Ray 1.0

We're happy to announce the release of Ray 1.0, an important step towards the goal of providing a universal API for distributed computing.

To learn more about Ray 1.0, check out our blog post and whitepaper.

Ray Core

The ray.init() and ray start commands have been cleaned up to remove deprecated arguments
The Ray Java API is now stable
Improved detection of Docker CPU limits
Add support and documentation for Dask-on-Ray and MARS-on-Ray: https://docs.ray.io/en/master/ray-libraries.html
Placement groups for fine-grained control over scheduling decisions: https://docs.ray.io/en/latest/placement-group.html.
New architecture whitepaper: https://docs.ray.io/en/master/whitepaper.html

Support for multiple instance types in the same cluster: https://docs.ray.io/en/master/cluster/autoscaling.html
Support for specifying GPU/accelerator type in @ray.remote

Improvements to the memory usage tab and machine view
The dashboard now supports visualization of actor states
Support for Prometheus metrics reporting: https://docs.ray.io/en/latest/ray-metrics.html

Two Model-based RL algorithms were added: MB-MPO (“Model-based meta-policy optimization”) and “Dreamer”. Both algos were benchmarked and are performing comparably to the respective papers’ reported results.
A “Curiosity” (intrinsic motivation) module was added via RLlib’s Exploration API and benchmarked on a sparse-reward Unity3D environment (Pyramids).
Added documentation for the Distributed Execution API.
Removed (already soft-deprecated) APIs: Model(V1) class, Trainer config keys, some methods/functions. Where you would see a warning previously when using these, there will be an error thrown now.
Added DeepMind Control Suite examples.

Breaking changes:

Multiple tune.run parameters have been deprecated: ray_auto_init, run_errored_only, global_checkpoint_period, with_server (#10518)
tune.run(upload_dir, sync_to_cloud, sync_to_driver, sync_on_checkpoint have been moved to tune.SyncConfig [docs] (#10518)

New APIs:

mode, metric, time_budget parameters for tune.run (#10627, #10642)
Search Algorithms now share a uniform API: (#10621, #10444). You can also use the new create_scheduler/create_searcher shim layer to create search algorithms/schedulers via string, reducing boilerplate code (#10456).
Native callbacks for: MXNet, Horovod, Keras, XGBoost, PytorchLightning (#10533, #10304, #10509, #10502, #10220)
PBT runs can be replayed with PopulationBasedTrainingReplay scheduler (#9953)
Search Algorithms are saved/resumed automatically (#9972)
New Optuna Search Algorithm docs (#10044)
Tune now can sync checkpoints across Kubernetes pods (#10097)
Failed trials can be rerun with tune.run(resume="run_errored_only") (#10060)

Other Changes:

Trial outputs can be saved to file via tune.run(log_to_file=...) (#9817)
Trial directories can be customized, and default trial directory now includes trial name (#10608, #10214)
Improved Experiment Analysis API (#10645)
Support for Multi-objective search via SigOpt Wrapper (#10457, #10446)
BOHB Fixes (#10531, #10320)
Wandb improvements + RLlib compatibility (#10950, #10799, #10680, #10654, #10614, #10441, #10252, #8521)
Updated documentation for FAQ, Tune+serve, search space API, lifecycle (#10813, #10925, #10662, #10576, #9713, #10222, #10126, #9908)

serve.client API makes it easy to appropriately manage lifetime for multiple Serve clusters. (#10460)
Serve APIs are fully typed. (#10205, #10288)
Backend configs are now typed and validated via Pydantic. (#10559, #10389)
Progress towards application level backend autoscaler. (#9955, #9845, #9828)
New architecture page in documentation. (#10204)

We thank all the contributors for their contribution to this release!