tinygrad

mirror of https://github.com/tinygrad/tinygrad.git synced 2026-01-26 15:28:10 -05:00

Files

Umut Zengin f720682beb np.argmax to Tensor.argmax (#1608 )

* to tensor argmax

* removed keepdim

* training update

2023-08-21 15:22:29 -07:00

helpers.py

Add MLPerf UNet3D model (#775 )

2023-05-28 20:38:19 -07:00

metrics.py

Add MLPerf UNet3D model (#775 )

2023-05-28 20:38:19 -07:00

model_eval.py

np.argmax to Tensor.argmax (#1608 )

2023-08-21 15:22:29 -07:00

model_spec.py

MaskRCNN Inference (#884 )

2023-06-25 15:37:51 -07:00

model_train.py

feat: add train scaffolding (#859 )

2023-05-30 07:10:40 -07:00

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