wehub-resource-sync
19 lines
597 B
ReStructuredText
19 lines
597 B
ReStructuredText
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Asynchronous HyperBand Example
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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This example demonstrates how to use Ray Tune's Asynchronous Successive Halving Algorithm (ASHA) scheduler
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to efficiently optimize hyperparameters for a machine learning model. ASHA is particularly useful for
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large-scale hyperparameter optimization as it can adaptively allocate resources and end
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poorly performing trials early.
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Requirements: `pip install "ray[tune]"`
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.. literalinclude:: /../../python/ray/tune/examples/async_hyperband_example.py
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See Also
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--------
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- `ASHA Paper <https://arxiv.org/abs/1810.05934>`_ |