ray-1.0.1
版本发布时间: 2020-11-10 10:50:05
ray-project/ray最新发布版本:ray-2.37.0(2024-09-25 07:37:52)
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
andreturn
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