tinygrad

mirror of https://github.com/tinygrad/tinygrad.git synced 2026-06-24 02:14:17 +00:00

History

chenyu bed70b130c mlperf bert getenv-able EVAL_STEP_FREQ (#4534 )		2024-05-11 14:36:56 -04:00
..
training_submission_v4.0/tinycorp	feat: more mlperf fixes (#4505 )	2024-05-09 20:50:20 -07:00
dataloader.py	pad resnet training data with training data mean (#4369 )	2024-05-02 20:26:15 -04:00
helpers.py	fix TRAIN_BEAM and Tensor.training for mlperf bert (#4525 )	2024-05-11 00:18:36 -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	mlperf bert getenv-able EVAL_STEP_FREQ (#4534 )	2024-05-11 14:36:56 -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