Display device_name="MYRIAD" for Intel NCS2 option per user question in https://github.com/ultralytics/yolov5/issues/8154

Improve DDP console response in https://github.com/ultralytics/yolov5/issues/8283

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* HUB dataset_stats() error reporting

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Negatively impacts YouTube inference but removes any lag on webcams/RTSP/RTMP etc.

Resolves https://github.com/ultralytics/yolov5/issues/4465

* 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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Bumps [actions/setup-python](https://github.com/actions/setup-python) from 3 to 4.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v3...v4)

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updated-dependencies:
- dependency-name: actions/setup-python
  dependency-type: direct:production
  update-type: version-update:semver-major
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* Update dataloaders.py

* 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

* Update autobatch.py

updates:
- [github.com/asottile/pyupgrade: v2.32.0 → v2.32.1](https://github.com/asottile/pyupgrade/compare/v2.32.0...v2.32.1)

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* experimental.py Apple MPS fix

May resolve https://github.com/ultralytics/yolov5/issues/8102

* Update experimental.py

* Update experimental.py

* Improved dataset error introspection

Intended to help https://github.com/ultralytics/yolov5/issues/8090

* Update general.py

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* Add `psutil` and `ipython` to requirements.txt

Lightweight packages used by YOLOv5 for system utilization (psutil) and interactive notebooks (IPython)

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* sort alphabetically

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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

* Write .yaml file when exporting model to openvino

Write .yaml file automatically when exporting model to openvino to be used during inference

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* Update export.py

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* Load metadata on inference

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Resolves https://github.com/ultralytics/yolov5/discussions/7931

* Code refactor for general.py

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* Update restapi.py

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