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automl [tune] Add AutoML algorithm of GeneticSearcher (#2699) Sep 12, 2018
automlboard [tune] Tune onto Logging Module (#2882) Sep 16, 2018
examples [tune] Fully deprecate raw function literals in Tune (#3788) Jan 20, 2019
schedulers [tune] Tweak/allow nested pbt mutations (#3455) Jan 4, 2019
suggest [tune] Fully deprecate raw function literals in Tune (#3788) Jan 20, 2019
test [tune] Fully deprecate raw function literals in Tune (#3788) Jan 20, 2019
ParallelCoordinatesVisualization.ipynb [rllib, tune] TrainingResult -> Dict, Removes C408 from flake8 (#2565) Aug 7, 2018
README.rst [tune] Annotated Example Page and showcase Tutorials (#3267) Nov 9, 2018
TuneClient.ipynb [tune] Split Search from Scheduling (#2452) Aug 5, 2018
__init__.py [tune] Change log handling for Tune (#3661) Jan 6, 2019
cluster_info.py [tune] Sync logs from workers and improve tensorboard reporting (#1567) Feb 26, 2018
config_parser.py [tune] Cluster Fault Tolerance (#3309) Dec 29, 2018
error.py [tune] Experiment Management API (#1328) Jan 24, 2018
experiment.py [tune] Cluster Fault Tolerance (#3309) Dec 29, 2018
function_runner.py [tune] Doc: Autofilled, StatusReporter (#3294) Nov 13, 2018
log_sync.py [tune] Cross-Node Recovery (#3725) Jan 15, 2019
logger.py [tune] Cross-Node Recovery (#3725) Jan 15, 2019
ray_trial_executor.py [tune] Cross-Node Recovery (#3725) Jan 15, 2019
registry.py [tune] Add a callable check for converting to trainable (#3711) Jan 8, 2019
result.py [tune] Doc: Autofilled, StatusReporter (#3294) Nov 13, 2018
trainable.py [tune] Cross-Node Recovery (#3725) Jan 15, 2019
trial.py [tune] Cross-Node Recovery (#3725) Jan 15, 2019
trial_executor.py [tune] Cluster Fault Tolerance (#3309) Dec 29, 2018
trial_runner.py [tune] Avoid overwriting checkpoint file (#3781) Jan 16, 2019
tune.py [tune] Avoid overwriting checkpoint file (#3781) Jan 16, 2019
util.py [tune] Support Configuration Merging (#3584) Dec 26, 2018
visual_utils.py [rllib] Fix LSTM regression on truncated sequences and add regression… Sep 18, 2018
web_server.py [tune] Better Serialization for Server (#3708) Jan 9, 2019

README.rst

Tune: Scalable Hyperparameter Search

Tune is a scalable framework for hyperparameter search with a focus on deep learning and deep reinforcement learning.

User documentation can be found here.

Tutorial

To get started with Tune, try going through our tutorial of using Tune with Keras.

(Experimental): You can try out the above tutorial on a free hosted server via Binder.

Citing Tune

If Tune helps you in your academic research, you are encouraged to cite our paper. Here is an example bibtex:

@article{liaw2018tune,
    title={Tune: A Research Platform for Distributed Model Selection and Training},
    author={Liaw, Richard and Liang, Eric and Nishihara, Robert and
            Moritz, Philipp and Gonzalez, Joseph E and Stoica, Ion},
    journal={arXiv preprint arXiv:1807.05118},
    year={2018}
}