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* start work on target * add test * update actions to use DEV * update docs * update readmes * tests need that too * update example * update tests (comments) * fix that test * ruff * mypy * oops * remove getenvs * don't add Target yet * and the test * lint * and docs * more stuff * assert * few more fixes * test assert
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