Cutting edge hyperparameter tuning with Ray Tune

Ray Tune is a Python library that accelerates hyperparameter tuning by allowing you to leverage cutting edge optimization algorithms at scale.

Richard Liaw
Aug 20, 2019 · 7 min read
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Population-based Training improves DeepMind’s algorithms on many domains by significant margins. Source: https://deepmind.com/blog/population-based-training-neural-networks/
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Tune scales your training from a single machine to a large distributed cluster without changing your code.
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Note for Search Algorithms: as of 8/12/2019, HpBandSter supports HyperBand, Random Search, and BOHB. KerasTuner supports Random Search, HyperBand, and Bayesian Optimization. Optuna supports Median (Percentile) Stopping, ASHA, Random Search, and Bayesian Optimization (TPE). HyperOpt supports Bayesian Optimization and Random Search. Tune supports PBT, BOHB, ASHA, HyperBand, Median Stopping, Random Search, Bayesian Optimization (TPE, etc), and numerous others due to library integrations.
If you run into any issues, please post in the comments!
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https://twitter.com/MarcCoru/status/1080596327006945281
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Using Tune is Easy!

header of `tune_script.py`
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Early stopping with ASHA.
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Going distributed

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Launch a cluster and distribute hyperparameter search without changing your code
tune-default.yaml
$ ray submit tune-default.yaml tune_script.py --start \
--args=”localhost:6379”
$ ray down tune-default.yaml

Learn more:


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