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1dbf3b2b19* resnet individual layer benchmarks! * small * 1 and 2 * mem_used * no ci * better conv print * defaults * prints * adjust * adjust * adjust * benchmark only one layer example * tensor.training, zero_grad, sum instead of mean, last mem, last kernel count * default jitcnt=1 * scale flops/kernels with jitcnt * add note about jitcnt memory * touchup |
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| .. | ||
| dataloader.py | ||
| helpers.py | ||
| initializers.py | ||
| lr_schedulers.py | ||
| metrics.py | ||
| model_eval.py | ||
| model_spec.py | ||
| model_train.py | ||
| 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