, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745785117223, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745785117223, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.719807653767722, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.719807653767722}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745785971577, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3148992, "step_num": 32802}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745785987960, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3148992, "step_num": 32802, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745785987960, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7201530842554001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3148992, "masked_lm_accuracy": 0.7201530842554001}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745785987961, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3148992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745785987961, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,93 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745786017497, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745786017512, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745786017512, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745786017512, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745786017512, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745786017635, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745786017635, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787211109, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787229721, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787229735, "event_type": "POINT_IN_TIME", "key": "seed", "value": 32670, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245941, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245941, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245942, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245942, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245942, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245942, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245942, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245942, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245942, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245943, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245943, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245943, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245943, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245943, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245943, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245943, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787245944, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787291328, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745788420396, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745788465722, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745788465723, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3736442960443951, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3736442960443951}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789321979, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789338432, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789338433, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.400805332830974, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.400805332830974}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745790194764, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745790211239, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745790211239, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.46333109367461434, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.46333109367461434}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791069993, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791086453, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791086454, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.606940507888794, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.606940507888794}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791944536, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791961053, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791961053, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6969162089484079, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.6969162089484079}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792823274, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792839814, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792839815, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7066007188388279, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7066007188388279}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745793701391, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745793717919, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745793717919, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7095723350842794, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7095723350842794}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745794576972, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745794593484, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745794593485, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7117142688660395, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7117142688660395}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795452646, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795469118, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795469119, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7129210210981823, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7129210210981823}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796328189, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796346080, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796346081, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7135851048287891, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7135851048287891}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745797206117, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745797222639, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745797222639, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7144846972965059, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7144846972965059}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798099453, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798115967, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798115967, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7151809629939851, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7151809629939851}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798972875, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798989390, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798989390, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.715795217809223, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.715795217809223}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799851968, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799868449, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799868449, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7165858081408909, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7165858081408909}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745800726576, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745800743139, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745800743139, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7168737269583203, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7168737269583203}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745801600341, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745801616830, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745801616830, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7174848630314782, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7174848630314782}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802471269, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802487752, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802487753, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7178929272152129, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7178929272152129}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803350240, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803366751, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803366751, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7188977406138466, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7188977406138466}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745804227786, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745804245700, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745804245700, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190916072754633, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7190916072754633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805107689, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805124208, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805124208, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195343852043152, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7195343852043152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805981361, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3148992, "step_num": 32802}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805997841, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3148992, "step_num": 32802, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805997842, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7201732056481498, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3148992, "masked_lm_accuracy": 0.7201732056481498}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805997842, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3148992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805997842, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,84 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745806025363, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806025378, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806025378, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806025378, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806025378, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806025507, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806025507, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807261586, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807280095, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807280110, "event_type": "POINT_IN_TIME", "key": "seed", "value": 25877, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296270, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296271, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296271, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296271, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296271, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296271, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296272, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296272, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296272, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296272, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296272, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296273, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745807296273, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745807296273, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296273, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745807296273, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807296273, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745807345166, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745808458376, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745808503186, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745808503187, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.37366413615998767, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.37366413615998767}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745809355092, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809371487, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745809371488, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.41079950133959453, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.41079950133959453}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745810222251, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745810238608, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745810238609, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6318097074826559, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.6318097074826559}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745811087580, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745811103948, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745811103948, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6998206564358302, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6998206564358302}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745811956425, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745811972824, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745811972825, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7077281526156834, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.7077281526156834}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745812823492, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745812841248, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745812841248, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7099457195826939, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7099457195826939}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745813692988, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745813709364, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745813709364, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7113831173805963, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7113831173805963}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745814562349, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745814578741, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
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:::MLLOG {"namespace": "", "time_ms": 1745814578741, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7134005540893191, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7134005540893191}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745815431564, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745815447942, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745815447942, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7145692609605335, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7145692609605335}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745816298939, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745816315317, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745816315317, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7152422836848668, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7152422836848668}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745817166658, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745817183040, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745817183040, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7160442278498695, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7160442278498695}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818032767, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818050528, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818050529, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7162078914188204, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7162078914188204}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818901002, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818917373, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818917374, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717176822253636, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.717176822253636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819785490, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819801825, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819801826, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7179754376411438, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7179754376411438}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745820657021, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745820673391, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745820673392, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7183612340972537, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7183612340972537}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821521217, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821537581, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821537581, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189742480005537, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7189742480005537}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745822387221, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745822403579, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745822403579, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7194264003208706, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7194264003208706}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823259046, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823275427, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823275427, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200029481024969, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7200029481024969}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823275427, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2699136}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823275427, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,93 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745823302673, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823302687, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823302688, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823302688, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823302688, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823302819, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823302819, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824543991, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824562214, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824562229, "event_type": "POINT_IN_TIME", "key": "seed", "value": 28100, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578735, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578735, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578735, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578735, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578735, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578736, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578736, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578736, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578736, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578736, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578736, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578736, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745824578737, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745824578737, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578737, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578737, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824578737, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824627870, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745825742554, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745825790935, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745825790935, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3742778738339742, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3742778738339742}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826639341, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826655849, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745826655850, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.41123455422265187, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.41123455422265187}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745827506044, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745827522610, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745827522611, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.49199818543025425, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.49199818543025425}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745828373508, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745828390052, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745828390052, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6114951428912935, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6114951428912935}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745829241326, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745829257895, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745829257896, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7013765476998829, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.7013765476998829}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745830111499, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745830128070, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745830128070, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7078263350895473, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7078263350895473}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745830981253, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745830997843, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745830997843, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7105506675583976, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7105506675583976}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745831850249, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745831866851, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745831866851, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7127624767167228, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7127624767167228}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745832720859, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745832737429, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745832737429, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7134371825626918, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7134371825626918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833590656, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833607244, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833607244, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7143038937023708, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7143038937023708}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745834459648, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745834476236, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745834476236, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.715397086029961, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.715397086029961}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745835329853, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745835346430, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745835346430, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7157154185431344, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7157154185431344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745836205647, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745836222255, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745836222255, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7164555277143206, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7164555277143206}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745837074549, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745837091187, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745837091187, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7164888756615775, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7164888756615775}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745837955933, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745837972531, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745837972531, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7169959885733468, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7169959885733468}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745838824798, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745838841423, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745838841424, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717557556288583, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.717557556288583}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745839692680, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745839709268, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745839709269, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181111250604902, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7181111250604902}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840566476, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840583081, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840583082, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189842644191924, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7189842644191924}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745841439990, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745841456632, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745841456632, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190832745461236, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7190832745461236}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745842320481, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745842337097, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745842337097, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192168309575036, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7192168309575036}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843193503, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3148992, "step_num": 32802}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843210131, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3148992, "step_num": 32802, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843210131, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7202679208346776, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3148992, "masked_lm_accuracy": 0.7202679208346776}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843210131, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3148992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843210131, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,102 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745787802566, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787802594, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787802594, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787802594, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787802594, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787802726, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745787802727, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789594073, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789613521, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789613548, "event_type": "POINT_IN_TIME", "key": "seed", "value": 19308, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635762, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635763, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635763, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635763, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635763, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635763, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635764, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635764, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635764, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635764, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635764, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635765, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635765, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635765, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635765, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635766, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789635766, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745789698057, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745790966288, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791025068, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791025069, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3743333379427592, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3743333379427592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791894750, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791911957, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745791911958, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.40425296141987754, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.40425296141987754}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792783857, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792801077, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745792801077, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.492629117057437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.492629117057437}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745793672991, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745793690266, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745793690267, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6120388820057824, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.6120388820057824}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745794563811, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745794581118, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745794581119, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6954671644029163, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.6954671644029163}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795456868, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795474160, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745795474160, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7052601121720814, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7052601121720814}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796348727, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796365991, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745796365992, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7080547185171218, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7080547185171218}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745797241370, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745797258609, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745797258609, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7103598571958996, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7103598571958996}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798133476, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798150737, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745798150737, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7112415722438268, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7112415722438268}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799023898, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799041165, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799041165, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7124293872288295, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7124293872288295}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799924843, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799942107, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745799942107, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7139696609406244, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7139696609406244}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745800815328, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745800832615, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745800832615, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7143780969438098, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7143780969438098}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745801712120, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745801729406, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745801729406, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7155611310686384, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7155611310686384}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802601680, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802618955, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745802618956, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7154839975493295, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7154839975493295}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803490060, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803507331, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745803507332, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716192881266276, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.716192881266276}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745804380068, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745804397307, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745804397308, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7166955840019953, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7166955840019953}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805278057, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805295302, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745805295303, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717407656851269, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.717407656851269}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806170941, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806188188, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745806188188, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7178891250065395, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7178891250065395}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807059891, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807077172, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807077172, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7180268843968709, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7180268843968709}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807956172, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807973421, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745807973422, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189508608409336, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.7189508608409336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745808865958, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3148992, "step_num": 32802}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745808883154, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3148992, "step_num": 32802, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745808883154, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192203856649853, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3148992, "masked_lm_accuracy": 0.7192203856649853}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809754328, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3298944, "step_num": 34364}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809771550, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3298944, "step_num": 34364, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745809771550, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.719561649504162, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3298944, "masked_lm_accuracy": 0.719561649504162}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745810642103, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3448896, "step_num": 35926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745810659332, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3448896, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3448896, "step_num": 35926, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745810659333, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198069998196193, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3448896, "masked_lm_accuracy": 0.7198069998196193}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811532161, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 3598848, "step_num": 37488}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811549386, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3598848, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 3598848, "step_num": 37488, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811549386, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200410479591006, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 3598848, "masked_lm_accuracy": 0.7200410479591006}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811549387, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3598848, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 3598848}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811549387, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1745811581618, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811581646, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811581646, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811581646, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811581646, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811581770, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745811581771, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813333105, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813352889, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813412916, "event_type": "POINT_IN_TIME", "key": "seed", "value": 383, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435199, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435199, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435200, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435200, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435200, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435200, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435201, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435201, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435201, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435201, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435202, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435202, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435202, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435202, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435203, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435203, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813435203, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745813498099, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745814758540, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745814817661, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745814817662, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3754178938411531, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3754178938411531}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745815683185, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745815700873, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745815700874, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4043772734346844, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4043772734346844}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745816568437, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745816586125, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745816586125, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.46416939128012885, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.46416939128012885}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745817454256, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745817471985, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745817471985, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5808823159762792, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.5808823159762792}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818340940, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818358717, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745818358718, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6975844576245263, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.6975844576245263}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819230249, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819247985, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745819247985, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7072370296432858, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7072370296432858}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745820119013, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745820136733, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745820136733, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7102725159554254, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.7102725159554254}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821006045, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821023778, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821023779, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7125218317622231, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7125218317622231}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821894699, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821912431, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745821912431, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7138815391631353, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7138815391631353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745822784454, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745822803745, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745822803746, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.714685621148064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.714685621148064}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823676721, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823694574, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745823694575, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7150295643579392, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7150295643579392}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824569435, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824587278, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745824587278, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7158357126372201, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7158357126372201}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745825476604, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745825494462, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745825494462, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716386129742577, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.716386129742577}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826367004, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826384822, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745826384822, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7171017362957909, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7171017362957909}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745827257763, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745827275559, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745827275559, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717421099117824, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.717421099117824}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828152818, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828170593, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745828170594, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7179956731342134, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7179956731342134}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745829042290, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2549184, "step_num": 26554}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745829060104, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2549184, "step_num": 26554, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745829060105, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7186045289039612, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2549184, "masked_lm_accuracy": 0.7186045289039612}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745829932500, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2699136, "step_num": 28116}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745829950294, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2699136, "step_num": 28116, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745829950294, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190587202707927, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2699136, "masked_lm_accuracy": 0.7190587202707927}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745830838162, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2849088, "step_num": 29678}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745830855978, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2849088, "step_num": 29678, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745830855978, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192222112701052, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2849088, "masked_lm_accuracy": 0.7192222112701052}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831740334, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2999040, "step_num": 31240}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831758212, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2999040, "step_num": 31240, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831758213, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.720143475418999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2999040, "masked_lm_accuracy": 0.720143475418999}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831758213, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2999040}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831758213, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
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|
|
@ -0,0 +1,78 @@
|
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:::MLLOG {"namespace": "", "time_ms": 1745831791121, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 917}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831791148, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831791148, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 919}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831791148, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 920}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831791148, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831791273, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 925}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745831791274, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 926}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833591107, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833610931, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 929}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833640959, "event_type": "POINT_IN_TIME", "key": "seed", "value": 10542, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 930}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663181, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 96, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1007}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663182, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1008}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663182, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1009}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663182, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1011}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663183, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.000175, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1012}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663183, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663183, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1014}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663183, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1015}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663184, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663184, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1017}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663184, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1019}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663184, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1020}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663184, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1021}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663185, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 37500, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1022}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663185, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1023}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663185, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10080, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1024}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833663185, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3600000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745833717917, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1055, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745834985679, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 149952, "step_num": 1562}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745835045156, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 149952, "step_num": 1562, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745835045156, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3788086683977218, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 149952, "masked_lm_accuracy": 0.3788086683977218}}
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:::MLLOG {"namespace": "", "time_ms": 1745835918374, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 299904, "step_num": 3124}}
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:::MLLOG {"namespace": "", "time_ms": 1745835935660, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 299904, "step_num": 3124, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745835935660, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4123597074122656, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 299904, "masked_lm_accuracy": 0.4123597074122656}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745836810436, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 449856, "step_num": 4686}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745836827731, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 449856, "step_num": 4686, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745836827731, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5868216173989432, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 449856, "masked_lm_accuracy": 0.5868216173989432}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745837700592, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 599808, "step_num": 6248}}
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||||
:::MLLOG {"namespace": "", "time_ms": 1745837719403, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 599808, "step_num": 6248, "samples_count": 10080}}
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||||
:::MLLOG {"namespace": "", "time_ms": 1745837719404, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7020311525889805, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 599808, "masked_lm_accuracy": 0.7020311525889805}}
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||||
:::MLLOG {"namespace": "", "time_ms": 1745838594733, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 749760, "step_num": 7810}}
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||||
:::MLLOG {"namespace": "", "time_ms": 1745838612057, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 749760, "step_num": 7810, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745838612058, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7091197768847147, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 749760, "masked_lm_accuracy": 0.7091197768847147}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745839489790, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 899712, "step_num": 9372}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745839507098, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 899712, "step_num": 9372, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745839507098, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7120704963093712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 899712, "masked_lm_accuracy": 0.7120704963093712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840383347, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1049664, "step_num": 10934}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840400642, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1049664, "step_num": 10934, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745840400643, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.713741013549623, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1049664, "masked_lm_accuracy": 0.713741013549623}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745841277938, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1199616, "step_num": 12496}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745841295237, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1199616, "step_num": 12496, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745841295238, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7150049794287908, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1199616, "masked_lm_accuracy": 0.7150049794287908}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745842172781, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1349568, "step_num": 14058}}
|
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:::MLLOG {"namespace": "", "time_ms": 1745842190096, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1349568, "step_num": 14058, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745842190096, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7166263137544905, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1349568, "masked_lm_accuracy": 0.7166263137544905}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843065811, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1499520, "step_num": 15620}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843083177, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1499520, "step_num": 15620, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843083177, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7167705734570821, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1499520, "masked_lm_accuracy": 0.7167705734570821}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843967823, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1649472, "step_num": 17182}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843985219, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1649472, "step_num": 17182, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745843985219, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7174085940633501, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1649472, "masked_lm_accuracy": 0.7174085940633501}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745844871164, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1799424, "step_num": 18744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745844888581, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1799424, "step_num": 18744, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745844888582, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175841513134185, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1799424, "masked_lm_accuracy": 0.7175841513134185}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745845771506, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 1949376, "step_num": 20306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745845788927, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 1949376, "step_num": 20306, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745845788928, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190523573330471, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 1949376, "masked_lm_accuracy": 0.7190523573330471}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745846665765, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2099328, "step_num": 21868}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745846683091, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2099328, "step_num": 21868, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745846683091, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195040078390212, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2099328, "masked_lm_accuracy": 0.7195040078390212}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745847564414, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2249280, "step_num": 23430}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745847581796, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2249280, "step_num": 23430, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745847581797, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7199954066957747, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2249280, "masked_lm_accuracy": 0.7199954066957747}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745848457298, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1106, "epoch_num": 2399232, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745848474654, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1159, "epoch_count": 2399232, "step_num": 24992, "samples_count": 10080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745848474654, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7207992462884812, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1160, "epoch_num": 2399232, "masked_lm_accuracy": 0.7207992462884812}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745848474655, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1178, "epoch_num": 2399232}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1745848474655, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 1179, "status": "success"}}
|
||||
|
|
@ -28,7 +28,7 @@
|
|||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, commit TBD",
|
||||
"framework": "tinygrad, branch mlperf_training_v5.0",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.16",
|
||||
"ROCm": "3.0.0+94441cb"
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@
|
|||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, commit b5546912e24e0a864b35924da4efa5d71cfe368b",
|
||||
"framework": "tinygrad, branch mlperf_training_v5.0",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.12",
|
||||
"CUDA": "12.4"
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@
|
|||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, commit b5546912e24e0a864b35924da4efa5d71cfe368b",
|
||||
"framework": "tinygrad, branch mlperf_training_v5.0",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.12"
|
||||
},
|
||||
|
|
|
|||
Loading…
Add table
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Reference in a new issue