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Releases: ray-project/tune-sklearn

tune-sklearn 0.2.0 release notes

19 Dec 04:41
@richardliaw richardliaw
v0.2.0
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tune-sklearn 0.2.0 release notes

New Features:

  • tune-sklearn now supports sampling with Optuna! (#136, #132)
  • You can now do deadline-based hyperparameter tuning with the new time_budget_s parameter (#134)
  • Custom logging can be done by passing in loggers as strings (TuneSearchCV(loggers=["json", "tensorboard"])) (#100)
  • Reproducible experiments can be set with a seed parameter to make initial configuration sampling deterministic (#140)
  • Custom stopping (such as stopping a hyperparameter search upon plateau) is now supported (#156)

Improvements:

  • Support for Tune search spaces (#128)
  • Use fractional GPUs for a Ray cluster (#145)
  • Bring API in line with sklearn best_params accessible without refit=True, (#114)
  • Early stopping support for sklearn Pipelines, LightGBM and CatBoost (#103, #109)
  • Implement resource step for early stopping (#121)
  • Raise Errors on trial failures instead of logging them (#130)
  • Remove unnecessary dependencies (#152)

Bug fixes:

  • Refactor early stopping case handling in _train (#97)
  • Fix Warm start errors (#106)
  • Fix hyperopt loguniform params (#104)
  • Fix of multi_metric scoring issue (#111)
  • BOHB sanity checks (#133)
  • Avoid Loky Pickle Error (#150)

Special thanks to: @krfricke, @amogkam, @Yard1, @richardliaw, @inventormc, @mattKretschmer

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tune-sklearn 0.1.0 release notes

12 Sep 03:59
@richardliaw richardliaw
v0.1.0
e865bce
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tune-sklearn 0.1.0 release notes

Release Information

This tune-sklearn release is expected to work with:

  • the latest Ray master branch
  • the latest Ray release (0.8.7).

Try out: pip install tune-sklearn==0.1.0
See the most up-to-date version of the documentation in https://docs.ray.io/en/master/tune/api_docs/sklearn.html (corresponding to the master branch).

Highlights

These release notes contain all updates since tune-sklearn==0.0.7.

  • tune-sklearn now supports multiple search algorithms (including TPE from HyperOpt and BOHB). Thanks @Yard1!
  • tune-sklearn now supports iterative training for XGBoost (by iteratively increasing the number of rounds) and most models that have warm_start capabilities. This is only enabled if early_stopping=True.

Other notes:

  • The Ray Dashboard is disabled by default. This should reduce error messages.
  • n_iter is now renamed to n_trials to avoid confusion
  • Multi-metric scoring is now supported
  • You can set local_mode to run everything on a single process. This can be faster in some cases.

List of changes

Update setup.py to remove sklearn version control (#96)
[travis] try-fast-build (#95)
Travis fix (#94)
[docs] Fix docs and build to avoid regression (#92)
Warm start for ensembles (#90)
Explicitly pass mode=max to schedulers (#91)
Enable scikit-optimize again (#89)
Multimetric scoring (#62)
Early stopping for XGBoost + Update Readme (#63)
Fix BOHB, change n_iter -> n_trials, fix up early stopping (#81)
Disable the Ray Dashboard (#82)
Provide local install command (#78)
Use warm start for early stopping (#46)
Fix condition in _fill_config_hyperparam (#76)
Enable local mode + forward compat (#74)
Add a missing space in readme (#69)
New search algorithms (#68)
fix resources per trial (#52)

Thanks to @inventormc, @Yard1 , @holgern , @krfricke , @richardliaw for contributing!

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