Ray 1.0.0

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

Autoscaler

• 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

Dashboard & Metrics

• 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

RLlib

• 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.

Tune

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)

RaySGD:

• Creator functions are subsumed by the TrainingOperator API (#10321)
• Training happens on actors by default (#10539)

Serve

• 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)

Thanks

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

@MissiontoMars, @ijrsvt, @desktable, @kfstorm, @lixin-wei, @Yard1, @chaokunyang, @justinkterry, @pxc, @ericl, @WangTaoTheTonic, @carlos-aguayo, @sven1977, @gabrieleoliaro, @alanwguo, @aryairani, @kishansagathiya, @barakmich, @rkube, @SongGuyang, @qicosmos, @ffbin, @PidgeyBE, @sumanthratna, @yushan111, @juliusfrost, @edoakes, @mehrdadn, @Basasuya, @icaropires, @michaelzhiluo, @fyrestone, @robertnishihara, @yncxcw, @oliverhu, @yiranwang52, @ChuaCheowHuan, @raphaelavalos, @suquark, @krfricke, @pcmoritz, @stephanie-wang, @hekaisheng, @zhijunfu, @Vysybyl, @wuisawesome, @sanderland, @richardliaw, @simon-mo, @janblumenkamp, @zhuohan123, @AmeerHajAli, @iamhatesz, @mfitton, @noahshpak, @maximsmol, @weepingwillowben, @raulchen, @09wakharet, @ashione, @henktillman, @architkulkarni, @rkooo567, @zhe-thoughts, @amogkam, @kisuke95, @clarkzinzow, @holli, @raoul-khour-ts