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 2^31 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)
• 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