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mlperf_tra
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52 changed files with 1120 additions and 1108 deletions
1
examples/mlperf/training_submission_v6.0/.gitignore
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examples/mlperf/training_submission_v6.0/.gitignore
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!*.txt
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examples/mlperf/training_submission_v6.0/submission.zip
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examples/mlperf/training_submission_v6.0/submission.zip
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@ -1,17 +0,0 @@
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#!/bin/bash
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||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=1 BS=128 EVAL_BS=128
|
||||
|
||||
export CHECK_OOB=0
|
||||
|
||||
export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
# export BEAM_LOG_SURPASS_MAX=1
|
||||
# export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export RESET_STEP=1
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||||
export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
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||||
|
||||
python3 examples/mlperf/model_train.py
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|
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@ -1,69 +0,0 @@
|
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# 1. Problem
|
||||
|
||||
This problem uses BERT for NLP.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
Also install gdown (for dataset), numpy, tqdm and tensorflow.
|
||||
```
|
||||
pip install gdown numpy tqdm tensorflow
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
### 1. Download raw data
|
||||
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
|
||||
```
|
||||
|
||||
### 2. Preprocess train and validation data
|
||||
|
||||
Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended.
|
||||
|
||||
#### Training:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
|
||||
```
|
||||
|
||||
Generating a specific topic (Between 0 and 499)
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
|
||||
```
|
||||
|
||||
#### Validation:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
|
||||
```
|
||||
## Running
|
||||
|
||||
### tinybox_green
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
|
||||
### tinybox_red
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
### tinybox_8xMI300X
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
|
||||
```
|
||||
|
|
@ -1,17 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
|
||||
export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=500000
|
||||
|
||||
export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export BENCHMARK=10 BERT_LAYERS=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,20 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
|
||||
|
||||
# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
|
||||
export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
|
||||
export TRAIN_STEPS=3900
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=500000
|
||||
|
||||
export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
RUNMLPERF=1 python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="bert"
|
||||
export SUBMISSION_PLATFORM="tinybox_8xMI300X"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
|
||||
|
||||
# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
|
||||
export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
|
||||
export TRAIN_STEPS=3900
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=500000
|
||||
|
||||
export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="bert_8xMI300x_${DATETIME}_${SEED}.log"
|
||||
|
||||
BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
|
|
@ -1,20 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=AMD DEBUG=0 JIT=1 FLASH_ATTENTION=1
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
|
||||
|
||||
# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
|
||||
export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
|
||||
export TRAIN_STEPS=3900
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=5000000
|
||||
|
||||
export BEAM=0 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
RUNMLPERF=1 python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
|
||||
|
||||
# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
|
||||
export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
|
||||
export TRAIN_STEPS=3900
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=5000000
|
||||
|
||||
export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
export BEAM_TIMEOUT_SEC=15
|
||||
export FP8_TRAIN=1
|
||||
# search
|
||||
IGNORE_BEAM_CACHE=1 BENCHMARK=10 BERT_LAYERS=2 RUNMLPERF=0 python3 examples/mlperf/model_train.py
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
RUNMLPERF=1 python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="bert"
|
||||
export SUBMISSION_PLATFORM="tinybox_8xMI350X"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
|
||||
|
||||
# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
|
||||
export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
|
||||
export TRAIN_STEPS=3900
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=5000000
|
||||
|
||||
export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="bert_8xMI350x_${DATETIME}_${SEED}.log"
|
||||
|
||||
BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
|
|
@ -1,69 +0,0 @@
|
|||
# 1. Problem
|
||||
|
||||
This problem uses BERT for NLP.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
Also install gdown (for dataset), numpy, tqdm and tensorflow.
|
||||
```
|
||||
pip install gdown numpy tqdm tensorflow
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
### 1. Download raw data
|
||||
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
|
||||
```
|
||||
|
||||
### 2. Preprocess train and validation data
|
||||
|
||||
Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended.
|
||||
|
||||
#### Training:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
|
||||
```
|
||||
|
||||
Generating a specific topic (Between 0 and 499)
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
|
||||
```
|
||||
|
||||
#### Validation:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
|
||||
```
|
||||
## Running
|
||||
|
||||
### tinybox_green
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
|
||||
### tinybox_red
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
### tinybox_8xMI300X
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
|
||||
```
|
||||
|
|
@ -1,17 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=NV
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=72 EVAL_BS=72
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=500000
|
||||
|
||||
export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BEAM_LOG_SURPASS_MAX=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=NV
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=72 EVAL_BS=72
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=500000
|
||||
|
||||
export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
RUNMLPERF=1 python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,28 +0,0 @@
|
|||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." DEV=NV
|
||||
export MODEL="bert"
|
||||
export SUBMISSION_PLATFORM="tinybox_green"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=72 EVAL_BS=72
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=500000
|
||||
|
||||
export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="bert_green_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
|
|
@ -1,69 +0,0 @@
|
|||
# 1. Problem
|
||||
|
||||
This problem uses BERT for NLP.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
Also install gdown (for dataset), numpy, tqdm and tensorflow.
|
||||
```
|
||||
pip install gdown numpy tqdm tensorflow
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
### 1. Download raw data
|
||||
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
|
||||
```
|
||||
|
||||
### 2. Preprocess train and validation data
|
||||
|
||||
Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended.
|
||||
|
||||
#### Training:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
|
||||
```
|
||||
|
||||
Generating a specific topic (Between 0 and 499)
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
|
||||
```
|
||||
|
||||
#### Validation:
|
||||
```
|
||||
BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
|
||||
```
|
||||
## Running
|
||||
|
||||
### tinybox_green
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
|
||||
### tinybox_red
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
### tinybox_8xMI300X
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
|
||||
```
|
||||
|
|
@ -1,18 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=500000
|
||||
|
||||
export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BEAM_LOG_SURPASS_MAX=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export RESET_STEP=1
|
||||
export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="bert"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=500000
|
||||
|
||||
export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
RUNMLPERF=1 python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="bert"
|
||||
export SUBMISSION_PLATFORM="tinybox_red"
|
||||
export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=500000
|
||||
|
||||
export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/wiki"
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=${LOGMLPERF:-1}
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="bert_red_${DATETIME}_${SEED}.log"
|
||||
|
||||
export HCQDEV_WAIT_TIMEOUT_MS=100000 # prevents hang?
|
||||
|
||||
# init
|
||||
sleep 5 && sudo rmmod amdgpu || true
|
||||
BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
|
|
@ -1,45 +0,0 @@
|
|||
#!/usr/bin/env bash
|
||||
|
||||
export PYTHONPATH="."
|
||||
export DEV=${DEV:-AMD}
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
|
||||
export DEVICE_IN_FUNCTION_BUG=1
|
||||
|
||||
export DEBUG=${DEBUG:-2}
|
||||
export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
|
||||
export ALL2ALL=${ALL2ALL:-1}
|
||||
export USE_ATOMICS=${USE_ATOMICS:-1}
|
||||
export ASM_GEMM=${ASM_GEMM:-1}
|
||||
export WQKV=${WQKV:-1}
|
||||
export MASTER_WEIGHTS=${MASTER_WEIGHTS:-1}
|
||||
export FP8=${FP8:-1}
|
||||
export ALLREDUCE_CAST=${ALLREDUCE_CAST:-1}
|
||||
export FAST_CE=${FAST_CE:-0}
|
||||
export FUSED_INPUT_QUANTIZE=${FUSED_INPUT_QUANTIZE:-1}
|
||||
export FUSED_ADD_NORM_MUL_QUANTIZE=${FUSED_ADD_NORM_MUL_QUANTIZE:-1}
|
||||
export FUSED_SILU_W13=${FUSED_SILU_W13:-1}
|
||||
export FUSED_PAD_GRAD_ACCUM=${FUSED_PAD_GRAD_ACCUM:-1}
|
||||
export OFFLOAD_OPTIM=${OFFLOAD_OPTIM:-1}
|
||||
|
||||
export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
|
||||
export DP=${DP:-1} MP=${MP:-8} BS=${BS:-1} EVAL_BS=${EVAL_BS:-1} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-2}
|
||||
export GBS=$((BS * GRADIENT_ACC_STEPS))
|
||||
|
||||
export MODEL="llama3"
|
||||
export BASEDIR="/raid/datasets/c4/"
|
||||
export LLAMA3_SIZE=${LLAMA3_SIZE:-"405B"}
|
||||
export SEQLEN=${SEQLEN:-8192}
|
||||
|
||||
export SEED=${SEED:-5760}
|
||||
export DATA_SEED=${DATA_SEED:-5760}
|
||||
|
||||
export JITBEAM=${JITBEAM:-3}
|
||||
export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=1
|
||||
|
||||
export FAKEDATA=${FAKEDATA:-1} BENCHMARK=${BENCHMARK:-10}
|
||||
if [ -z "$FULL_LAYERS" ]; then
|
||||
export LLAMA_LAYERS=2
|
||||
fi
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,32 +0,0 @@
|
|||
#!/usr/bin/env bash
|
||||
|
||||
export PYTHONPATH="."
|
||||
export DEV=${DEV:-AMD}
|
||||
export EMULATE="AMD_CDNA4"
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
|
||||
|
||||
export DEBUG=${DEBUG:-0}
|
||||
export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
|
||||
export ALL2ALL=${ALL2ALL:-1}
|
||||
export USE_ATOMICS=${USE_ATOMICS:-0}
|
||||
export ASM_GEMM=${ASM_GEMM:-1}
|
||||
export WQKV=${WQKV:-1}
|
||||
export OFFLOAD_OPTIM=${OFFLOAD_OPTIM:-1}
|
||||
|
||||
export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
|
||||
export DP=${DP:-1} MP=${MP:-8}
|
||||
export BS=${BS:-1} EVAL_BS=${EVAL_BS:-1} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-1152}
|
||||
|
||||
export MODEL="llama3"
|
||||
export BASEDIR="/raid/datasets/c4/"
|
||||
export LLAMA3_SIZE=${LLAMA3_SIZE:-"405B"}
|
||||
export SEQLEN=${SEQLEN:-8192}
|
||||
|
||||
export SEED=${SEED:-$RANDOM}
|
||||
export DATA_SEED=${DATA_SEED:-5760}
|
||||
|
||||
export JITBEAM=${JITBEAM:-3}
|
||||
export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=1
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
# 1. Problem
|
||||
|
||||
small llm pretraining: llama 3.1 8b on c4.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v6.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
### 1. Download raw data
|
||||
|
||||
follow mlperf steps to download the preprocessed c4 dataset.
|
||||
|
||||
## Running
|
||||
|
||||
### tinybox_8xMI350X
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/run_and_time.sh
|
||||
```
|
||||
|
|
@ -1,50 +0,0 @@
|
|||
#!/usr/bin/env bash
|
||||
|
||||
export PYTHONPATH="."
|
||||
export DEV=${DEV:-AMD}
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
|
||||
export DEVICE_IN_FUNCTION_BUG=1
|
||||
|
||||
export DEBUG=${DEBUG:-2}
|
||||
export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
|
||||
export ALL2ALL=${ALL2ALL:-1}
|
||||
export USE_ATOMICS=${USE_ATOMICS:-1}
|
||||
export ASM_GEMM=${ASM_GEMM:-1}
|
||||
export WQKV=${WQKV:-1}
|
||||
export MASTER_WEIGHTS=${MASTER_WEIGHTS:-1}
|
||||
export FP8=${FP8:-1}
|
||||
export ALLREDUCE_CAST=${ALLREDUCE_CAST:-1}
|
||||
export FAST_CE=${FAST_CE:-0}
|
||||
export FUSED_INPUT_QUANTIZE=${FUSED_INPUT_QUANTIZE:-1}
|
||||
export FUSED_ADD_NORM_MUL_QUANTIZE=${FUSED_ADD_NORM_MUL_QUANTIZE:-1}
|
||||
export FUSED_SILU_W13=${FUSED_SILU_W13:-1}
|
||||
export FUSED_PAD_GRAD_ACCUM=${FUSED_PAD_GRAD_ACCUM:-1}
|
||||
export OFFLOAD_OPTIM=${OFFLOAD_OPTIM:-1}
|
||||
|
||||
export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
|
||||
export DP=${DP:-1} MP=${MP:-8} BS=${BS:-1} EVAL_BS=${EVAL_BS:-1} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-2}
|
||||
export GBS=$((BS * GRADIENT_ACC_STEPS))
|
||||
|
||||
export MODEL="llama3"
|
||||
export BASEDIR="/raid/datasets/c4-8b/"
|
||||
export SMALL=1
|
||||
export LLAMA3_SIZE=${LLAMA3_SIZE:-"8B"}
|
||||
export EVAL_TARGET=3.3 EVAL_FREQ=12288
|
||||
export LR="1e-3" END_LR="1e-4" WARMUP_SAMPLES=4096 MAX_STEPS=1200000
|
||||
export WARMUP_STEPS=$((WARMUP_SAMPLES / GBS))
|
||||
export SAMPLES=$((MAX_STEPS * GBS))
|
||||
export SEQLEN=${SEQLEN:-8192}
|
||||
|
||||
export SEED=${SEED:-5760}
|
||||
export DATA_SEED=${DATA_SEED:-5760}
|
||||
|
||||
export JITBEAM=${JITBEAM:-3}
|
||||
export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=1
|
||||
|
||||
export FAKEDATA=${FAKEDATA:-1} BENCHMARK=${BENCHMARK:-10}
|
||||
if [ -z "$FULL_LAYERS" ]; then
|
||||
export LLAMA_LAYERS=2
|
||||
fi
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
#!/usr/bin/env bash
|
||||
|
||||
export PYTHONPATH="."
|
||||
export DEV=${DEV:-AMD}
|
||||
export EMULATE="AMD_CDNA4"
|
||||
export CHECK_OOB=0
|
||||
export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
|
||||
export DEVICE_IN_FUNCTION_BUG=1
|
||||
|
||||
export DEBUG=${DEBUG:-0}
|
||||
export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
|
||||
export ALL2ALL=${ALL2ALL:-1}
|
||||
export USE_ATOMICS=${USE_ATOMICS:-0}
|
||||
export ASM_GEMM=${ASM_GEMM:-1}
|
||||
export WQKV=${WQKV:-1}
|
||||
export OFFLOAD_OPTIM=${OFFLOAD_OPTIM:-1}
|
||||
|
||||
export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
|
||||
export DP=${DP:-1} MP=${MP:-8} BS=${BS:-1} EVAL_BS=${EVAL_BS:-1} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-32}
|
||||
export GBS=$((BS * GRADIENT_ACC_STEPS))
|
||||
|
||||
export MODEL="llama3"
|
||||
export BASEDIR="/raid/datasets/c4-8b/"
|
||||
export SMALL=1
|
||||
export LLAMA3_SIZE=${LLAMA3_SIZE:-"8B"}
|
||||
export EVAL_TARGET=3.3 EVAL_FREQ=12288
|
||||
export LR="1e-3" END_LR="1e-4" WARMUP_SAMPLES=4096 MAX_STEPS=1200000
|
||||
export WARMUP_STEPS=$((WARMUP_SAMPLES / GBS))
|
||||
export SAMPLES=$((MAX_STEPS * GBS))
|
||||
export SEQLEN=${SEQLEN:-8192}
|
||||
|
||||
export SEED=${SEED:-$RANDOM}
|
||||
export DATA_SEED=${DATA_SEED:-5760}
|
||||
|
||||
export JITBEAM=${JITBEAM:-3}
|
||||
export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=1
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,50 +0,0 @@
|
|||
# 1. Problem
|
||||
|
||||
This problem uses the ResNet-50 CNN to do image classification.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging from master.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
### tinybox_red
|
||||
Disable cwsr
|
||||
This is the default on production tinybox red.
|
||||
```
|
||||
sudo vi /etc/modprobe.d/amdgpu.conf
|
||||
cat <<EOF > /etc/modprobe.d/amdgpu.conf
|
||||
options amdgpu cwsr_enable=0
|
||||
EOF
|
||||
sudo update-initramfs -u
|
||||
sudo reboot
|
||||
|
||||
# validate
|
||||
sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
|
||||
```
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
```
|
||||
IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
|
||||
```
|
||||
|
||||
## Steps for one time setup
|
||||
|
||||
### tinybox_red
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
|
||||
```
|
||||
|
||||
## Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=NV
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=10 DEBUG=2
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=NV
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
|
||||
|
||||
export EVAL_START_EPOCH=3 EVAL_FREQ=4
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,25 +0,0 @@
|
|||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." DEV=NV
|
||||
export MODEL="resnet"
|
||||
export SUBMISSION_PLATFORM="tinybox_green"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=${LOGMLPERF:-1}
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="resnet_green_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
|
|
@ -1,50 +0,0 @@
|
|||
# 1. Problem
|
||||
|
||||
This problem uses the ResNet-50 CNN to do image classification.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging from master.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
### tinybox_red
|
||||
Disable cwsr
|
||||
This is the default on production tinybox red.
|
||||
```
|
||||
sudo vi /etc/modprobe.d/amdgpu.conf
|
||||
cat <<EOF > /etc/modprobe.d/amdgpu.conf
|
||||
options amdgpu cwsr_enable=0
|
||||
EOF
|
||||
sudo update-initramfs -u
|
||||
sudo reboot
|
||||
|
||||
# validate
|
||||
sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
|
||||
```
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download and verify data
|
||||
|
||||
```
|
||||
IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
|
||||
```
|
||||
|
||||
## Steps for one time setup
|
||||
|
||||
### tinybox_red
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
|
||||
```
|
||||
|
||||
## Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
|
||||
```
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=10 DEBUG=${DEBUG:-2}
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="resnet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export EVAL_START_EPOCH=3 EVAL_FREQ=4
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
|
||||
python3 examples/mlperf/model_train.py
|
||||
|
|
@ -1,26 +0,0 @@
|
|||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="resnet"
|
||||
export SUBMISSION_PLATFORM="tinybox_red"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
|
||||
|
||||
export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=${LOGMLPERF:-1}
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="resnet_red_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
sleep 5 && sudo rmmod amdgpu || true
|
||||
BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
rocm-smi --setprofile compute
|
||||
rocm-smi --setmclk 3
|
||||
rocm-smi --setperflevel high
|
||||
|
||||
# power cap to 350W
|
||||
echo "350000000" | sudo tee /sys/class/drm/card{1..6}/device/hwmon/hwmon*/power1_cap
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
# 1. Problem
|
||||
|
||||
This problem uses RetinaNet for SSD.
|
||||
|
||||
## Requirements
|
||||
|
||||
Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
|
||||
```
|
||||
git clone https://github.com/tinygrad/tinygrad.git
|
||||
python3 -m pip install -e ".[mlperf]"
|
||||
```
|
||||
|
||||
Also install the following dependencies:
|
||||
```
|
||||
pip install tqdm numpy pycocotools boto3 pandas torch torchvision
|
||||
```
|
||||
|
||||
### tinybox_green
|
||||
Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
|
||||
This is the default on production tinybox green.
|
||||
|
||||
# 2. Directions
|
||||
|
||||
## Steps to download data
|
||||
|
||||
Run the following:
|
||||
```
|
||||
BASEDIR=/raid/datasets/openimages python3 extra/datasets/openimages.py
|
||||
```
|
||||
|
||||
## Running
|
||||
|
||||
### tinybox_green
|
||||
|
||||
#### Steps to run benchmark
|
||||
```
|
||||
examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/run_and_time.sh
|
||||
```
|
||||
|
|
@ -1,14 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=NV
|
||||
export MODEL="retinanet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=5 DEBUG=2
|
||||
|
||||
python examples/mlperf/model_train.py
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=NV
|
||||
export MODEL="retinanet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
export RUNMLPERF=1
|
||||
|
||||
python examples/mlperf/model_train.py
|
||||
|
|
@ -1,25 +0,0 @@
|
|||
#!/bin/bash
|
||||
set -e # Exit on any error
|
||||
set -o pipefail # Make pipeline fail if any command fails
|
||||
|
||||
export PYTHONPATH="." DEV=NV
|
||||
export MODEL="retinanet"
|
||||
export SUBMISSION_PLATFORM="tinybox_green"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
|
||||
export TRAIN_BEAM=2 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
export IGNORE_JIT_FIRST_BEAM=1
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# pip install -e ".[mlperf]"
|
||||
export LOGMLPERF=1
|
||||
|
||||
export SEED=$RANDOM
|
||||
DATETIME=$(date "+%m%d%H%M")
|
||||
LOGFILE="retinanet_green_${DATETIME}_${SEED}.log"
|
||||
|
||||
# init
|
||||
BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
|
||||
|
||||
# run
|
||||
PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
|
||||
|
|
@ -1,14 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="retinanet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export BENCHMARK=5 DEBUG=2
|
||||
|
||||
python examples/mlperf/model_train.py
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
#!/bin/bash
|
||||
|
||||
export PYTHONPATH="." DEV=AMD
|
||||
export MODEL="retinanet"
|
||||
export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
|
||||
export BASEDIR="/raid/datasets/openimages"
|
||||
|
||||
# export RESET_STEP=0
|
||||
|
||||
export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
|
||||
|
||||
export WANDB=1 PARALLEL=0
|
||||
export RUNMLPERF=1
|
||||
|
||||
python examples/mlperf/model_train.py
|
||||
|
|
@ -0,0 +1,106 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1778207373785, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207373789, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1329}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207373790, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1330}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207373790, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1331}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207373790, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "llama31_8b", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207373791, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207373791, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1337}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207734506, "event_type": "INTERVAL_END", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747904, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1340}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "seed", "value": 25580, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 32, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1343}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 8192, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "max_steps", "value": 1200000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 2, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 1024, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 38400000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1348}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adamw", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1350}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1351}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "opt_end_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747908, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_1", "value": 0.9, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747909, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_2", "value": 0.95, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747909, "event_type": "POINT_IN_TIME", "key": "opt_adamw_epsilon", "value": 1e-05, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747909, "event_type": "POINT_IN_TIME", "key": "opt_adamw_weight_decay", "value": 0.1, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747909, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1357}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747909, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747909, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_steps", "value": 1199872, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747909, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_schedule", "value": "cosine with linear warmup", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778207747909, "event_type": "POINT_IN_TIME", "key": "opt_gradient_clip_norm", "value": 1.0, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1361}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778208080716, "event_type": "INTERVAL_START", "key": "epoch_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1529, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778208080717, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1530, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778208901302, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778208901303, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778208952059, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 5.705078125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778208952060, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778208952060, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778209608282, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778209608282, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778209637796, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.552001953125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778209637796, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778209637797, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778210294879, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778210294879, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778210324584, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.1011962890625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778210324584, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778210324585, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778210980564, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778210980565, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778211010225, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.8807373046875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778211010225, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778211010226, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778211667184, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778211667185, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778211696784, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.7498779296875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778211696785, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778211696786, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778212356059, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778212356060, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778212385775, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.65478515625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778212385776, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778212385776, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778213044774, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778213044775, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778213074311, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5731201171875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778213074312, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778213074313, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778213732225, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778213732225, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778213761806, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5137939453125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778213761806, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778213761807, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778214419768, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778214419769, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778214449443, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.46630859375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778214449444, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778214449445, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778215112018, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778215112019, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778215141586, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.428955078125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778215141586, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778215141587, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778215794970, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778215794970, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778215824346, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.390869140625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778215824346, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778215824347, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778216475810, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778216475810, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778216505269, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.361328125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778216505269, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778216505270, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778217157389, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778217157390, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778217186831, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.346923828125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778217186832, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778217186832, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778217846265, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778217846266, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778217876013, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3133544921875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778217876014, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778217876014, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218532377, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218532378, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218561863, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.2989501953125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218561863, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218561864, "event_type": "INTERVAL_END", "key": "epoch_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1646, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218561864, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1647, "status": "success"}}
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1778218577779, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218577783, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1329}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218577784, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1330}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218577784, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1331}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218577784, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "llama31_8b", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218578371, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218578371, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1337}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218957180, "event_type": "INTERVAL_END", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971058, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1340}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "seed", "value": 356, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 32, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1343}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 8192, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "max_steps", "value": 1200000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 2, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 1024, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 38400000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1348}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adamw", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1350}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1351}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "opt_end_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_1", "value": 0.9, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_2", "value": 0.95, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971063, "event_type": "POINT_IN_TIME", "key": "opt_adamw_epsilon", "value": 1e-05, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971064, "event_type": "POINT_IN_TIME", "key": "opt_adamw_weight_decay", "value": 0.1, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971064, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1357}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971064, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971064, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_steps", "value": 1199872, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971064, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_schedule", "value": "cosine with linear warmup", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778218971064, "event_type": "POINT_IN_TIME", "key": "opt_gradient_clip_norm", "value": 1.0, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1361}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778219289653, "event_type": "INTERVAL_START", "key": "epoch_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1529, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778219289654, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1530, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778220097041, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778220097042, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778220141757, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 5.743896484375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778220141758, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778220141758, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778220795772, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778220795773, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778220825439, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.58349609375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778220825440, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778220825440, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778221480609, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778221480610, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778221510284, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.1131591796875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778221510285, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778221510286, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778222164664, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778222164665, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778222194290, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.8935546875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778222194291, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778222194291, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778222848846, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778222848847, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778222878557, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.7567138671875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778222878558, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778222878558, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778223532447, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778223532447, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778223562036, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.658203125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778223562037, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778223562037, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778224215343, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778224215344, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778224244924, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5860595703125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778224244925, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778224244925, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778224898378, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778224898379, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778224928021, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.51708984375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778224928021, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778224928022, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778225581424, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778225581425, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778225611002, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.471923828125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778225611003, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778225611003, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778226265043, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778226265044, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778226294659, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.43701171875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778226294660, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778226294661, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778226949577, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778226949577, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778226979238, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5406494140625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778226979239, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778226979239, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778227635352, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778227635352, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778227664978, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3836669921875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778227664978, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778227664979, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778228323150, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778228323151, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778228352865, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.355712890625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778228352865, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778228352866, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778229010307, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778229010307, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778229040142, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3319091796875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778229040143, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778229040143, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778229696378, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778229696379, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778229726195, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.30615234375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778229726195, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778229726196, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230383239, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230383240, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230412831, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.29052734375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230412832, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230412832, "event_type": "INTERVAL_END", "key": "epoch_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1646, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230412833, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1647, "status": "success"}}
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1778230427283, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230427287, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1329}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230427287, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1330}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230427287, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1331}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230427287, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "llama31_8b", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230427939, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230427939, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1337}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230779581, "event_type": "INTERVAL_END", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792886, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1340}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792890, "event_type": "POINT_IN_TIME", "key": "seed", "value": 2774, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 32, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1343}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 8192, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "max_steps", "value": 1200000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 2, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 1024, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 38400000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1348}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adamw", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1350}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1351}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "opt_end_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_1", "value": 0.9, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_2", "value": 0.95, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792891, "event_type": "POINT_IN_TIME", "key": "opt_adamw_epsilon", "value": 1e-05, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792892, "event_type": "POINT_IN_TIME", "key": "opt_adamw_weight_decay", "value": 0.1, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792892, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1357}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792892, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792892, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_steps", "value": 1199872, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792892, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_schedule", "value": "cosine with linear warmup", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778230792892, "event_type": "POINT_IN_TIME", "key": "opt_gradient_clip_norm", "value": 1.0, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1361}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778231115792, "event_type": "INTERVAL_START", "key": "epoch_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1529, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778231115793, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1530, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778232030906, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778232030907, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778232075494, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 5.812255859375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778232075494, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778232075495, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778232729579, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778232729580, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778232759140, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.582275390625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778232759141, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778232759142, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778233413630, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778233413631, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778233443219, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.11767578125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778233443220, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778233443220, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778234097427, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778234097428, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778234127034, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.9005126953125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778234127034, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778234127035, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778234780955, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778234780956, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778234810558, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.7586669921875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778234810558, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778234810559, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778235463904, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778235463905, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778235493473, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.657958984375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778235493474, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778235493475, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778236147005, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778236147005, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778236176551, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.585693359375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778236176552, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778236176552, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778236830530, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778236830530, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778236860107, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.521484375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778236860108, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778236860108, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778237514002, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778237514003, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778237543592, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4742431640625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778237543592, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778237543593, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778238197935, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778238197936, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778238227501, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.428955078125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778238227502, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778238227503, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778238882036, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778238882037, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778238911645, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4019775390625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778238911645, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778238911646, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778239565129, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778239565130, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778239594721, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.37890625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778239594722, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778239594722, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778240248763, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778240248764, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778240278335, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3448486328125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778240278336, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778240278337, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778240933651, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778240933651, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778240963429, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.325439453125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778240963430, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778240963431, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778241626264, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778241626265, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778241656303, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3072509765625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778241656304, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778241656304, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242315322, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242315323, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242345178, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.2781982421875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242345178, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242345179, "event_type": "INTERVAL_END", "key": "epoch_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1646, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242345179, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1647, "status": "success"}}
|
||||
|
|
@ -0,0 +1,106 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1778242359541, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242359545, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1329}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242359545, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1330}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242359545, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1331}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242359545, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "llama31_8b", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242360117, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242360118, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1337}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242702158, "event_type": "INTERVAL_END", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715949, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1340}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715953, "event_type": "POINT_IN_TIME", "key": "seed", "value": 1261, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715953, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 32, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1343}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715953, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 8192, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "max_steps", "value": 1200000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 2, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 1024, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 38400000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1348}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adamw", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1350}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1351}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "opt_end_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_1", "value": 0.9, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_2", "value": 0.95, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "opt_adamw_epsilon", "value": 1e-05, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "opt_adamw_weight_decay", "value": 0.1, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715954, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1357}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715955, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715955, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_steps", "value": 1199872, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715955, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_schedule", "value": "cosine with linear warmup", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778242715955, "event_type": "POINT_IN_TIME", "key": "opt_gradient_clip_norm", "value": 1.0, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1361}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778243033805, "event_type": "INTERVAL_START", "key": "epoch_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1529, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778243033806, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1530, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778243851371, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778243851372, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778243896651, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 5.7802734375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778243896652, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778243896652, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778244555628, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778244555629, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778244585531, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.574951171875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778244585532, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778244585533, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778245246511, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778245246512, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778245276502, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.1171875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778245276503, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778245276503, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778245937187, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778245937187, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778245967058, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.8995361328125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778245967059, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778245967059, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778246626117, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778246626117, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778246656019, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.762451171875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778246656019, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778246656020, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778247315255, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778247315256, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778247345128, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.6572265625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778247345128, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778247345129, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778248003582, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778248003582, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778248033442, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.58740234375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778248033443, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778248033443, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778248692764, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778248692764, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778248722726, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5286865234375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778248722727, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778248722727, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778249383186, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778249383186, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778249413099, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.475830078125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778249413099, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778249413100, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778250072852, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778250072852, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778250102740, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4278564453125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778250102741, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778250102741, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778250762230, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778250762230, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778250792198, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.400146484375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778250792199, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778250792199, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778251455492, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778251455492, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778251485544, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3818359375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778251485545, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778251485545, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778252146772, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778252146772, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778252176776, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.345458984375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778252176776, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778252176777, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778252836585, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778252836586, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778252866442, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.322265625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778252866443, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778252866443, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253526422, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253526422, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253556343, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.299072265625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253556343, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253556344, "event_type": "INTERVAL_END", "key": "epoch_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1646, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253556344, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1647, "status": "success"}}
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1778253570454, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253570459, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1329}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253570459, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1330}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253570459, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1331}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253570459, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "llama31_8b", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253571045, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253571045, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1337}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253944036, "event_type": "INTERVAL_END", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957691, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1340}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957695, "event_type": "POINT_IN_TIME", "key": "seed", "value": 14711, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957695, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 32, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1343}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957695, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 8192, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957695, "event_type": "POINT_IN_TIME", "key": "max_steps", "value": 1200000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957695, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 2, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957695, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 1024, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957695, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 38400000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1348}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957695, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adamw", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1350}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957695, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1351}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957696, "event_type": "POINT_IN_TIME", "key": "opt_end_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957696, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_1", "value": 0.9, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957696, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_2", "value": 0.95, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957696, "event_type": "POINT_IN_TIME", "key": "opt_adamw_epsilon", "value": 1e-05, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957696, "event_type": "POINT_IN_TIME", "key": "opt_adamw_weight_decay", "value": 0.1, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957696, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1357}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957696, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957696, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_steps", "value": 1199872, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957696, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_schedule", "value": "cosine with linear warmup", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778253957696, "event_type": "POINT_IN_TIME", "key": "opt_gradient_clip_norm", "value": 1.0, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1361}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778254276545, "event_type": "INTERVAL_START", "key": "epoch_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1529, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778254276546, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1530, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778255100535, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778255100536, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778255143977, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 5.77978515625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778255143977, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778255143978, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778255806844, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778255806845, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778255836518, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.578857421875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778255836519, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778255836520, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778256495933, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778256495933, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778256525443, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.1239013671875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778256525443, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778256525444, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778257180826, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778257180827, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778257210282, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.906494140625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778257210283, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778257210283, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778257866434, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778257866435, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778257895945, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.75244140625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778257895945, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778257895946, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778258550818, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778258550819, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778258580369, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.6553955078125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778258580369, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778258580370, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778259234200, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778259234201, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778259263770, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5762939453125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778259263771, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778259263772, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778259917494, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778259917495, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778259947011, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.52197265625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778259947012, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778259947013, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778260600453, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778260600454, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778260629950, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4765625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778260629951, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778260629951, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778261285126, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778261285127, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778261314809, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4378662109375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778261314810, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778261314810, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778261971632, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778261971632, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778262001260, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3968505859375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778262001261, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778262001261, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778262657393, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778262657394, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778262686962, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.365966796875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778262686962, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778262686963, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778263342665, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778263342666, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778263372176, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3365478515625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778263372176, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778263372177, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778264027427, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778264027428, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778264056993, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3363037109375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778264056993, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778264056994, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778264710992, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778264710993, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778264740486, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3016357421875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778264740486, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778264740487, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265396989, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265396989, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265426521, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.2861328125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265426522, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265426522, "event_type": "INTERVAL_END", "key": "epoch_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1646, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265426522, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1647, "status": "success"}}
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1778265440911, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265440915, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1329}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265440915, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1330}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265440916, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1331}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265440916, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "llama31_8b", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265441493, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265441493, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1337}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265779467, "event_type": "INTERVAL_END", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792765, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1340}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792769, "event_type": "POINT_IN_TIME", "key": "seed", "value": 27754, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792769, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 32, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1343}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792769, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 8192, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792769, "event_type": "POINT_IN_TIME", "key": "max_steps", "value": 1200000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792769, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 2, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792769, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 1024, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792769, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 38400000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1348}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792769, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adamw", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1350}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1351}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "opt_end_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_1", "value": 0.9, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_2", "value": 0.95, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "opt_adamw_epsilon", "value": 1e-05, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "opt_adamw_weight_decay", "value": 0.1, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1357}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_steps", "value": 1199872, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_schedule", "value": "cosine with linear warmup", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778265792770, "event_type": "POINT_IN_TIME", "key": "opt_gradient_clip_norm", "value": 1.0, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1361}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778266108942, "event_type": "INTERVAL_START", "key": "epoch_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1529, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778266108943, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1530, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778266913943, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778266913944, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778266957471, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 5.74072265625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778266957472, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778266957472, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778267616663, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778267616663, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778267648052, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.612060546875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778267648053, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778267648053, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778268306168, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778268306168, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778268335863, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.16552734375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778268335864, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778268335864, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778268998030, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778268998030, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778269027991, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.915283203125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778269027992, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778269027992, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778269689514, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778269689515, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778269719312, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.7637939453125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778269719313, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778269719313, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778270378319, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778270378320, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778270408037, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.6695556640625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778270408038, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778270408038, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778271066429, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778271066430, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778271096134, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.583251953125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778271096135, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778271096135, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778271754376, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778271754377, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778271784142, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.525146484375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778271784142, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778271784143, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778272442458, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778272442459, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778272472257, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4774169921875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778272472257, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778272472258, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778273129575, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778273129576, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778273159231, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.443359375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778273159231, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778273159232, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778273816098, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778273816099, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778273845769, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4072265625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778273845770, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778273845770, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778274505683, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778274505684, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778274535540, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3677978515625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778274535541, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778274535541, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778275195662, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778275195662, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778275225396, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4146728515625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778275225397, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778275225397, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778275884245, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778275884246, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778275913924, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3697509765625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778275913925, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778275913925, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778276570930, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778276570931, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778276600619, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.321533203125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778276600620, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778276600620, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277262406, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277262407, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277292466, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.287353515625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277292467, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277292467, "event_type": "INTERVAL_END", "key": "epoch_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1646, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277292468, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1647, "status": "success"}}
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1778277306868, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277306872, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1329}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277306872, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1330}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277306873, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1331}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277306873, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "llama31_8b", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277307428, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277307429, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1337}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277671564, "event_type": "INTERVAL_END", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685153, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1340}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685157, "event_type": "POINT_IN_TIME", "key": "seed", "value": 17816, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685157, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 32, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1343}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685157, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 8192, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "max_steps", "value": 1200000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 2, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 1024, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 38400000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1348}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adamw", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1350}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1351}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "opt_end_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_1", "value": 0.9, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_2", "value": 0.95, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "opt_adamw_epsilon", "value": 1e-05, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "opt_adamw_weight_decay", "value": 0.1, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1357}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685158, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685159, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_steps", "value": 1199872, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685159, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_schedule", "value": "cosine with linear warmup", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778277685159, "event_type": "POINT_IN_TIME", "key": "opt_gradient_clip_norm", "value": 1.0, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1361}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778278007248, "event_type": "INTERVAL_START", "key": "epoch_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1529, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778278007260, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1530, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778278810368, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778278810369, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778278855284, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 5.768798828125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778278855285, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778278855285, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778279519460, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778279519461, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778279549391, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.568603515625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778279549392, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778279549392, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778280214562, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778280214563, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778280244495, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.151123046875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778280244496, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778280244496, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778280909906, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778280909906, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778280939913, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.9197998046875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778280939913, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778280939914, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778281607749, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778281607750, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778281637814, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.7734375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778281637815, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778281637815, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778282306223, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778282306224, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778282336322, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.673583984375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778282336323, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778282336323, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778283007699, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778283007700, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778283037808, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.6011962890625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778283037808, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778283037809, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778283706598, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778283706598, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778283736748, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.526123046875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778283736748, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778283736749, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778284408590, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778284408590, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778284438316, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.475341796875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778284438317, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778284438317, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778285098897, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778285098898, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778285128703, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.432861328125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778285128703, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778285128704, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778285786660, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778285786660, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778285816222, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4031982421875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778285816222, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778285816223, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778286473781, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778286473782, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778286503417, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3638916015625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778286503418, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778286503418, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778287160556, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778287160556, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778287190213, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.341796875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778287190214, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778287190215, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778287846424, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778287846424, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778287876044, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.32177734375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778287876045, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778287876046, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778288531947, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778288531947, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778288561549, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5465087890625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778288561550, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778288561550, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289220442, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289220442, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289250127, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.2855224609375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289250128, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289250128, "event_type": "INTERVAL_END", "key": "epoch_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1646, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289250129, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1647, "status": "success"}}
|
||||
|
|
@ -0,0 +1,106 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1778289264340, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289264344, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1329}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289264344, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1330}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289264344, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1331}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289264344, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "llama31_8b", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289264911, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289264912, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1337}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289599730, "event_type": "INTERVAL_END", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613197, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1340}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613200, "event_type": "POINT_IN_TIME", "key": "seed", "value": 16781, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 32, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1343}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 8192, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "max_steps", "value": 1200000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 2, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 1024, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 38400000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1348}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adamw", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1350}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1351}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "opt_end_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_1", "value": 0.9, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_2", "value": 0.95, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613201, "event_type": "POINT_IN_TIME", "key": "opt_adamw_epsilon", "value": 1e-05, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613202, "event_type": "POINT_IN_TIME", "key": "opt_adamw_weight_decay", "value": 0.1, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613202, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1357}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613202, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613202, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_steps", "value": 1199872, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613202, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_schedule", "value": "cosine with linear warmup", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289613202, "event_type": "POINT_IN_TIME", "key": "opt_gradient_clip_norm", "value": 1.0, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1361}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289929875, "event_type": "INTERVAL_START", "key": "epoch_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1529, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778289929878, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1530, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778290756967, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778290756968, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778290801735, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 5.758544921875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778290801736, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778290801736, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778291460896, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778291460896, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778291490685, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.683349609375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778291490685, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778291490686, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778292152773, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778292152774, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778292182518, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.1280517578125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778292182519, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778292182519, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778292842100, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778292842101, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778292871768, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.90185546875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778292871769, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778292871769, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778293529314, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778293529315, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778293559042, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.757080078125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778293559043, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778293559043, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778294218188, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778294218189, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778294247880, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.6575927734375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778294247880, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778294247881, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778294908017, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778294908018, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778294937688, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.586181640625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778294937689, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778294937690, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778295595710, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778295595710, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778295625392, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5230712890625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778295625393, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778295625394, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778296283795, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778296283795, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778296313518, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.467529296875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778296313519, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778296313519, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778296973892, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778296973893, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778297003579, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4351806640625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778297003580, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778297003580, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778297661577, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778297661578, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778297691130, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.406982421875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778297691130, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778297691131, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778298348217, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778298348218, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778298377837, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3848876953125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778298377837, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778298377838, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778299035939, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778299035940, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778299065575, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3480224609375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778299065576, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778299065576, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778299724382, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778299724383, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778299754023, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3209228515625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778299754023, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778299754024, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300412415, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300412415, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300442058, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.2950439453125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300442059, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300442060, "event_type": "INTERVAL_END", "key": "epoch_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1646, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300442060, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1647, "status": "success"}}
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1778300456451, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300456455, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1329}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300456455, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1330}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300456455, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1331}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300456455, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "llama31_8b", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300457011, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300457012, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1337}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300803665, "event_type": "INTERVAL_END", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817390, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1340}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817395, "event_type": "POINT_IN_TIME", "key": "seed", "value": 4729, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817395, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 32, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1343}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817395, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 8192, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817395, "event_type": "POINT_IN_TIME", "key": "max_steps", "value": 1200000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817395, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 2, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817395, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 1024, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817395, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 38400000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1348}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817395, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adamw", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1350}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817395, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1351}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817395, "event_type": "POINT_IN_TIME", "key": "opt_end_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817396, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_1", "value": 0.9, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817396, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_2", "value": 0.95, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817396, "event_type": "POINT_IN_TIME", "key": "opt_adamw_epsilon", "value": 1e-05, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817396, "event_type": "POINT_IN_TIME", "key": "opt_adamw_weight_decay", "value": 0.1, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817396, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1357}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817396, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817396, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_steps", "value": 1199872, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817396, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_schedule", "value": "cosine with linear warmup", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778300817396, "event_type": "POINT_IN_TIME", "key": "opt_gradient_clip_norm", "value": 1.0, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1361}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778301145773, "event_type": "INTERVAL_START", "key": "epoch_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1529, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778301145774, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1530, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778301985088, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778301985089, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778302030319, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 5.865966796875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778302030319, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778302030320, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778302687526, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778302687527, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778302717259, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.615966796875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778302717260, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778302717261, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778303376036, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778303376037, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778303406044, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.154296875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778303406045, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778303406045, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778304071224, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778304071225, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778304101168, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.9095458984375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778304101169, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778304101170, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778304762172, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778304762173, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778304792161, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.775634765625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778304792162, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778304792162, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778305452836, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778305452836, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778305482708, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.676513671875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778305482708, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778305482709, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778306140246, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778306140246, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778306169947, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5947265625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778306169947, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778306169948, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778306828284, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778306828285, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778306858077, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5255126953125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778306858077, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778306858078, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778307519609, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778307519610, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778307549531, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4757080078125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778307549532, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778307549532, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778308208151, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778308208152, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778308237856, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4312744140625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778308237857, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778308237857, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778308896397, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778308896398, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778308926271, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.402099609375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778308926271, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778308926272, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778309586346, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778309586347, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778309616134, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.37060546875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778309616134, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778309616135, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778310273337, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778310273338, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778310303090, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3968505859375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778310303091, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778310303092, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778310958883, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778310958883, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778310988541, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3284912109375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778310988542, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778310988542, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778311645004, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778311645004, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778311674742, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.302001953125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778311674743, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778311674744, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312331845, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312331846, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312361570, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.2777099609375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312361571, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312361571, "event_type": "INTERVAL_END", "key": "epoch_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1646, "samples_count": 196608}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312361572, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1647, "status": "success"}}
|
||||
|
|
@ -0,0 +1,106 @@
|
|||
:::MLLOG {"namespace": "", "time_ms": 1778312377935, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1328}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312377940, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1329}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312377940, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1330}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312377940, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1331}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312377940, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "llama31_8b", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1333}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312378485, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1336}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312378485, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1337}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312726494, "event_type": "INTERVAL_END", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1629}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740045, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1340}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740049, "event_type": "POINT_IN_TIME", "key": "seed", "value": 12228, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1341}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740049, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 32, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1343}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740049, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 8192, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1344}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "max_steps", "value": 1200000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1345}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 2, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1346}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 1024, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1347}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 38400000, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1348}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "adamw", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1350}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1351}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "opt_end_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1352}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_1", "value": 0.9, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1353}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "opt_adamw_beta_2", "value": 0.95, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1354}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "opt_adamw_epsilon", "value": 1e-05, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1355}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "opt_adamw_weight_decay", "value": 0.1, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1356}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1357}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740050, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 128, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1358}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740051, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_steps", "value": 1199872, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1359}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740051, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_decay_schedule", "value": "cosine with linear warmup", "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1360}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778312740051, "event_type": "POINT_IN_TIME", "key": "opt_gradient_clip_norm", "value": 1.0, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1361}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778313057094, "event_type": "INTERVAL_START", "key": "epoch_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1529, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778313057095, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1530, "samples_count": 0}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778313872567, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778313872567, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778313917470, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 5.736083984375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778313917471, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778313917472, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 12288}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778314572849, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778314572850, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778314602523, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.584716796875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778314602524, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778314602525, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 24576}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778315258897, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778315258898, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778315288494, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 4.114501953125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778315288495, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778315288496, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 36864}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778315946776, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778315946777, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778315976384, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.906005859375, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778315976385, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778315976386, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 49152}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778316632177, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778316632178, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778316661800, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.76513671875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778316661800, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778316661801, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 61440}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778317318705, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778317318706, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778317348421, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.6568603515625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778317348421, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778317348422, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 73728}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778318007246, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778318007246, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778318036837, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.5897216796875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778318036838, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778318036839, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 86016}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778318691769, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778318691770, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778318721376, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.52587890625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778318721377, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778318721377, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 98304}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778319374807, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778319374808, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778319404256, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.473388671875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778319404257, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778319404258, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 110592}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778320058613, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778320058613, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778320087986, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.4307861328125, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778320087987, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778320087988, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 122880}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778320742022, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778320742022, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778320771659, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3931884765625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778320771660, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778320771660, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 135168}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778321426019, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778321426019, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778321455724, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3629150390625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778321455725, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778321455726, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 147456}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778322114634, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778322114634, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778322144126, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3377685546875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778322144127, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778322144127, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 159744}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778322801727, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778322801728, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778322831371, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.3150634765625, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778322831372, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778322831372, "event_type": "INTERVAL_START", "key": "block_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1654, "samples_count": 172032}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778323487126, "event_type": "INTERVAL_END", "key": "block_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1616, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778323487126, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1617, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778323516691, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 3.2889404296875, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1637, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778323516691, "event_type": "INTERVAL_END", "key": "eval_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1638, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778323516692, "event_type": "INTERVAL_END", "key": "epoch_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1646, "samples_count": 184320}}
|
||||
:::MLLOG {"namespace": "", "time_ms": 1778323516692, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad3/examples/mlperf/model_train.py", "lineno": 1647, "status": "success"}}
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
{
|
||||
"submitter": "tinycorp",
|
||||
"division": "closed",
|
||||
"status": "Available on-premise",
|
||||
"system_name": "tinybox 8xMI300X",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "2",
|
||||
"host_processor_model_name": "AMD EPYC 9354",
|
||||
"host_processor_core_count": "32",
|
||||
"host_processor_vcpu_count": "64",
|
||||
"host_processor_frequency": "",
|
||||
"host_processor_caches": "",
|
||||
"host_processor_interconnect": "",
|
||||
"host_memory_capacity": "2304GB",
|
||||
"host_storage_type": "NVMe SSD",
|
||||
"host_storage_capacity": "3x 4TB raid array",
|
||||
"host_networking": "",
|
||||
"host_networking_topology": "",
|
||||
"host_memory_configuration": "24x 96GB DDR5",
|
||||
"accelerators_per_node": "8",
|
||||
"accelerator_model_name": "AMD Instinct MI300X 192GB HBM3",
|
||||
"accelerator_host_interconnect": "PCIe 5.0 x16",
|
||||
"accelerator_frequency": "",
|
||||
"accelerator_on-chip_memories": "",
|
||||
"accelerator_memory_configuration": "HBM3",
|
||||
"accelerator_memory_capacity": "192GB",
|
||||
"accelerator_interconnect": "",
|
||||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, branch mlperf_training_v5.0",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.16",
|
||||
"ROCm": "3.0.0+94441cb"
|
||||
},
|
||||
"operating_system": "Ubuntu 24.04.1 LTS",
|
||||
"sw_notes": ""
|
||||
}
|
||||
|
|
@ -34,5 +34,5 @@
|
|||
"ROCm": "7.1.1"
|
||||
},
|
||||
"operating_system": "Ubuntu 24.04.3 LTS",
|
||||
"sw_notes": ""
|
||||
"sw_notes": "tinygrad @ 026688f03f84a75ec3fef034bcba916bf8f8bdc6"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,38 +0,0 @@
|
|||
{
|
||||
"submitter": "tinycorp",
|
||||
"division": "closed",
|
||||
"status": "Available on-premise",
|
||||
"system_name": "tinybox green",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "1",
|
||||
"host_processor_model_name": "AMD EPYC 7532",
|
||||
"host_processor_core_count": "32",
|
||||
"host_processor_vcpu_count": "64",
|
||||
"host_processor_frequency": "",
|
||||
"host_processor_caches": "",
|
||||
"host_processor_interconnect": "",
|
||||
"host_memory_capacity": "128GB",
|
||||
"host_storage_type": "NVMe SSD",
|
||||
"host_storage_capacity": "4 TB raid array + 1 TB boot",
|
||||
"host_networking": "",
|
||||
"host_networking_topology": "",
|
||||
"host_memory_configuration": "8x 16GB DDR4",
|
||||
"accelerators_per_node": "6",
|
||||
"accelerator_model_name": "NVIDIA GeForce RTX 4090",
|
||||
"accelerator_host_interconnect": "PCIe 4.0 x16",
|
||||
"accelerator_frequency": "",
|
||||
"accelerator_on-chip_memories": "",
|
||||
"accelerator_memory_configuration": "GDDR6X",
|
||||
"accelerator_memory_capacity": "24GB",
|
||||
"accelerator_interconnect": "",
|
||||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, branch mlperf_training_v5.0",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.12",
|
||||
"CUDA": "12.4"
|
||||
},
|
||||
"operating_system": "Ubuntu 22.04.4",
|
||||
"sw_notes": ""
|
||||
}
|
||||
|
|
@ -1,37 +0,0 @@
|
|||
{
|
||||
"submitter": "tinycorp",
|
||||
"division": "closed",
|
||||
"status": "Available on-premise",
|
||||
"system_name": "tinybox red",
|
||||
"number_of_nodes": "1",
|
||||
"host_processors_per_node": "1",
|
||||
"host_processor_model_name": "AMD EPYC 7532",
|
||||
"host_processor_core_count": "32",
|
||||
"host_processor_vcpu_count": "64",
|
||||
"host_processor_frequency": "",
|
||||
"host_processor_caches": "",
|
||||
"host_processor_interconnect": "",
|
||||
"host_memory_capacity": "128GB",
|
||||
"host_storage_type": "NVMe SSD",
|
||||
"host_storage_capacity": "4 TB raid array + 1 TB boot",
|
||||
"host_networking": "",
|
||||
"host_networking_topology": "",
|
||||
"host_memory_configuration": "8x 16GB DDR4",
|
||||
"accelerators_per_node": "6",
|
||||
"accelerator_model_name": "AMD Radeon RX 7900 XTX",
|
||||
"accelerator_host_interconnect": "PCIe 4.0 x16",
|
||||
"accelerator_frequency": "",
|
||||
"accelerator_on-chip_memories": "",
|
||||
"accelerator_memory_configuration": "GDDR6",
|
||||
"accelerator_memory_capacity": "24GB",
|
||||
"accelerator_interconnect": "",
|
||||
"accelerator_interconnect_topology": "",
|
||||
"cooling": "air",
|
||||
"hw_notes": "",
|
||||
"framework": "tinygrad, branch mlperf_training_v5.0",
|
||||
"other_software_stack": {
|
||||
"python": "3.10.12"
|
||||
},
|
||||
"operating_system": "Ubuntu 22.04.4",
|
||||
"sw_notes": ""
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue