mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-25 06:48:22 -05:00
9f66dcf718
* working PolynomialDecayWithWarmup + tests....... add lars_util.py, oops * keep lars_util.py as intact as possible, simplify our interface * whitespace * clean up * clean up * asserts * test polylr for full resnet training run * add comment * rename * fix do_optim * don't cast lr * info * calculate from train_files * skip it
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