Ray 0.7.6 Release Notes
Highlights
-
The Ray autoscaler now supports Kubernetes as a backend (#5492). This makes it possible to start a Ray cluster on top of your existing Kubernetes cluster with a simple shell command.
- Please see the Kubernetes section of the autoscaler documentation to get started.
- This is a new feature and may be rough around the edges. If you run into problems or have suggestions for how to improve Ray on Kubernetes, please file an issue.
-
The Ray cluster dashboard has been revamped (#5730, #5857) to improve the UI and include logs and error messages. More improvements will be coming in the near future.
- You can try out the dashboard by starting Ray with
ray.init(include_webui=True)orray start --include-webui. - Please let us know if you have suggestions for what would be most useful to you in the new dashboard.
- You can try out the dashboard by starting Ray with
Core
- Progress towards refactoring the Python worker on top of the core worker. #5750, #5771, #5752
- Fix an issue in local mode where multiple actors didn't work properly. #5863
- Fix class attributes and methods for actor classes. #5802
- Improvements in error messages and handling. #5782, #5746, #5799
- Serialization improvements. #5841, #5725
- Various documentation improvements. #5801, #5792, #5414, #5747, #5780, #5582
RLlib
- Added a link to BAIR blog posts in the documentation. #5762
- Tracing for eager tensorflow policies with
tf.function. #5705
Tune
- Improved MedianStoppingRule. #5402
- Add PBT + Memnn example. #5723
- Add support for function-based stopping condition. #5754
- Save/Restore for Suggestion Algorithms. #5719
- TensorBoard HParams for TF2.0. #5678
Other Libraries
Thanks
We thank the following contributors for their amazing contributions:
@hershg, @JasonWayne, @kfstorm, @richardliaw, @batzner, @vakker, @robertnishihara, @stephanie-wang, @gehring, @edoakes, @zhijunfu, @pcmoritz, @mitchellstern, @ujvl, @simon-mo, @ecederstrand, @mawright, @ericl, @anthonyhsyu, @suquark, @waldroje