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4h32m with this https://wandb.ai/chenyuxyz/MLPerf-BERT/runs/q99frv1l/overview. loss scaler 2**13->2**10. matched the closest submission, no nan for ~10 runs. increased lr and total step a bit. `PARALLEL=0` after setup, same as resnet.
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