ray-project/ray ray-0.8.6 on GitHub

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Experimental support for Windows is now available for single node Ray usage. Check out the Windows section below for known issues and other details.
Have you had troubles monitoring GPU or memory usage while you used Ray? The Ray dashboard now supports the GPU monitoring and a memory view.
Want to use RLlib with Unity? RLlib officially supports the Unity3D adapter! Please check out the documentation.
Ray Serve is ready for feedback! We've gotten feedback from many users, and Ray Serve is already being used in production. Please reach out to us with your use cases, ideas, documentation improvements, and feedback. We'd love to hear from you. Please do so on the Ray Slack and join #serve! Please see the Serve section below for more details.

We’ve introduced a new feature to automatically retry failed actor tasks after an actor has been restarted by Ray (by specifying max_restarts in @ray.remote). Try it out with max_task_retries=-1 where -1 indicates that the system can retry the task until it succeeds.

To enable automatic restarts of a failed actor, you must now use max_restarts in the @ray.remote decorator instead of max_reconstructions. You can use -1 to indicate infinity, i.e., the system should always restart the actor if it fails unexpectedly.
We’ve merged the named and detached actor APIs. To create an actor that will survive past the duration of its job (a “detached” actor), specify name=<str> in its remote constructor (Actor.options(name='<str>').remote()). To delete the actor, you can use ray.kill.

PyTorch: IMPALA PyTorch version and all rllib/examples scripts now work for either TensorFlow or PyTorch (--torch command line option).
Switched to using distributed execution API by default (replaces Policy Optimizers) for all algorithms.
Unity3D adapter (supports all Env types: multi-agent, external env, vectorized) with example scripts for running locally or in the cloud.
Added support for variable length observation Spaces ("Repeated").
Added support for arbitrarily nested action spaces.
Added experimental GTrXL (Transformer/Attention net) support to RLlib + learning tests for PPO and IMPALA.
QMIX now supports complex observation spaces.

Retire use_pytorch and eager flags in configs and replace these with framework=[tf|tfe|torch].
Deprecate PolicyOptimizers in favor of the new distributed execution API.
Retired support for Model(V1) class. Custom Models should now only use the ModelV2 API. There is still a warning when using ModelV1, which will be changed into an error message in the next release.
Retired TupleActions (in favor of arbitrarily nested action Spaces).

There is now a Dataset API for handling large datasets with RaySGD. (#7839)
You can now filter by an average of the last results using the ExperimentAnalysis tool (#8445).
BayesOptSearch received numerous contributions, enabling preliminary random search and warm starting. (#8541, #8486, #8488)

tune.report is now the right way to use the Tune function API. tune.track is deprecated (#8388)

New APIs to inspect and manage Serve objects:
- serve.list_backends and serve.list_endpoints (#8737)
- serve.delete_backend and serve.delete_endpoint (#8252, #8256)
serve.create_endpoint now requires specifying the backend directly. You can remove serve.set_traffic if there's only one backend per endpoint. (#8764)
serve.init API cleanup, the following options were removed:
- blocking, ray_init_kwargs, start_server (#8747, #8447, #8620)
serve.init now supports namespacing with name. You can run multiple serve clusters with different names on the same ray cluster. (#8449)
You can specify session affinity when splitting traffic with backends using X-SERVE-SHARD-KEY HTTP header. (#8449)
Various documentation improvements. Highlights:
- A new section on how to perform A/B testing and incremental rollout (#8741)
- Tutorial for batch inference (#8490)
- Instructions for specifying GPUs and resources (#8495)

The Machine View of the dashboard now shows information about GPU utilization such as:
- Average GPU/GRAM utilization at a node and cluster level
- Worker-level information about how many GPUs each worker is assigned as well as its GRAM use.
The dashboard has a new Memory View tab that should be very useful for debugging memory issues. It has:
- Information about objects in the Ray object store, including size and call-site
- Information about reference counts and what is keeping an object pinned in the Ray object store.

IDLE workers get automatically sorted to the end of the worker list in the Machine View

Improved logging output. Errors are more clearly propagated and excess output has been reduced. (#7198, #8751, #8753)
Added support for k8s services.

Windows wheels are now available for basic experimental usage (via ray.init()).
Windows support is currently unstable. Unusual, unattended, or production usage is not recommended.
Various functionality may still lack support, including Ray Serve, Ray SGD, the autoscaler, the dashboard, non-ASCII file paths, etc.
Please check the latest nightly wheels & known issues (#9114), and let us know if any issue you encounter has not yet been addressed.
Wheels are available for Python 3.6, 3.7, and 3.8. (#8369)
redis-py has been patched for Windows sockets. (#8386)