* assert torch!=1.12.0 for DDP training
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* Refactor optimizer initialization
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* Update train.py
* Update train.py
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* Add `--hard-fail` list argument to benchmarks for CI
Will cause CI to fail on a benchmark failure for given indices.
* Update ci-testing.yml
* Attempt Failure (CI should fail)
* Update benchmarks.py
* Update export.py
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* Update benchmarks.py
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* Update ci-testing.yml
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* attempt at reproducibility
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* use deterministic algs
* fix everything :)
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* revert dataloader changes
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* process_batch as np
* remove newline
* Remove dataloader init fcn
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* revert additional changes
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* Update train.py
* Add --seed arg
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* Update train.py
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* Add deterministic argument to init_seeds()
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* Add filesize to benchmarks.py
* Add filesize to benchmarks.py
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* HUB dataset_stats() error reporting
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* Update dataloaders.py
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* Reject webcam inference on Colab/Kaggle
Improve user error understanding for https://github.com/ultralytics/yolov5/issues/8180
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* cleanup
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* Update dataloaders.py
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* AutoBatch checks against failed solutions
@kalenmike this is a simple improvement to AutoBatch to verify that returned solutions have not already failed, i.e. return batch-size 8 when 8 already produced CUDA out of memory.
This is a halfway fix until I can implement a 'final solution' that will actively verify the solved-for batch size rather than passively assume it works.
* Update autobatch.py
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* Apple Metal Performance Shader (MPS) device support
Following https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Should work with Apple M1 devices with PyTorch nightly installed with command `--device mps`. Usage examples:
```bash
python train.py --device mps
python detect.py --device mps
python val.py --device mps
```
* Update device strategy to fix MPS issue
* Code refactor for general.py
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* Update restapi.py
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* Add PyTorch AMP check
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* Cleanup
* Cleanup
* Cleanup
* Robust for DDP
* Fixes
* Add amp enabled boolean to check_train_batch_size
* Simplify
* space to prefix
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