Add tuner.py to Docs (#3689)
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docs/reference/yolo/utils/tuner.md
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9
docs/reference/yolo/utils/tuner.md
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---
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description: Optimize YOLO models' hyperparameters with Ultralytics YOLO's `run_ray_tune` function using Ray Tune and ASHA scheduler.
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keywords: Ultralytics YOLO, Hyperparameter Tuning, Ray Tune, ASHAScheduler, Optimization, Object Detection
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---
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## run_ray_tune
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---
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### ::: ultralytics.yolo.utils.tuner.run_ray_tune
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<br><br>
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@ -351,6 +351,7 @@ nav:
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- plotting: reference/yolo/utils/plotting.md
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- tal: reference/yolo/utils/tal.md
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- torch_utils: reference/yolo/utils/torch_utils.md
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- tuner: reference/yolo/utils/tuner.md
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- v8:
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- classify:
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- predict: reference/yolo/v8/classify/predict.md
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setup.py
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setup.py
@ -46,7 +46,7 @@ setup(
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'mkdocs-material',
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'mkdocstrings[python]',
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'mkdocs-redirects', # for 301 redirects
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'mkdocs-ultralytics-plugin', # for meta descriptions and images, dates and authors
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'mkdocs-ultralytics-plugin>=0.0.21', # for meta descriptions and images, dates and authors
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],
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'export': ['coremltools>=6.0', 'openvino-dev>=2022.3', 'tensorflowjs'], # automatically installs tensorflow
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},
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@ -389,15 +389,7 @@ class YOLO:
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def tune(self, *args, **kwargs):
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"""
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Runs hyperparameter tuning using Ray Tune.
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Args:
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data (str): The dataset to run the tuner on.
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space (dict, optional): The hyperparameter search space. Defaults to None.
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grace_period (int, optional): The grace period in epochs of the ASHA scheduler. Defaults to 10.
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gpu_per_trial (int, optional): The number of GPUs to allocate per trial. Defaults to None.
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max_samples (int, optional): The maximum number of trials to run. Defaults to 10.
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train_args (dict, optional): Additional arguments to pass to the `train()` method. Defaults to {}.
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Runs hyperparameter tuning using Ray Tune. See ultralytics.yolo.utils.tuner.run_ray_tune for Args.
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Returns:
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(dict): A dictionary containing the results of the hyperparameter search.
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