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Ray v1.0.1 Release Notes

• Ray 1.0.1

  🚀 Ray 1.0.1 is now officially released!

  Highlights

  • If you're migrating from Ray < 1.0.0, be sure to check out the 1.0 Migration Guide.
  • 🐳 Autoscaler is now docker by default.
  • RLLib features multiple new environments.
  • 🐳 Tune supports population based bandits, checkpointing in Docker, and multiple usability improvements.
  • 👍 SGD supports PyTorch Lightning
  • 🐎 All of Ray's components and libraries have improved performance, scalability, and stability.

  Core

  • 1.0 Migration Guide.
  • 🛠 Many bug fixes and optimizations in GCS.
  • 💅 Polishing of the Placement Group API.
  • 👌 Improved Java language support

  RLlib

  • ➕ Added documentation for Curiosity exploration module (#11066).
  • ➕ Added RecSym environment wrapper (#11205).
  • ➕ Added Kaggle’s football environment (multi-agent) wrapper (#11249).
  • 👷 Multiple bug fixes: GPU related fixes for SAC (#11298), MARWIL, all example scripts run on GPU (#11105), lifted limitation on 2³¹ timesteps (#11301), fixed eval workers for ES and ARS (#11308), fixed broken no-eager-no-workers mode (#10745).
  • 👌 Support custom MultiAction distributions (#11311).
  • 👷 No environment is created on driver (local worker) if not necessary (#11307).
  • ➕ Added simple SampleCollector class for Trajectory View API (#11056).
  • 📄 Code cleanup: Docstrings and type annotations for Exploration classes (#11251), DQN (#10710), MB-MPO algorithm, SAC algorithm (#10825).

  Serve

  • API: Serve will error when serve_client is serialized. (#11181)
  • Performance: serve_client.get_handle("endpoint") will now get a handle to nearest node, increasing scalability in distributed mode. (#11477)
  • ⚡️ Doc: Added FAQ page and updated architecture page (#10754, #11258)
  • ✅ Testing: New distributed tests and benchmarks are added (#11386)
  • 🏁 Testing: Serve now run on Windows (#10682)

  SGD

  • 👍 Pytorch Lightning integration is now supported (#11042)
  • 👌 Support num_steps continue training (#11142)
  • Callback API for SGD+Tune (#11316)

  Tune

  • 🆕 New Algorithm: Population-based Bandits (#11466)
  • tune.with_parameters(), a wrapper function to pass arbitrary objects through the object store to trainables (#11504)
  • Strict metric checking - by default, Tune will now error if a result dict does not include the optimization metric as a key. You can disable this with TUNE_DISABLE_STRICT_METRIC_CHECKING (#10972)
  • 🐳 Syncing checkpoints between multiple Docker containers on a cluster is now supported with the DockerSyncer (#11035)
  • ➕ Added type hints (#10806)
  • Trials are now dynamically created (instead of created up front) (#10802)
  • Use tune.is_session_enabled() in the Function API to toggle between Tune and non-tune code (#10840)
  • 👌 Support hierarchical search spaces for hyperopt (#11431)
  • 👍 Tune function API now also supports yield and return statements (#10857)
  • 👍 Tune now supports callbacks with tune.run(callbacks=... (#11001)
  • 0️⃣ By default, the experiment directory will be dated (#11104)
  • 👷 Tune now supports reuse_actors for function API, which can largely accelerate tuning jobs.

  Thanks

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

  @acxz, @Gekho457, @allenyin55, @AnesBenmerzoug, @michaelzhiluo, @SongGuyang, @maximsmol, @WangTaoTheTonic, @Basasuya, @sumanthratna, @juliusfrost, @maxco2, @Xuxue1, @jparkerholder, @AmeerHajAli, @raulchen, @justinkterry, @herve-alanaai, @richardliaw, @raoul-khour-ts, @C-K-Loan, @mattearllongshot, @robertnishihara, @internetcoffeephone, @Servon-Lee, @clay4444, @fangyeqing, @krfricke, @ffbin, @akotlar, @rkooo567, @chaokunyang, @PidgeyBE, @kfstorm, @barakmich, @amogkam, @edoakes, @ashione, @jseppanen, @ttumiel, @desktable, @pcmoritz, @ingambe, @ConeyLiu, @wuisawesome, @fyrestone, @oliverhu, @ericl, @weepingwillowben, @rkube, @alanwguo, @architkulkarni, @lasagnaphil, @rohitrawat, @ThomasLecat, @stephanie-wang, @suquark, @ijrsvt, @VishDev12, @Leemoonsoo, @scottwedge, @sven1977, @yiranwang52, @carlos-aguayo, @mvindiola1, @zhongchun, @mfitton, @simon-mo

Previous changes from v1.0.0

• 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)
  • 0️⃣ 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)
  • 0️⃣ 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

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