Skip to content

Add PTL to README.rst #9594

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Jul 20, 2020
Merged

Add PTL to README.rst #9594

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
5751436
Update README.rst
williamFalcon Jul 20, 2020
fa7d58d
Update README.rst
richardliaw Jul 20, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion README.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -78,7 +78,7 @@ Tune Quick Start
`Tune`_ is a library for hyperparameter tuning at any scale.

- Launch a multi-node distributed hyperparameter sweep in less than 10 lines of code.
- Supports any deep learning framework, including PyTorch, TensorFlow, and Keras.
- Supports any deep learning framework, including PyTorch, `PyTorch Lightning <https://github.com/williamFalcon/pytorch-lightning>`_, TensorFlow, and Keras.
- Visualize results with `TensorBoard <https://www.tensorflow.org/get_started/summaries_and_tensorboard>`__.
- Choose among scalable SOTA algorithms such as `Population Based Training (PBT)`_, `Vizier's Median Stopping Rule`_, `HyperBand/ASHA`_.
- Tune integrates with many optimization libraries such as `Facebook Ax <http://ax.dev>`_, `HyperOpt <https://github.com/hyperopt/hyperopt>`_, and `Bayesian Optimization <https://github.com/fmfn/BayesianOptimization>`_ and enables you to scale them transparently.
Expand Down