tinygrad/examples/mlperf
George Hotz 41efaa848c
move graph.py and jit.py into features (#3376)
* move graph.py into features

* move jit into features

* fix quickstart
2024-02-12 17:34:34 +01:00
..
README start on mlperf models 2023-05-10 16:30:49 -07:00
dataloader.py use hip events (#3157) 2024-01-17 10:39:57 -08:00
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 move graph.py and jit.py into features (#3376) 2024-02-12 17:34:34 +01:00
model_spec.py move globalcounters to ops (#2960) 2024-01-01 14:21:02 -08:00
model_train.py with Tensor.train() (#1935) 2023-09-28 18:02:31 -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