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qazal a9a87ad8fd
 viz/cli: less flags (#16076) 
* viz/cli: merge -s and -i flags

* only -t

* merge parser

* fix
2026-05-08 00:22:40 +09:00
..
models llama speed 6 (#16071) 2026-05-06 20:51:03 -07:00
scripts download data and ckpts for sd train/eval (#12170) 2025-09-15 00:31:45 -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 deprecate <dev>=1 in favor of DEV=<dev> (#15467) 2026-03-26 03:48:03 -04:00
training_submission_v5.1/tinycorp deprecate <dev>=1 in favor of DEV=<dev> (#15467) 2026-03-26 03:48:03 -04:00
training_submission_v6.0/tinycorp viz/cli: less flags (#16076) 2026-05-08 00:22:40 +09:00
dataloader.py fix retinanet shared memory race condition in parallel tests (#15030) 2026-02-27 08:36:24 +08:00
helpers.py train bert with fp8 (#13874) 2026-01-09 09:21:59 -05:00
initializers.py remove contiguous and use where in EmbeddingBert (#13632) 2025-12-09 15:49:21 -05:00
losses.py cleanups on losses and dataset tests (#9538) 2025-03-21 17:03:18 -04:00
lr_schedulers.py LR scheduler for Stable Diffusion mlperf training (#12201) 2025-09-30 21:21:08 -04:00
metrics.py log_perplexity metrics (#10912) 2025-06-21 10:44:47 -04:00
model_eval.py DEV is ContextVar, setting Device.DEFAULT is deprecated (#15508) 2026-03-30 17:10:49 -04:00
model_spec.py remove Tensor.no_grad, it's meaningless now [pr] (#10556) 2025-05-28 22:20:02 -07:00
model_train.py llama mp fixes (#16050) 2026-05-05 15:35:32 -07:00
optim.py llama mp fixes (#16050) 2026-05-05 15:35:32 -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