ray-project/ray ray-1.6.0

Ray-1.6.0

on GitHub

Highlights

• Runtime Environments are ready for general use! This feature enables you to dynamically specify per-task, per-actor and per-job dependencies, including a working directory, environment variables, pip packages and conda environments. Install it with pip install -U 'ray[default]'.
• Ray Dataset is now in alpha! Dataset is an interchange format for distributed datasets, powered by Arrow. You can also use it for a basic Ray native data processing experience. Check it out here.
• Ray Lightning v0.1 has been released! You can install it via pip install ray-lightning. Ray Lightning is a library of PyTorch Lightning plugins for distributed training using Ray. Features:
  • Enables quick and easy parallel training
  • Supports PyTorch DDP, Horovod, and Sharded DDP with Fairscale
  • Integrates with Ray Tune for hyperparameter optimization and is compatible with Ray Client
• pip install ray now has a significantly reduced set of dependencies. Features such as the dashboard, the cluster launcher, runtime environments, and observability metrics may require pip install -U 'ray[default]' to be enabled. Please report any issues on Github if this is an issue!

Ray Autoscaler

🎉 New Features:

• The Ray autoscaler now supports TPUs on GCP. Please refer to this example for spinning up a simple TPU cluster. (#17278)

💫Enhancements:

• Better AWS networking configurability (#17236 #17207 #14080)
• Support for running autoscaler without NodeUpdaters (#17194, #17328)

🔨 Fixes:

• Code clean up and corrections to downscaling policy (#17352)
• Docker file sync fix (#17361)

Ray Client

💫Enhancements:

• Updated docs for client server ports and ray.init(ray://) (#17003, #17333)
• Better error handling for deserialization failures (#17035)

🔨 Fixes:

• Fix for server proxy not working with non-default redis passwords (#16885)

Ray Core

🎉 New Features:

• Runtime Environments are ready for general use!
  • Specify a working directory to upload your local files to all nodes in your cluster.
  • Specify different conda and pip dependencies for your tasks and actors and have them installed on the fly.

🔨 Fixes:

• Fix plasma store bugs for better data processing stability (#16976, #17135, #17140, #17187, #17204, #17234, #17396, #17550)
• Fix a placement group bug where CUDA_VISIBLE_DEVICES were not properly detected (#17318)
• Improved Ray stacktrace messages. (#17389)
• Improved GCS stability and scalability (#17456, #17373, #17334, #17238, #17072)

🏗 Architecture refactoring:

• Plasma store refactor for better testability and extensibility. (#17332, #17313, #17307)

Ray Data Processing

Ray Dataset is now in alpha! Dataset is an interchange format for distributed datasets, powered by Arrow. You can also use it for a basic Ray native data processing experience. Check it out here.

RLLib

🎉 New Features:

• Support for RNN/LSTM models with SAC (new agent: "RNNSAC"). Shoutout to ddworak94! (#16577)
• Support for ONNX model export (tf and torch). (#16805)
• Allow Policies to be added to/removed from a Trainer on-the-fly. (#17566)

🔨 Fixes:

• Fix for view requirements captured during compute actions test pass. Shoutout to Chris Bamford (#15856)

• Issues: 17397, 17425, 16715, 17174. When on driver, Torch|TFPolicy should not use ray.get_gpu_ids() (b/c no GPUs assigned by ray). (#17444)

• Other bug fixes: #15709, #15911, #16083, #16716, #16744, #16896, #16999, #17010, #17014, #17118, #17160, #17315, #17321, #17335, #17341, #17356, #17460, #17543, #17567, #17587

🏗 Architecture refactoring:

• CV2 to Skimage dependency change (CV2 still supported). Shoutout to Vince Jankovics. (#16841)
• Unify tf and torch policies wrt. multi-GPU handling: PPO-torch is now 33% faster on Atari and 1 GPU. (#17371)
• Implement all policy maps inside RolloutWorkers to be LRU-caches so that a large number of policies can be added on-the-fly w/o running out of memory. (#17031)
• Move all tf static-graph code into DynamicTFPolicy, such that policies can be deleted and their tf-graph is GC'd. (#17169)
• Simplify multi-agent configs: In most cases, creating dummy envs (only to retrieve spaces) are no longer necessary. (#16565, #17046)

📖Documentation:

• Examples scripts do-over (shoutout to Stefan Schneider for this initiative).
• Example script: League-based self-play with "open spiel" env. (#17077)
• Other doc improvements: #15664 (shoutout to kk-55), #17030, #17530

Tune

🎉 New Features:

• Dynamic trial resource allocation with ResourceChangingScheduler (#16787)
• It is now possible to use a define-by-run function to generate a search space with OptunaSearcher (#17464)

💫Enhancements:

• String names of searchers/schedulers can now be used directly in tune.run (#17517)
• Filter placement group resources if not in use (progress reporting) (#16996)
• Add unit tests for flatten_dict (#17241)

🔨Fixes:

• Fix HDFS sync down template (#17291)
• Re-enable TensorboardX without Torch installed (#17403)

📖Documentation:

• LightGBM integration (#17304)
• Other documentation improvements: #17407 (shoutout to amavilla), #17441, #17539, #17503

SGD

🎉 New Features:

• We have started initial development on a new RaySGD v2! We will be rolling it out in a future version of Ray. See the documentation here. (#17536, #17623, #17357, #17330, #17532, #17440, #17447, #17300, #17253)

💫Enhancements:

• Placement Group support for TorchTrainer (#17037)

Serve

🎉 New Features:

• Add Ray API stability annotations to Serve, marking many serve.\* APIs as Stable (#17295)
• Support runtime_env's working_dir for Ray Serve (#16480)

🔨Fixes:

• Fix FastAPI's response_model not added to class based view routes (#17376)
• Replace backend with deployment in metrics & logging (#17434)

🏗Stability Enhancements:

• Run Ray Serve with multi & single deployment large scale (1K+ cores) test running nightly (#17310, #17411, #17368, #17026, #17277)

Thanks

Many thanks to all who contributed to this release:

@suquark, @xwjiang2010, @clarkzinzow, @kk-55, @mGalarnyk, @pdames, @Souphis, @edoakes, @sasha-s, @iycheng, @stephanie-wang, @antoine-galataud, @scv119, @ericl, @amogkam, @ckw017, @wuisawesome, @krfricke, @vakker, @qingyun-wu, @Yard1, @juliusfrost, @DmitriGekhtman, @clay4444, @mwtian, @corentinmarek, @matthewdeng, @simon-mo, @pcmoritz, @qicosmos, @architkulkarni, @rkooo567, @navneet066, @dependabot[bot], @jovany-wang, @kombuchafox, @thomasjpfan, @kimikuri, @Ivorforce, @franklsf95, @MissiontoMars, @lantian-xu, @duburcqa, @ddworak94, @ijrsvt, @sven1977, @kira-lin, @SongGuyang, @kfstorm, @Rohan138, @jamesmishra, @amavilla, @fyrestone, @lixin-wei, @stefanbschneider, @jiaodong, @richardliaw, @WangTaoTheTonic, @chenk008, @Catch-Bull, @Bam4d