tinygrad

mirror of https://github.com/tinygrad/tinygrad.git synced 2026-04-07 03:00:26 -04:00

Files

chenyu 8401de9922 resnet benchmark return early in eval (#4278 )

only do few eval steps to compile, and skip second epoch when doing beam + benchmark. save 2 minutes

2024-04-24 00:55:01 -04:00

dataloader.py

BERT dataloader (#4252 )

2024-04-23 13:44:49 -04:00

helpers.py

scipy.signal.gaussian -> scipy.signal.windows.gaussian (#4205 )

2024-04-17 19:15:37 -04:00

initializers.py

Benchmarks for individual resnet layers (#4182 )

2024-04-16 13:53:18 -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

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

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

model_spec.py

move globalcounters to ops (#2960 )

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

model_train.py

resnet benchmark return early in eval (#4278 )

2024-04-24 00:55:01 -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