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chenyuandGitHub ab01a9433d resnet eval 4n+3 if epoch < 33 (#4391 )

the rule is as thoroughly as 4n+k and we can stop the clock as soon as eval hits target. this can save 24 evals or 12 minutes

2024-05-02 16:52:07 -04:00

training_submission_v4.0/tinycorp

resnet eval 4n+3 if epoch < 33 (#4391 )

2024-05-02 16:52:07 -04:00

dataloader.py

resnet correct steps_in_val_epoch in logging (#4389 )

2024-05-02 10:51:36 -04:00

helpers.py

BERT: Checkpoint loading tests (#4359 )

2024-04-30 14:43:41 -04:00

initializers.py

MLPerf BERT: Main training loop (#4288 )

2024-04-29 14:35:27 -04:00

losses.py

[MLPerf][UNet3D] Add DICE loss + metrics (#4204 )

2024-04-17 20:09:33 -04:00

lr_schedulers.py

fp16 resnet (without expand backwards sum in float, doesn't work) (#3816 )

2024-03-28 01:25:37 -04:00

metrics.py

[MLPerf][UNet3D] Add DICE loss + metrics (#4204 )

2024-04-17 20:09:33 -04:00

model_eval.py

pad first batch of imagenet dataloader and update eval (#4368 )

2024-05-01 00:21:52 -04:00

model_spec.py

move globalcounters to ops (#2960 )

2024-01-01 14:21:02 -08:00

model_train.py

resnet eval 4n+3 if epoch < 33 (#4391 )

2024-05-02 16:52:07 -04:00

README

start on mlperf models

2023-05-10 16:30:49 -07:00

README

Each model should be a clean single file.
They are imported from the top level `models` directory

It should be capable of loading weights from the reference imp.

We will focus on these 5 models:

# Resnet50-v1.5 (classic) -- 8.2 GOPS/input
# Retinanet
# 3D UNET (upconvs)
# RNNT
# BERT-large (transformer)

They are used in both the training and inference benchmark:
https://mlcommons.org/en/training-normal-21/
https://mlcommons.org/en/inference-edge-30/
And we will submit to both.

NOTE: we are Edge since we don't have ECC RAM