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mlperf_tra
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31 changed files with 1797 additions and 215 deletions
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# 1. Problem
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This problem uses the ResNet-50 CNN to do image classification.
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## Requirements
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Install tinygrad and mlperf-logging from master.
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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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### 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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### tinybox_red
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Disable cwsr
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This is the default on production tinybox red.
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```
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sudo vi /etc/modprobe.d/amdgpu.conf
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cat <<EOF > /etc/modprobe.d/amdgpu.conf
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options amdgpu cwsr_enable=0
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EOF
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sudo update-initramfs -u
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sudo reboot
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# validate
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sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
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```
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# 2. Directions
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## Steps to download and verify data
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```
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IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
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```
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## Steps for one time setup
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### tinybox_red
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```
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examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
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```
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## Steps to run benchmark
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```
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examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
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```
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@ -1,13 +0,0 @@
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#!/bin/bash
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export PYTHONPATH="."
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export MODEL="resnet"
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export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
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export LAZYCACHE=0 RESET_STEP=0
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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
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export BENCHMARK=10 DEBUG=2
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python3 examples/mlperf/model_train.py
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#!/bin/bash
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export PYTHONPATH="."
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export MODEL="resnet"
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export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
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export LAZYCACHE=0 RESET_STEP=0
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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
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export EVAL_START_EPOCH=3 EVAL_FREQ=4
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export WANDB=1 PARALLEL=0
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python3 examples/mlperf/model_train.py
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#!/bin/bash
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export PYTHONPATH="."
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export MODEL="resnet"
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export SUBMISSION_PLATFORM="tinybox_green"
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export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
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export LAZYCACHE=0 RESET_STEP=0
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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
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# pip install -e ".[mlperf]"
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export LOGMLPERF=1
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export SEED=$RANDOM
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DATETIME=$(date "+%m%d%H%M")
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LOGFILE="resnet_green_${DATETIME}_${SEED}.log"
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# init
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BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
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# run
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PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
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@ -1,50 +0,0 @@
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# 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
|
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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)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
### tinybox_red
|
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Disable cwsr
|
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This is the default on production tinybox red.
|
||||
```
|
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sudo vi /etc/modprobe.d/amdgpu.conf
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cat <<EOF > /etc/modprobe.d/amdgpu.conf
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options amdgpu cwsr_enable=0
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EOF
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sudo update-initramfs -u
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sudo reboot
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|
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# validate
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sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
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```
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|
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# 2. Directions
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||||
|
||||
## Steps to download and verify data
|
||||
|
||||
```
|
||||
IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
|
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```
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|
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## Steps for one time setup
|
||||
|
||||
### tinybox_red
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||||
```
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examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
|
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```
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## Steps to run benchmark
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```
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examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
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```
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@ -1,13 +0,0 @@
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#!/bin/bash
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export PYTHONPATH="."
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export MODEL="resnet"
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export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
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|
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export LAZYCACHE=0 RESET_STEP=0
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|
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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
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export BENCHMARK=10 DEBUG=2
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python3 examples/mlperf/model_train.py
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@ -1,15 +0,0 @@
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#!/bin/bash
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export PYTHONPATH="."
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export MODEL="resnet"
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export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
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|
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export LAZYCACHE=0 RESET_STEP=0
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|
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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
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export EVAL_START_EPOCH=3 EVAL_FREQ=4
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export WANDB=1 PARALLEL=0
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python3 examples/mlperf/model_train.py
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|
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@ -1,23 +0,0 @@
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#!/bin/bash
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export PYTHONPATH="."
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export MODEL="resnet"
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export SUBMISSION_PLATFORM="tinybox_red"
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export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
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export LAZYCACHE=0 RESET_STEP=0
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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
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# pip install -e ".[mlperf]"
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export LOGMLPERF=1
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export SEED=$RANDOM
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DATETIME=$(date "+%m%d%H%M")
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LOGFILE="resnet_red_${DATETIME}_${SEED}.log"
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# init
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BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
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# run
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PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
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#!/bin/bash
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rocm-smi --setprofile compute
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rocm-smi --setmclk 3
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rocm-smi --setperflevel high
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# power cap to 350W
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echo "350000000" | sudo tee /sys/class/drm/card{1..6}/device/hwmon/hwmon*/power1_cap
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@ -0,0 +1,87 @@
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:::MLLOG {"namespace": "", "time_ms": 1728516968768, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
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:::MLLOG {"namespace": "", "time_ms": 1728516968782, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
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:::MLLOG {"namespace": "", "time_ms": 1728516968782, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
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:::MLLOG {"namespace": "", "time_ms": 1728516968782, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
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:::MLLOG {"namespace": "", "time_ms": 1728516968782, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
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:::MLLOG {"namespace": "", "time_ms": 1728516968917, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
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:::MLLOG {"namespace": "", "time_ms": 1728516968917, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
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:::MLLOG {"namespace": "", "time_ms": 1728518095273, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
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:::MLLOG {"namespace": "", "time_ms": 1728518110874, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125400, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125401, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125401, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125401, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125401, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125401, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125402, "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": 720}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125403, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125403, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
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:::MLLOG {"namespace": "", "time_ms": 1728518125403, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
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:::MLLOG {"namespace": "", "time_ms": 1728518171154, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
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:::MLLOG {"namespace": "", "time_ms": 1728519204577, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
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:::MLLOG {"namespace": "", "time_ms": 1728519263743, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
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:::MLLOG {"namespace": "", "time_ms": 1728519263744, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38641827217854635, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38641827217854635}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728520267792, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
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||||
:::MLLOG {"namespace": "", "time_ms": 1728520321266, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728520321266, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.40444660376272445, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.40444660376272445}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728521322547, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728521376298, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728521376298, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4533385156548231, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4533385156548231}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728522377080, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728522429361, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728522429362, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5267527467952778, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5267527467952778}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728523431856, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728523485950, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728523485951, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6413663900499224, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6413663900499224}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524487365, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524539365, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524539365, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7015928945715869, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.7015928945715869}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728525540578, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728525593684, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728525593684, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7095211997458373, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7095211997458373}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526596673, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526649935, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526649936, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.712105579231768, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.712105579231768}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728527652974, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728527704774, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728527704774, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7137153520152179, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7137153520152179}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728528706518, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728528759227, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728528759227, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7148766237672532, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7148766237672532}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728529761683, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728529814822, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728529814823, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7160955339258992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7160955339258992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728530822786, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728530876907, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728530876907, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716297444534931, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.716297444534931}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728531883583, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728531936703, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728531936703, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7177683101775908, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7177683101775908}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728532940983, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728532993183, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728532993183, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718530326026889, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.718530326026889}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728534011810, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728534065533, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728534065533, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7185927641985298, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7185927641985298}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728535067978, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728535122144, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728535122145, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192297569276619, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7192297569276619}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728536131543, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728536184105, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728536184105, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7197693878473032, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7197693878473032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537200158, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537253408, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537253408, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7199979490266993, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7199979490266993}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538262628, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538316185, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538316186, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7208575420416825, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7208575420416825}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538316186, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2849088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538316186, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538316186, "event_type": "POINT_IN_TIME", "key": "seed", "value": 6505, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,99 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728538334148, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538334162, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538334162, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538334162, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538334162, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538334302, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538334302, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539452588, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539466234, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480889, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480890, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480890, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480890, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480890, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480890, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480890, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480891, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480891, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480891, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480891, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480891, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480891, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480891, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480891, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480892, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539480892, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539531181, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728540563757, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728540627488, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728540627488, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38798193001575504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38798193001575504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728541627605, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728541684840, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728541684840, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4043695551053306, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.4043695551053306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542682448, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542739985, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542739985, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.43848311595381845, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.43848311595381845}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543736385, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543792735, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543792736, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.519008471802029, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.519008471802029}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544788834, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544846253, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544846253, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6122590443583112, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6122590443583112}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545843895, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545901605, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545901606, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.687927868134545, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.687927868134545}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546896899, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546954045, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546954045, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7037530967627161, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7037530967627161}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728547951266, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548007399, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548007400, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7094319296154922, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7094319296154922}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549002681, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549061015, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549061015, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7113743569225913, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7113743569225913}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550055497, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550112524, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550112524, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7128702056715427, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7128702056715427}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728551106231, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728551163221, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728551163221, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7141216593941458, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7141216593941458}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552166923, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552223146, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552223147, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7146758918069978, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7146758918069978}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728553218362, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728553275684, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728553275684, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7159770885197503, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7159770885197503}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728554271310, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728554328503, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728554328503, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7166727805252052, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7166727805252052}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555323857, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555381026, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555381026, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7173972926457342, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7173972926457342}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556388293, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556444413, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556444413, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175414781407389, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7175414781407389}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557446881, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557503528, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557503528, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7179403612909735, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7179403612909735}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558506474, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558562665, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558562665, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7183986956585505, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7183986956585505}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559573313, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559631801, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559631801, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190847733311119, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7190847733311119}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728560634330, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728560690618, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728560690618, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7193026458280274, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.7193026458280274}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561686592, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3148992, "step_num": 47712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561743999, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3148992, "step_num": 47712, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561743999, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195320777644207, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3148992, "masked_lm_accuracy": 0.7195320777644207}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562740953, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3298944, "step_num": 49984}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562797084, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3298944, "step_num": 49984, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562797084, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198491232010441, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3298944, "masked_lm_accuracy": 0.7198491232010441}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563794843, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3448896, "step_num": 52256}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563851217, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3448896, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3448896, "step_num": 52256, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563851218, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7201704975009752, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3448896, "masked_lm_accuracy": 0.7201704975009752}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563851218, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3448896, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 3448896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563851218, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563851218, "event_type": "POINT_IN_TIME", "key": "seed", "value": 20151, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728563867610, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563867623, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563867623, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563867623, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563867623, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563867760, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563867761, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564959716, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564973303, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988006, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988006, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988006, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988006, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988006, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988007, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988007, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988007, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988007, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988007, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988007, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988007, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988008, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988008, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988008, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988008, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564988008, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728565046616, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728566082579, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728566146228, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728566146228, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3875764702688668, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.3875764702688668}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567148838, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567205854, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567205855, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4064989212786429, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.4064989212786429}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568204176, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568261886, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568261886, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4445486674068499, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4445486674068499}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728569260373, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728569317827, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728569317827, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5039052128362741, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5039052128362741}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570316014, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570373692, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570373692, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5860576768012982, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.5860576768012982}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571372081, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571429048, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571429048, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6553790274678981, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6553790274678981}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728572429036, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728572485646, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728572485646, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.695299510013292, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.695299510013292}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573485353, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573541985, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573541985, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7060198057319994, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7060198057319994}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574539420, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574596892, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574596892, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7096440969074137, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7096440969074137}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728575594573, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728575651826, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728575651827, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7117632173033053, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7117632173033053}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728576649028, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728576706272, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728576706273, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7133303043652096, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7133303043652096}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728577704079, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728577761356, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728577761356, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7137490666835123, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7137490666835123}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728578758647, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728578815837, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728578815837, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7155028079610137, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7155028079610137}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579812964, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728579870775, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728579870776, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7161324361018528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7161324361018528}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580866942, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728580924299, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728580924299, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7167852242763842, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7167852242763842}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728581922472, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728581979021, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728581979022, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7176919813705335, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7176919813705335}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728582983990, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728583040463, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728583040464, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7181049583197069, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7181049583197069}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728584044016, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728584101869, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728584101869, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7182932556855443, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7182932556855443}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728585118787, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728585176571, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728585176571, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189288123968338, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7189288123968338}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728586176081, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728586233843, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728586233843, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190464254475384, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.7190464254475384}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728587233422, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3148992, "step_num": 47712}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728587290110, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3148992, "step_num": 47712, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728587290111, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189439674587971, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3148992, "masked_lm_accuracy": 0.7189439674587971}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588298920, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3298944, "step_num": 49984}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588356857, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3298944, "step_num": 49984, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588356858, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195348424974429, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3298944, "masked_lm_accuracy": 0.7195348424974429}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589362222, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3448896, "step_num": 52256}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589419360, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3448896, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3448896, "step_num": 52256, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589419360, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198157412389402, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3448896, "masked_lm_accuracy": 0.7198157412389402}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590419216, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3598848, "step_num": 54528}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590477169, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3598848, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3598848, "step_num": 54528, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590477170, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7196774663650568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3598848, "masked_lm_accuracy": 0.7196774663650568}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590477170, "event_type": "POINT_IN_TIME", "key": "seed", "value": 15936, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
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|
|
@ -0,0 +1,78 @@
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|||
:::MLLOG {"namespace": "", "time_ms": 1728590796935, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590796948, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590796948, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590796949, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590796949, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590797097, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590797098, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591915361, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591929057, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943823, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943823, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943826, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591989241, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593032285, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593096400, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593096400, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38762320266130373, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38762320266130373}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594107968, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594165957, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594165958, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.41101139415576204, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.41101139415576204}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595175795, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595232491, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595232492, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4496020218106037, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4496020218106037}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596242699, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596299316, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596299317, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5736796348911599, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5736796348911599}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728597307215, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728597365230, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728597365230, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6932630223218166, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6932630223218166}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598375878, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598433374, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598433374, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7074951962503617, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.7074951962503617}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599442119, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599498818, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599498818, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7122511012366809, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7122511012366809}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600507693, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600566544, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600566545, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7142625644525941, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7142625644525941}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601572961, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601630426, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601630427, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7150282886618973, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7150282886618973}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728602641519, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728602697877, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728602697878, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7163425168378953, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7163425168378953}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728603708872, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728603765541, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728603765541, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7169951724305293, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7169951724305293}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604783436, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604840983, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604840983, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7174601919220533, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7174601919220533}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605853878, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605911397, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605911397, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718169204153268, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.718169204153268}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728606921863, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728606978402, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728606978403, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192717549253096, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7192717549253096}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728607995812, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728608053682, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728608053682, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7194449275499629, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7194449275499629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609084329, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609142724, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609142725, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200488402375792, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7200488402375792}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609142725, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2399232}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609142725, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609142725, "event_type": "POINT_IN_TIME", "key": "seed", "value": 20762, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728609159110, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609159123, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609159123, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609159124, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609159124, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609159270, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609159271, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610267217, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610281092, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295781, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295781, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295781, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295782, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295782, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295782, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295782, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295782, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295782, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295782, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295782, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295783, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295783, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295783, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295783, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295783, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610295783, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610347873, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728611397047, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728611461353, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728611461353, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38775324024121494, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38775324024121494}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728612481307, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728612538833, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728612538833, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4005705962727437, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.4005705962727437}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728613556463, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728613613480, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728613613480, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4705434759434069, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4705434759434069}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728614631311, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728614687851, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728614687851, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5429764502622013, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5429764502622013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728615706690, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728615763381, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728615763381, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6442740782693109, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6442740782693109}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728616781291, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728616838087, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728616838087, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6980952795351345, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6980952795351345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728617855821, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728617912283, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728617912283, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.707689059052413, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.707689059052413}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618929847, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618986661, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618986661, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7109381170707616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7109381170707616}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728620004644, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728620061231, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728620061231, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.712553325461712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.712553325461712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621080635, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621138171, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621138171, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.714370012497859, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.714370012497859}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622165561, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728622221929, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622221929, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7153810627029982, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7153810627029982}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728623242362, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728623299587, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728623299588, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7157506428582981, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7157506428582981}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728624319084, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728624376686, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728624376687, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716899270201845, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.716899270201845}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728625402109, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728625460126, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728625460126, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7170993249861152, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7170993249861152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728626480567, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728626539447, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728626539448, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7173656942820077, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7173656942820077}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728627566869, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728627623861, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728627623862, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7182765536917565, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7182765536917565}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728628643742, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728628701681, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728628701681, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7185246580435118, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7185246580435118}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728629728019, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728629785903, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728629785903, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7187082183954597, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7187082183954597}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630806207, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630863277, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630863277, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192723798623111, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7192723798623111}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631896497, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631954615, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631954616, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195008011299047, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.7195008011299047}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728632984222, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3148992, "step_num": 47712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633041346, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3148992, "step_num": 47712, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633041346, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195586102339202, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3148992, "masked_lm_accuracy": 0.7195586102339202}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634068369, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3298944, "step_num": 49984}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634127804, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3298944, "step_num": 49984, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634127805, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7202416826810534, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3298944, "masked_lm_accuracy": 0.7202416826810534}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634127805, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 3298944}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634127805, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634127805, "event_type": "POINT_IN_TIME", "key": "seed", "value": 219, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
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|
|
@ -0,0 +1,93 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728524965239, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524965253, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524965253, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524965253, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524965253, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524965392, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524965393, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526062933, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526077625, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092468, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092468, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092468, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092468, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092469, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092469, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092469, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092469, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092469, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092469, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092469, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092470, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092470, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092470, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092470, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092470, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526092470, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526138852, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728527173955, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728527237057, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728527237057, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3877335678748049, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.3877335678748049}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728528242671, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728528300179, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728528300179, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.40263391588716785, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.40263391588716785}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728529303573, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728529359959, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728529359959, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.42964756483436706, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.42964756483436706}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728530364689, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728530422126, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728530422126, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4922114010334873, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.4922114010334873}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728531424135, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728531481285, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728531481286, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5822947872707639, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.5822947872707639}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728532482693, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728532539641, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728532539641, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6813526992749224, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6813526992749224}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728533542012, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728533599055, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728533599056, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7051245396052854, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7051245396052854}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728534601465, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728534657337, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728534657337, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7107940144930761, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7107940144930761}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728535659894, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728535716827, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728535716827, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7130225877169728, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7130225877169728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728536719704, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728536776465, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728536776465, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7149506402573474, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7149506402573474}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728537786290, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537844483, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537844483, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7156235970418183, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7156235970418183}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538854316, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538910323, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538910323, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716550941694691, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.716550941694691}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539924500, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539982702, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539982702, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7173966967065533, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7173966967065533}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728540985244, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728541042328, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728541042329, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7179239220295971, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7179239220295971}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542045079, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542102249, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542102249, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7180614758195746, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7180614758195746}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543106186, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543163150, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543163150, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7188610007967431, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7188610007967431}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544166236, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544222285, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544222285, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190189941528677, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7190189941528677}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545224291, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545280273, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545280273, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189939534442469, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7189939534442469}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546290997, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546348255, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546348255, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7197046605641069, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7197046605641069}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728547370392, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728547427703, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728547427704, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198148297920296, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.7198148297920296}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548433236, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3148992, "step_num": 47712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548489162, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3148992, "step_num": 47712, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548489163, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7201927967892483, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3148992, "masked_lm_accuracy": 0.7201927967892483}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548489163, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 3148992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548489163, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548489163, "event_type": "POINT_IN_TIME", "key": "seed", "value": 28210, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,87 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728548506444, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548506457, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548506458, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548506458, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548506458, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548506735, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548506735, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549621641, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549635270, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652872, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652872, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652872, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652872, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652872, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652873, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652873, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652873, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652873, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652873, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652873, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652873, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652873, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728549652873, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652874, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652874, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549652874, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549705049, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550749936, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550814006, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728550814007, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3879261584913366, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.3879261584913366}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728551828200, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728551884657, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728551884657, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.40485212616242544, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.40485212616242544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552894731, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552950982, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728552950982, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.44991275376735795, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.44991275376735795}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728553961756, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728554019767, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728554019768, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5169473737210089, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5169473737210089}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555028196, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555084535, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555084536, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6089998500284207, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6089998500284207}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556093244, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556150861, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556150861, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6900064689568152, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6900064689568152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557159270, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557216649, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557216649, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7058100798706416, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7058100798706416}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558223895, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558281962, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558281962, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7103145412148726, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7103145412148726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559289692, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559346801, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559346801, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7119302401016341, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7119302401016341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728560353303, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728560410326, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728560410327, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7139668108176956, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7139668108176956}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561418512, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561474810, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561474810, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.715211286184383, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.715211286184383}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562483778, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562541169, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562541169, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7157247102968551, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7157247102968551}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563551560, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563609899, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563609899, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717246541128805, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.717246541128805}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564615944, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564673046, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564673046, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7174253223824801, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7174253223824801}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728565680391, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728565737965, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728565737965, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175353851658753, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7175353851658753}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728566749670, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728566807129, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728566807130, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7185968662280842, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7185968662280842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567827685, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567883857, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567883857, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189708248755141, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7189708248755141}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568903914, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568961416, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568961416, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191977317131559, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7191977317131559}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728569970441, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570027956, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570027956, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7201856829552287, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7201856829552287}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570027957, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2849088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570027957, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570027957, "event_type": "POINT_IN_TIME", "key": "seed", "value": 10448, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728570044042, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570044056, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570044056, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570044056, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570044056, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570044375, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570044375, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571155314, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571169188, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185077, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185077, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185077, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185077, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185077, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185078, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185078, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185078, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185078, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185078, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185078, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185078, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185079, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185079, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185079, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185079, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571185079, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571232077, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728572260932, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728572324272, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728572324272, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3880780045687258, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.3880780045687258}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573321862, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573379098, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573379099, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4047361867257629, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.4047361867257629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574372317, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574430334, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574430334, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4497806984254108, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4497806984254108}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728575423648, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728575480052, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728575480052, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5134892886673252, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5134892886673252}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728576473686, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728576529490, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728576529490, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6072650871808899, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6072650871808899}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728577521455, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728577578395, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728577578395, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6880894238246581, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6880894238246581}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728578569253, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728578627203, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728578627203, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7046831176176569, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7046831176176569}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579618293, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579675086, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579675087, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7089016024552924, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7089016024552924}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580666335, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580724803, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580724804, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7109720907171257, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7109720907171257}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728581716985, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728581773028, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728581773028, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7131220351884519, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7131220351884519}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728582764878, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728582822486, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728582822487, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7144350144916047, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7144350144916047}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728583814857, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728583872551, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728583872551, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7149028261407235, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7149028261407235}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728584862980, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728584919984, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728584919984, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7159077662798052, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7159077662798052}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728585920409, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728585976497, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728585976497, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7165625739683987, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7165625739683987}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728586977974, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728587034924, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728587034924, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716636929886743, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.716636929886743}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588046632, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588103542, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588103543, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7177939713180029, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7177939713180029}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589095819, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589153039, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589153040, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718201180382553, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.718201180382553}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590145828, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590203087, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590203087, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7187695578679255, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7187695578679255}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591195213, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591252415, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591252415, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192902017941215, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7192902017941215}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728592251653, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728592309614, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728592309614, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7194759279721928, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.7194759279721928}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593301651, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3148992, "step_num": 47712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593358684, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3148992, "step_num": 47712, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593358684, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7194070329763393, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3148992, "masked_lm_accuracy": 0.7194070329763393}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594357831, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3298944, "step_num": 49984}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594414864, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3298944, "step_num": 49984, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594414864, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200078809769052, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3298944, "masked_lm_accuracy": 0.7200078809769052}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594414864, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 3298944}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594414864, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594414864, "event_type": "POINT_IN_TIME", "key": "seed", "value": 10752, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
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|
|
@ -0,0 +1,78 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728590796935, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590796948, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590796948, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590796949, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590796949, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590797097, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590797098, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591915361, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591929057, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943823, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943823, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943824, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943825, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591943826, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591989241, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593032285, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593096400, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593096400, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38762320266130373, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38762320266130373}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594107968, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594165957, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594165958, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.41101139415576204, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.41101139415576204}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595175795, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595232491, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595232492, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4496020218106037, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4496020218106037}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596242699, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596299316, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596299317, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5736796348911599, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5736796348911599}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728597307215, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728597365230, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728597365230, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6932630223218166, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6932630223218166}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598375878, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598433374, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598433374, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7074951962503617, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.7074951962503617}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599442119, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599498818, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599498818, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7122511012366809, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7122511012366809}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600507693, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600566544, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600566545, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7142625644525941, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7142625644525941}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601572961, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601630426, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601630427, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7150282886618973, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7150282886618973}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728602641519, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728602697877, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728602697878, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7163425168378953, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7163425168378953}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728603708872, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728603765541, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728603765541, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7169951724305293, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7169951724305293}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604783436, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604840983, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604840983, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7174601919220533, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7174601919220533}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605853878, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605911397, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605911397, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718169204153268, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.718169204153268}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728606921863, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728606978402, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728606978403, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192717549253096, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7192717549253096}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728607995812, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728608053682, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728608053682, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7194449275499629, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7194449275499629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609084329, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609142724, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609142725, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200488402375792, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7200488402375792}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609142725, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2399232}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609142725, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609142725, "event_type": "POINT_IN_TIME", "key": "seed", "value": 22978, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728618801034, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618801047, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618801048, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618801048, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618801048, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618801209, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618801209, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619929327, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619943087, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957912, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957913, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957913, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957913, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957913, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957913, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957913, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957913, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957914, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957914, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957914, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957914, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957914, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728619957914, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957914, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957914, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619957915, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728620007681, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621045859, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621108773, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621108773, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3865617481387155, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.3865617481387155}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622114605, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622171150, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622171151, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.40262117073717557, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.40262117073717557}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728623174052, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728623230873, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728623230873, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4523024549724531, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4523024549724531}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728624233281, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728624289615, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728624289616, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5221301693185476, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5221301693185476}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728625291673, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728625349229, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728625349230, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6420224388345102, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6420224388345102}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728626351043, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728626408667, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728626408667, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.699528387405233, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.699528387405233}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728627410463, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728627466811, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728627466811, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7079550924241078, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7079550924241078}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728628468561, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728628525964, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728628525965, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7118257526945195, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7118257526945195}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728629528193, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728629584413, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728629584413, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7130748185842568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7130748185842568}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630587761, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630645519, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630645520, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.714902451421661, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.714902451421661}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631646942, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631704444, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631704445, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7154911232337883, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7154911232337883}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728632715855, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728632772059, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728632772059, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7161726189646523, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7161726189646523}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633776195, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633832753, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633832753, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7168811186745844, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7168811186745844}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634841112, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634897563, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634897563, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7172674153762159, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7172674153762159}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728635899496, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728635956963, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728635956963, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717885359040119, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.717885359040119}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728636964772, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728637021152, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728637021152, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718571519987556, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.718571519987556}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728638039702, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728638096247, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728638096247, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190522497187993, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7190522497187993}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728639099786, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728639156384, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728639156384, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190969545610951, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7190969545610951}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728640172070, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728640229709, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728640229709, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7197579530877272, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7197579530877272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728641233638, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728641290427, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728641290427, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200084952587272, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.7200084952587272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728641290427, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2999040}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728641290428, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728641290428, "event_type": "POINT_IN_TIME", "key": "seed", "value": 9634, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,93 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728516945293, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728516945306, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728516945306, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728516945307, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728516945307, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728516945490, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728516945491, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518502722, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518514008, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528257, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528257, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528258, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528258, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528258, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528258, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528258, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528258, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528258, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528259, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528259, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528259, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528259, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528259, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528259, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528259, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518528259, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728518578008, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728519789747, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728519848812, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728519848813, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38834677969150794, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38834677969150794}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728521004546, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728521057409, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728521057409, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.40273311821800833, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.40273311821800833}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728522209891, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728522262867, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728522262867, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.44312383646584586, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.44312383646584586}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728523414970, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728523467811, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728523467812, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5062930049407103, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5062930049407103}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524621021, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524672880, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524672880, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5888718058230566, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.5888718058230566}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728525825012, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728525878025, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728525878025, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6833749091689574, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6833749091689574}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728527029872, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728527083655, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728527083655, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7040974767750154, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7040974767750154}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728528235285, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728528288182, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728528288183, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7093540651038799, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7093540651038799}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728529439469, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728529492117, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728529492117, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7120359153824791, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7120359153824791}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728530643295, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728530695998, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728530695999, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7137061499448043, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7137061499448043}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728531854130, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728531905972, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728531905973, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7148870925168185, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7148870925168185}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728533058059, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728533110923, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728533110923, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7155579556657943, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7155579556657943}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728534262752, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728534314582, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728534314582, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716432806969261, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.716432806969261}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728535466733, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728535518647, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728535518647, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7171081434700685, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7171081434700685}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728536674344, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728536727210, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728536727211, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175339546901564, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7175339546901564}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537884762, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537937482, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537937482, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718394822250531, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.718394822250531}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539088991, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539141647, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539141648, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7188054349298025, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7188054349298025}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728540309614, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728540362635, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728540362635, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7188799817474859, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7188799817474859}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728541513809, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728541567687, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728541567687, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195802817962523, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7195802817962523}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542725822, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542778626, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542778627, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.719959929272595, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.719959929272595}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543937336, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3148992, "step_num": 47712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543990349, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3148992, "step_num": 47712, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543990349, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200285581392518, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3148992, "masked_lm_accuracy": 0.7200285581392518}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543990349, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 3148992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543990349, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543990349, "event_type": "POINT_IN_TIME", "key": "seed", "value": 21254, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,99 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728544003831, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544003844, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544003844, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544003844, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544003845, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544004114, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544004115, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545567231, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545577478, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591839, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591839, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591839, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591839, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591840, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591840, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591840, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591840, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591840, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591840, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591840, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591840, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591841, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591841, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591841, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591841, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545591841, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728545644357, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546841240, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546898744, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546898744, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38783227444481694, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38783227444481694}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548039569, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548092787, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548092788, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4103626927252508, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.4103626927252508}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549229970, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549283151, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549283152, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.45124479976326815, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.45124479976326815}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550420161, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550473622, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550473623, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5112407873616508, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5112407873616508}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728551611312, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728551664679, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728551664679, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6004241028086611, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6004241028086611}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552801410, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552853292, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552853292, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6789556939443143, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6789556939443143}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728553989456, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728554042458, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728554042458, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7036405706305524, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7036405706305524}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555178691, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555231909, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555231909, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7080815071178612, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7080815071178612}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556368078, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556420173, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556420173, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7108210982072117, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7108210982072117}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557556063, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557608016, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557608016, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7125928249628013, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7125928249628013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558744467, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558796348, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558796348, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7135176017174266, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7135176017174266}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559932625, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559984418, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559984418, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7145863458576786, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7145863458576786}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561120215, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561173235, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561173236, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7155747616727265, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7155747616727265}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562309022, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562360931, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562360931, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7163686522768155, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7163686522768155}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563501949, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728563554781, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563554781, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716544431773359, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.716544431773359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564690440, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728564743201, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728564743201, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175336914214104, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7175336914214104}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728565883246, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728565936169, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728565936169, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7186442701202992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7186442701202992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567078310, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567130373, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567130374, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7184390328784295, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7184390328784295}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568270051, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568322854, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568322854, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7190133258834455, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7190133258834455}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728569476727, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728569528906, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728569528906, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7193232258423117, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.7193232258423117}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570672300, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3148992, "step_num": 47712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570725375, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3148992, "step_num": 47712, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570725375, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7193006322136452, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3148992, "masked_lm_accuracy": 0.7193006322136452}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571862039, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3298944, "step_num": 49984}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571914059, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3298944, "step_num": 49984, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571914060, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.71975367935961, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3298944, "masked_lm_accuracy": 0.71975367935961}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573050479, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3448896, "step_num": 52256}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573103486, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3448896, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3448896, "step_num": 52256, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573103487, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200944615325745, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3448896, "masked_lm_accuracy": 0.7200944615325745}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573103487, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3448896, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 3448896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573103487, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573103487, "event_type": "POINT_IN_TIME", "key": "seed", "value": 31023, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
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|
|
@ -0,0 +1,81 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728573115586, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573115599, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573115600, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573115600, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573115600, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573115806, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573115807, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574701526, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574711971, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726511, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726512, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726512, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726512, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726512, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726512, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726512, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726512, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726513, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726513, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726513, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726513, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726513, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726513, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726513, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726513, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574726514, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574767832, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728575974065, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728576033823, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728576033823, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3871400942148816, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.3871400942148816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728577185498, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728577237616, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728577237616, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.39147048244665106, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.39147048244665106}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728578385099, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728578438339, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728578438339, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.45783117753199354, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.45783117753199354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579586799, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579639018, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728579639018, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5536425763000323, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5536425763000323}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580787082, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580840252, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728580840252, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6808095073800067, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6808095073800067}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728581987844, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728582039916, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728582039917, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.70571623852529, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.70571623852529}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728583186452, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728583239391, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728583239392, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7116154495584228, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7116154495584228}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728584385675, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728584438607, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728584438607, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7138893727993064, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7138893727993064}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728585586366, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728585638448, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728585638449, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7147087185198344, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7147087185198344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728586785128, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728586837097, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728586837097, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7161258058628066, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7161258058628066}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728587983727, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588035691, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588035692, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175371565358254, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7175371565358254}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589188060, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589241204, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589241204, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.71796901310427, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.71796901310427}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590398050, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590450110, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590450111, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7184663905522461, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7184663905522461}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591601749, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591654909, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591654909, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191987109527519, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7191987109527519}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728592801533, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728592853668, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728592853668, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7193377415935079, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7193377415935079}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594003086, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594056207, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594056207, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198944457577029, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7198944457577029}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595208245, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595261648, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595261648, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7206625282657168, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7206625282657168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595261648, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2549184}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595261648, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595261648, "event_type": "POINT_IN_TIME", "key": "seed", "value": 6117, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728595273658, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595273672, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595273672, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595273672, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595273672, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595273864, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595273865, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596851159, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596861539, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876123, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876124, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876124, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876124, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876124, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876124, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876124, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876125, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876125, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876125, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876125, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876125, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876125, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876125, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876126, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876126, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596876126, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596921065, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598131675, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598191093, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598191093, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38629304813852405, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38629304813852405}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599345629, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599399811, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599399812, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4057833703070539, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.4057833703070539}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600550664, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600603826, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600603826, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4394682460404758, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4394682460404758}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601755405, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601807545, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601807545, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5091635388711576, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5091635388711576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728602959507, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728603011549, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728603011550, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6130874210764136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6130874210764136}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604163051, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604215106, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604215107, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6886828500994252, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6886828500994252}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605365869, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605418812, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605418812, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7043221683007339, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7043221683007339}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728606570227, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728606622252, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728606622252, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7098102495208356, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7098102495208356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728607773165, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728607826339, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728607826339, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7117401148361865, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7117401148361865}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728608976638, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609028698, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609028698, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7135091588821822, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7135091588821822}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610184971, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610238021, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610238021, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.714804682570013, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.714804682570013}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728611388386, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728611441326, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728611441326, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7155881319921319, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7155881319921319}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728612596531, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728612648484, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728612648485, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7166956780410199, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7166956780410199}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728613799458, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728613851376, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728613851376, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175573623673818, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7175573623673818}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728615017853, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728615070888, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728615070888, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7177324852295528, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7177324852295528}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728616224796, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728616276794, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728616276794, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7187149448529216, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7187149448529216}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728617427166, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728617480097, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728617480098, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189440351084598, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7189440351084598}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618630518, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618683523, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728618683524, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189651959015355, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7189651959015355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619834438, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619886484, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619886484, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7197905807489396, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7197905807489396}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621047498, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621100440, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621100440, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7202326569216796, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.7202326569216796}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621100441, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2999040}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621100441, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621100441, "event_type": "POINT_IN_TIME", "key": "seed", "value": 18962, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728621113036, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621113050, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621113050, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621113050, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621113050, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621113364, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728621113364, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622708323, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622718838, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734010, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734010, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734010, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734011, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734011, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734011, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734011, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734011, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734011, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734012, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734012, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734012, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734012, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734012, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734012, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734012, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622734013, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728622779756, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728623989242, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728624047931, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728624047931, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38761837577562386, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38761837577562386}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728625199877, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728625251862, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728625251862, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3991255365093096, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.3991255365093096}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728626401253, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728626454238, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728626454239, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4629036359752662, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4629036359752662}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728627603380, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728627655201, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728627655201, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5288661295558138, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5288661295558138}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728628804682, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728628857572, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728628857573, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6336066501184932, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6336066501184932}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630005680, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630057532, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630057532, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.698150883303049, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.698150883303049}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631206492, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631258444, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631258444, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7087594178456637, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7087594178456637}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728632406126, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728632458945, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728632458946, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.712074551420721, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.712074551420721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633607305, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633660325, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633660325, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7133723366167564, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7133723366167564}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634807043, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634859650, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634859650, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7153185129380183, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7153185129380183}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728636007670, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728636060454, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728636060454, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7153358636105497, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7153358636105497}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728637212672, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728637266430, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728637266431, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7166292644147753, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7166292644147753}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728638413489, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728638466302, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728638466303, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.717635804439778, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.717635804439778}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728639621382, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728639673232, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728639673232, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7184983831576123, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7184983831576123}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728640830900, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728640882936, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728640882936, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7185333441243461, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7185333441243461}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728642038106, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728642090127, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728642090127, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.71883562362759, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.71883562362759}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728643238601, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728643291575, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728643291575, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7195813601862262, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7195813601862262}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728644439777, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728644491633, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728644491633, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7200058278167899, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7200058278167899}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728644491633, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2699136}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728644491633, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728644491633, "event_type": "POINT_IN_TIME", "key": "seed", "value": 7451, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728524927811, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524927825, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524927825, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524927825, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524927825, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524927826, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728524927826, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526542156, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526553341, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570472, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570473, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570473, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570473, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570473, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570473, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570473, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570474, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570474, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570474, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570474, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570474, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570474, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570474, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570474, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570475, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526570475, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728526620720, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728527836136, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728527898691, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728527898691, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3863042194326981, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.3863042194326981}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728529058617, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728529115079, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728529115080, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.40961273735915393, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.40961273735915393}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728530272495, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728530328049, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728530328049, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.43488811103970115, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.43488811103970115}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728531485648, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728531541201, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728531541201, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4996365338081218, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.4996365338081218}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728532697882, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728532753299, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728532753299, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5947849176998402, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.5947849176998402}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728533910552, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728533966102, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728533966102, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.690821249612306, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.690821249612306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728535121207, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728535177526, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728535177526, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7054878836201564, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7054878836201564}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728536332776, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728536389219, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728536389220, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7097416914145819, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7097416914145819}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537544044, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537599323, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728537599324, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7116119135763378, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7116119135763378}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538754453, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538809839, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728538809839, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7137712529124653, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7137712529124653}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728539966085, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728540021340, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728540021340, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7149044781941172, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7149044781941172}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728541177404, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728541233606, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728541233606, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7153092085349753, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7153092085349753}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542397120, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542452484, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728542452485, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.716667587340915, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.716667587340915}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543613320, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543669699, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728543669699, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7173634540126506, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7173634540126506}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544826341, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544881826, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728544881826, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7179233225148527, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7179233225148527}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546038641, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546093965, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728546093965, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7184311815844229, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7184311815844229}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728547260318, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728547316564, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728547316565, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191096605050328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7191096605050328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548476862, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548532277, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728548532277, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192467427854418, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7192467427854418}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549694358, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549750718, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728549750718, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7199938913341333, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7199938913341333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550912201, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550968747, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550968748, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7203293324374981, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.7203293324374981}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550968748, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2999040}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550968748, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550968748, "event_type": "POINT_IN_TIME", "key": "seed", "value": 31643, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728550982004, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550982018, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550982018, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550982018, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550982018, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550982200, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728550982201, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552594185, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552604676, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619008, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619008, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619008, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619009, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619009, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619009, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619009, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619009, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619009, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619009, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619010, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619010, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619010, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619011, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619011, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619011, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552619011, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728552666986, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728553875969, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728553939951, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728553939951, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38616175677830683, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38616175677830683}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555092364, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555150488, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728555150489, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.41162710727584095, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.41162710727584095}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556296541, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556355574, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728556355575, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4350904176781545, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4350904176781545}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557502466, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557561747, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728557561747, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.496310165579904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.496310165579904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558708887, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558766886, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728558766887, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5802412135699729, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.5802412135699729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559914092, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559973260, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728559973260, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6652080704154694, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6652080704154694}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561120077, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561179197, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728561179198, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6984256211339748, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.6984256211339748}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562325103, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562383110, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728562383111, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7061839626088569, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7061839626088569}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563529512, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563588342, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728563588343, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.709852535744663, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.709852535744663}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564732742, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564791651, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728564791651, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7124425528931918, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7124425528931918}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728565936303, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728565995377, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728565995377, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7139212561175718, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7139212561175718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567148853, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567207007, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728567207007, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7147917833311085, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7147917833311085}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568352291, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568410261, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728568410261, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7158374547529306, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7158374547529306}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728569560993, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728569619018, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728569619018, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7164395152271045, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7164395152271045}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570770470, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570828476, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728570828476, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7171282240496328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7171282240496328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728571971989, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728572031038, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728572031039, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7176245095920047, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7176245095920047}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573181122, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573239069, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728573239069, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7185501086189852, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7185501086189852}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574384118, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574443334, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728574443334, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.718467390029532, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.718467390029532}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728575608940, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728575667042, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728575667042, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7193740987105504, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7193740987105504}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728576812654, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728576871686, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728576871686, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.719547293968521, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.719547293968521}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728578016561, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3148992, "step_num": 47712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728578075504, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3148992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3148992, "step_num": 47712, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728578075504, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198685481652716, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3148992, "masked_lm_accuracy": 0.7198685481652716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579221384, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 3298944, "step_num": 49984}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579280495, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 3298944, "step_num": 49984, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579280495, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7202844063393284, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 3298944, "masked_lm_accuracy": 0.7202844063393284}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579280496, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 3298944, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 3298944}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579280496, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579280496, "event_type": "POINT_IN_TIME", "key": "seed", "value": 26715, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
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|
|
@ -0,0 +1,87 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728579292347, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579292360, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579292360, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579292360, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579292360, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579292510, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728579292511, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580879014, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580889187, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903804, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903804, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903805, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903805, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903805, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903805, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903805, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903805, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903805, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903806, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903806, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903806, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903806, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903806, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903806, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903806, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580903806, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728580951866, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728582174949, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728582235954, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728582235955, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38839849547204247, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38839849547204247}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728583404398, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728583457118, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728583457118, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4043779520660943, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.4043779520660943}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728584623300, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728584677358, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728584677358, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4277538998833515, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4277538998833515}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728585841710, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728585894585, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728585894586, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4895906483404781, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.4895906483404781}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728587058762, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728587112509, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728587112509, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5868821954505488, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.5868821954505488}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588276137, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588329834, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728588329834, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6855756070131875, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6855756070131875}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589494361, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589548276, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728589548276, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7039273525328428, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7039273525328428}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590710884, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590765567, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728590765567, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7093820230075536, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7093820230075536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591929060, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591981842, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728591981842, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7118489585669369, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7118489585669369}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593146668, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593199377, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728593199377, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7138080239009914, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7138080239009914}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594362906, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594416691, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728594416692, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7149828011287352, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7149828011287352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595585602, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595639450, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728595639450, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7161310530023511, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7161310530023511}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596808968, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596861749, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728596861749, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7167342547606621, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7167342547606621}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598025777, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598079566, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728598079567, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175075857669347, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7175075857669347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599242962, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599297649, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728599297649, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7177893112502416, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7177893112502416}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600461272, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600514019, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728600514019, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7186980957604484, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7186980957604484}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601688118, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601741703, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728601741703, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7194635579882086, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7194635579882086}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728602912396, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728602965140, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728602965140, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7196329377980453, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7196329377980453}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604140305, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604193177, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604193177, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7201142786646146, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7201142786646146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604193178, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2849088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604193178, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604193178, "event_type": "POINT_IN_TIME", "key": "seed", "value": 24659, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728604206238, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604206251, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604206251, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604206251, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604206251, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604206399, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728604206400, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605831206, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605841697, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856169, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856169, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856170, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856170, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856170, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856170, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856170, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856170, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856170, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856171, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856171, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856171, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856171, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856171, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856171, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856171, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605856171, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728605907083, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728607116687, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728607176173, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728607176173, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38733550816792245, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38733550816792245}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728608331645, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728608385445, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728608385445, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4099236127138853, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.4099236127138853}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609536865, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609589476, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728609589476, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.46445941542463526, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.46445941542463526}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610742733, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610796400, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728610796401, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5356849022231992, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.5356849022231992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728611950463, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728612003989, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728612003989, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6243599510936588, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.6243599510936588}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728613157626, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728613211058, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728613211058, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6915194793740074, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.6915194793740074}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728614363023, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728614416397, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728614416397, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.70565997331387, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.70565997331387}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728615568690, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728615622127, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728615622128, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7094783287385873, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7094783287385873}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728616773489, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728616825919, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728616825919, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7119873171805191, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7119873171805191}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728617978563, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728618032221, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728618032222, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7142333673658525, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7142333673658525}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728619183815, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728619236305, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728619236305, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7149768588638763, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7149768588638763}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728620386747, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728620439998, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728620439998, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7156523679928931, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7156523679928931}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728621597193, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728621650622, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728621650622, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7164913090174971, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7164913090174971}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728622809144, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728622861570, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728622861570, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7172874884828523, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7172874884828523}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728624015271, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728624067817, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728624067818, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7180004538213985, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7180004538213985}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728625221988, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728625275277, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
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:::MLLOG {"namespace": "", "time_ms": 1728625275277, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7182580689577264, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7182580689577264}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728626428374, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2549184, "step_num": 38624}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728626481696, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2549184, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2549184, "step_num": 38624, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728626481696, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7189949285361891, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2549184, "masked_lm_accuracy": 0.7189949285361891}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728627644056, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2699136, "step_num": 40896}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728627696455, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2699136, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2699136, "step_num": 40896, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728627696456, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7192161206220823, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2699136, "masked_lm_accuracy": 0.7192161206220823}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728628864498, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2849088, "step_num": 43168}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728628917907, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2849088, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2849088, "step_num": 43168, "samples_count": 10002}}
|
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:::MLLOG {"namespace": "", "time_ms": 1728628917908, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7198127043697744, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2849088, "masked_lm_accuracy": 0.7198127043697744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630073687, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2999040, "step_num": 45440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630126070, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2999040, "step_num": 45440, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630126070, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7204812415431342, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2999040, "masked_lm_accuracy": 0.7204812415431342}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630126070, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2999040, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2999040}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630126071, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630126071, "event_type": "POINT_IN_TIME", "key": "seed", "value": 6018, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1728630138587, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630138600, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_red", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630138600, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630138601, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630138601, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630138750, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728630138750, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631762258, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631772243, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787279, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787280, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787280, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787280, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787280, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787280, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787280, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787281, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787281, "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": 720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787281, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787281, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787281, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787281, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787281, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787282, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787282, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631787282, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728631833698, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633040032, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633099704, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728633099705, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38797098084452913, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38797098084452913}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634252081, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634305117, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728634305117, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.3995994811617263, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.3995994811617263}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728635455137, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728635508121, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728635508121, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.5011176564375941, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.5011176564375941}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728636656616, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 599808, "step_num": 9088}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728636710601, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 599808, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 599808, "step_num": 9088, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728636710602, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.6374410459147146, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 599808, "masked_lm_accuracy": 0.6374410459147146}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728637859253, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 749760, "step_num": 11360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728637913116, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 749760, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 749760, "step_num": 11360, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728637913117, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7006531824018688, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 749760, "masked_lm_accuracy": 0.7006531824018688}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728639062400, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 899712, "step_num": 13632}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728639116398, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 899712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 899712, "step_num": 13632, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728639116398, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7092244053358937, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 899712, "masked_lm_accuracy": 0.7092244053358937}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728640264599, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1049664, "step_num": 15904}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728640317583, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1049664, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1049664, "step_num": 15904, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728640317584, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7131378053546167, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1049664, "masked_lm_accuracy": 0.7131378053546167}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728641465346, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1199616, "step_num": 18176}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728641518225, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1199616, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1199616, "step_num": 18176, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728641518225, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7143681931080901, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1199616, "masked_lm_accuracy": 0.7143681931080901}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728642666024, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1349568, "step_num": 20448}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728642718905, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1349568, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1349568, "step_num": 20448, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728642718905, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7157575550305322, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1349568, "masked_lm_accuracy": 0.7157575550305322}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728643864313, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1499520, "step_num": 22720}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728643917070, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1499520, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1499520, "step_num": 22720, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728643917070, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7172654897564532, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1499520, "masked_lm_accuracy": 0.7172654897564532}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728645063069, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1649472, "step_num": 24992}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728645115906, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1649472, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1649472, "step_num": 24992, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728645115907, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7175817649094158, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1649472, "masked_lm_accuracy": 0.7175817649094158}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728646261029, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728646314945, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728646314946, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7180203909851078, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7180203909851078}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728647468431, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728647522632, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728647522633, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191875515115712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7191875515115712}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728648686121, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728648739069, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728648739069, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7196067409309428, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7196067409309428}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728649885357, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728649939244, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728649939245, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7197489482454004, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7197489482454004}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728651085252, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728651139304, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728651139304, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7202936197442785, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7202936197442785}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728651139305, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2399232}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728651139305, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1728651139305, "event_type": "POINT_IN_TIME", "key": "seed", "value": 32021, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
{
|
||||
"submitter": "tinycorp",
|
||||
"division": "closed",
|
||||
"status": "available",
|
||||
"status": "Available on-premise",
|
||||
"system_name": "tinybox green",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "1",
|
||||
|
|
@ -28,7 +28,7 @@
|
|||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, commit 0e8aa0e2886bf9a2d3ce093bce87305e182e6d4a",
|
||||
"framework": "tinygrad, commit b5546912e24e0a864b35924da4efa5d71cfe368b",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.12",
|
||||
"CUDA": "12.4"
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
{
|
||||
"submitter": "tinycorp",
|
||||
"division": "closed",
|
||||
"status": "available",
|
||||
"status": "Available on-premise",
|
||||
"system_name": "tinybox red",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "1",
|
||||
|
|
@ -28,10 +28,10 @@
|
|||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, commit 0e8aa0e2886bf9a2d3ce093bce87305e182e6d4a",
|
||||
"framework": "tinygrad, commit b5546912e24e0a864b35924da4efa5d71cfe368b",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.12",
|
||||
"ROCm": "6.1"
|
||||
"ROCm": "6.1.3"
|
||||
},
|
||||
"operating_system": "Ubuntu 22.04.4",
|
||||
"sw_notes": ""
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue