tinygrad

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History

chenyu 2af129c078 bert corealize multiple outputs (#9359 ) 1% faster step		2025-03-05 10:58:37 -05:00
..
scripts	UNet3D MLPerf (#3470 )	2024-09-10 04:37:28 -04:00
training_submission_v4.0/tinycorp	copy mlperf 4.0 to mlperf 4.1 (#5614 )	2024-07-20 16:12:00 -04:00
training_submission_v4.1/tinycorp	update mlperf systems and copy 4.1 to 5.0 (#7004 )	2024-10-11 16:20:34 -04:00
training_submission_v5.0/tinycorp	RESET_STEP in bert setup and beam (#9248 )	2025-02-25 19:15:10 -05:00
dataloader.py	revert buffer_view change (#9311 )	2025-03-01 11:00:12 +01:00
helpers.py	CLANG -> CPU (#9189 )	2025-02-20 18:03:09 -05:00
initializers.py	Tuple -> tuple, List -> list [pr] (#8936 )	2025-02-06 14:21:19 -05:00
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	[MLPerf] Prepare openimages dataset script (#6747 )	2024-09-27 11:13:56 -04:00
model_spec.py	move globalcounters to ops (#2960 )	2024-01-01 14:21:02 -08:00
model_train.py	bert corealize multiple outputs (#9359 )	2025-03-05 10:58:37 -05:00
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