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Ray-1.11.0

09 Mar 01:45
@mwtian mwtian
ray-1.11.0
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Ray-1.11.0

Highlights

🎉 Ray no longer starts Redis by default. Cluster metadata previously stored in Redis is stored in the GCS now.

Ray Autoscaler

🎉 New Features

  • AWS Cloudwatch dashboard support #20266

💫 Enhancements

  • Kuberay autoscaler prototype #21086

🔨 Fixes

  • Ray.autoscaler.sdk import issue #21795

Ray Core

🎉 New Features

  • Set actor died error message in ActorDiedError #20903
  • Event stats is enabled by default #21515

🔨 Fixes

  • Better support for nested tasks
  • Fixed 16GB mac perf issue by limit the plasma store size to 2GB #21224
  • Fix SchedulingClassInfo.running_tasks memory leak #21535
  • Round robin during spread scheduling #19968

🏗 Architecture refactoring

  • Refactor scheduler resource reporting public APIs #21732
  • Refactor ObjectManager wait logic to WaitManager #21369

Ray Data Processing

🎉 New Features

  • More powerful to_torch() API, providing more control over the GPU batch format. (#21117)

🔨 Fixes

  • Fix simple Dataset sort generating only 1 non-empty block. (#21588)
  • Improve error handling across sorting, groupbys, and aggregations. (#21610, #21627)
  • Fix boolean tensor column representation and slicing. (#22358)

RLlib

🎉 New Features

  • Better utils for flattening complex inputs and enable prev-actions for LSTM/attention for complex action spaces. (#21330)
  • MultiAgentEnv pre-checker (#21476)
  • Base env pre-checker. (#21569)

🔨 Fixes

  • Better defaults for QMix (#21332)
  • Fix contrib/MADDPG + pettingzoo coop-pong-v4. (#21452)
  • Fix action unsquashing causes inf/NaN actions for unbounded action spaces. (#21110)
  • Ignore PPO KL-loss term completely if kl-coeff == 0.0 to avoid NaN values (#21456)
  • unsquash_action and clip_action (when None) cause wrong actions computed by Trainer.compute_single_action. (#21553)
  • Conv2d default filter tests and add default setting for 96x96 image obs space. (#21560)
  • Bing back and fix offline RL(BC & MARWIL) learning tests. (#21574, #21643)
  • SimpleQ should not use a prio. replay buffer. (#21665)
  • Fix video recorder env wrapper. Added test case. (#21670)

🏗 Architecture refactoring

  • Decentralized multi-agent learning (#21421)
  • Preparatory PR for multi-agent multi-GPU learner (alpha-star style) (#21652)

Ray Workflow

🔨 Fixes

  • Fixed workflow recovery issue due to a bug of dynamic output #21571

Tune

🎉 New Features

  • It is now possible to load all evaluated points from an experiment into a Searcher (#21506)
  • Add CometLoggerCallback (#20766)

💫 Enhancements

  • Only sync the checkpoint folder instead of the entire trial folder for cloud checkpoint. (#21658)
  • Add test for heterogeneous resource request deadlocks (#21397)
  • Remove unused return_or_clean_cached_pg (#21403)
  • Remove TrialExecutor.resume_trial (#21225)
  • Leave only one canonical way of stopping a trial (#21021)

🔨 Fixes

  • Replace deprecated running_sanity_check with sanity_checking in PTL integration (#21831)
  • Fix loading an ExperimentAnalysis object without a registered Trainable (#21475)
  • Fix stale node detection bug (#21516)
  • Fixes to allow tune/tests/test_commands.py to run on Windows (#21342)
  • Deflake PBT tests (#21366)
  • Fix dtype coercion in tune.choice (#21270)

📖 Documentation

  • Fix typo in schedulers.rst (#21777)

Train

🎉 New Features

  • Add PrintCallback (#21261)
  • Add MLflowLoggerCallback(#20802)

💫 Enhancements

🔨 Fixes

📖 Documentation

  • Documentation and example fixes (#​​21761, #21689, #21464)

Serve

🎉 New Features

  • Checkout our revampt end-to-end tutorial that walks through the deployment journey! (#20765)

🔨 Fixes

  • Warn when serve.start() with different options (#21562)
  • Detect http.disconnect and cancel requests properly (#21438)

Thanks
Many thanks to all those who contributed to this release!
@isaac-vidas, @wuisawesome, @stephanie-wang, @jon-chuang, @xwjiang2010, @jjyao, @MissiontoMars, @qbphilip, @yaoyuan97, @gjoliver, @Yard1, @rkooo567, @talesa, @czgdp1807, @DN6, @sven1977, @kfstorm, @krfricke, @simon-mo, @hauntsaninja, @pcmoritz, @JamieSlome, @chaokunyang, @jovany-wang, @sidward14, @DmitriGekhtman, @ericl, @mwtian, @jwyyy, @clarkzinzow, @hckuo, @vakker, @HuangLED, @iycheng, @edoakes, @shrekris-anyscale, @robertnishihara, @avnishn, @mickelliu, @ndrwnaguib, @ijrsvt, @Zyiqin-Miranda, @bveeramani, @SongGuyang, @n30111, @WangTaoTheTonic, @suquark, @richardliaw, @qicosmos, @scv119, @architkulkarni, @lixin-wei, @Catch-Bull, @acxz, @benblack769, @clay4444, @amogkam, @marin-ma, @maxpumperla, @jiaodong, @mattip, @isra17, @raulchen, @wilsonwang371, @carlogrisetti, @ashione, @matthewdeng

Contributors

ericl, pcmoritz, and 65 other contributors
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Ray-1.10.0

04 Feb 19:23
@architkulkarni architkulkarni
ray-1.10.0
5ea5653
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Ray-1.10.0

Highlights

  • 🎉 Ray Windows support is now in beta – a significant fraction of the Ray test suite is now passing on Windows. We are eager to learn about your experience with Ray 1.10 on Windows, please file issues you encounter at https://github.com/ray-project/ray/issues. In the upcoming releases we will spend more time on making Ray Serve and Runtime Environment tests pass on Windows and on polishing things.

Ray Autoscaler

💫Enhancements:

  • Add autoscaler update time to prometheus metrics (#20831)
  • Fewer non terminated nodes calls in autoscaler update (#20359, #20623)

🔨 Fixes:

  • GCP TPU autoscaling fix (#20311)
  • Scale-down stability fix (#21204)
  • Report node launch failure in driver logs (#20814)

Ray Client

💫Enhancements

  • Client task options are encoded with pickle instead of json (#20930)

Ray Core

🎉 New Features:

  • runtime_env’s pip field now installs pip packages in your existing environment instead of installing them in a new isolated environment. (#20341)

🔨 Fixes:

  • Fix bug where specifying runtime_env conda/pip per-job using local requirements file using Ray Client on a remote cluster didn’t work (#20855)
  • Security fixes for log4j2 – the log4j2 version has been bumped to 2.17.1 (#21373)

💫Enhancements:

  • Allow runtime_env working_dir and py_modules to be pathlib.Path type (#20853, #20810)
  • Add environment variable to skip local runtime_env garbage collection (#21163)
  • Change runtime_env error log to debug log (#20875)
  • Improved reference counting for runtime_env resources (#20789)

🏗 Architecture refactoring:

  • Refactor runtime_env to use protobuf for multi-language support (#19511)

📖Documentation:

Ray Data Processing

🎉 New Features:

  • Added stats framework for debugging Datasets performance (#20867, #21070)
  • [Dask-on-Ray] New config helper for enabling the Dask-on-Ray scheduler (#21114)

💫Enhancements:

  • Reduce memory usage during when converting to a Pandas DataFrame (#20921)

🔨 Fixes:

  • Fix slow block evaluation when splitting (#20693)
  • Fix boundary sampling concatenation on non-uniform blocks (#20784)
  • Fix boolean tensor column slicing (#20905)

🏗 Architecture refactoring:

  • Refactor table block structure to support more tabular block formats (#20721)

RLlib

🎉 New Features:

🏗 Architecture refactoring:

  • Evaluation: Support evaluation setting that makes sure train doesn't ever have to wait for eval to finish (b/c of long episodes). (#20757); Always attach latest eval metrics. (#21011)
  • Soft-deprecate build_trainer() utility function in favor of sub-classing Trainer directly (and overriding some of its methods). (#20635, #20636, #20633, #20424, #20570, #20571, #20639, #20725)
  • Experimental no-flatten option for actions/prev-actions. (#20918)
  • Use SampleBatch instead of an input dict whenever possible. (#20746)
  • Switch off Preprocessors by default for PGTrainer (experimental). (#21008)
  • Toward a Replay Buffer API (cleanups; docstrings; renames; move into rllib/execution/buffers dir) (#20552)

📖Documentation:

  • Overhaul of auto-API reference pages. (#19786, #20537, #20538, #20486, #20250)
  • README and RLlib landing page overhaul (#20249).
  • Added example containing code to compute an adapted (time-dependent) GAE used by the PPO algorithm (#20850).

🔨 Fixes:

Tune

🎉 New Features:

  • Introduce TrialCheckpoint class, making checkpoint down/upload easie (#20585)
  • Add random state to BasicVariantGenerator (#20926)
  • Multi-objective support for Optuna (#20489)

💫Enhancements:

  • Add set_max_concurrency to Searcher API (#20576)
  • Allow for tuples in _split_resolved_unresolved_values. (#20794)
  • Show the name of training func, instead of just ImplicitFunction. (#21029)
  • Enforce one future at a time for any given trial at any given time. (#20783)
    move on_no_available_trials to a subclass under runner (#20809)
  • Clean up code (#20555, #20464, #20403, #20653, #20796, #20916, #21067)
  • Start restricting TrialRunner/Executor interface exposures. (#20656)
  • TrialExecutor should not take in Runner interface. (#20655)

🔨Fixes:

  • Deflake test_tune_restore.py (#20776)
  • Fix best_trial_str for nested custom parameter columns (#21078)
  • Fix checkpointing error message on K8s (#20559)
  • Fix testResourceScheduler and testMultiStepRun. (#20872)
  • Fix tune cloud tests for function and rllib trainables (#20536)
  • Move _head_bundle_is_empty after conversion (#21039)
  • Elongate test_trial_scheduler_pbt timeout. (#21120)

Train

🔨Fixes:

  • Ray Train environment variables are automatically propagated and do not need to be manually set on every node (#20523)
  • Various minor fixes and improvements (#20952, #20893, #20603, #20487)
    📖Documentation:
  • Update saving/loading checkpoint docs (#20973). Thanks @jwyyy!
  • Various minor doc updates (#20877, #20683)

Serve

💫Enhancements:

  • Add validation to Serve AutoscalingConfig class (#20779)
  • Add Serve metric for HTTP error codes (#21009)

🔨Fixes:

  • No longer create placement group for deployment with no resources (#20471)
  • Log errors in deployment initialization/configuration user code (#20620)

Jobs

🎉 New Features:

  • Logs can be streamed from job submission server with ray job logs command (#20976)
  • Add documentation for ray job submission (#20530)
  • Propagate custom headers field to JobSubmissionClient and apply to all requests (#20663)

🔨Fixes:

  • Fix job serve accidentally creates local ray processes instead of connecting (#20705)

💫Enhancements:

  • [Jobs] Update CLI examples to use the same setup (#20844)

Thanks

Many thanks to all those who contributed to this release!

@dmatrix, @suquark, @tekumara, @jiaodong, @jovany-wang, @avnishn, @simon-mo, @iycheng, @SongGuyang, @ArturNiederfahrenhorst, @wuisawesome, @kfstorm, @matthewdeng, @jjyao, @chenk008, @Sertingolix, @larrylian, @czgdp1807, @scv119, @duburcqa, @runedog48, @Yard1, @robertnishihara, @geraint0923, @amogkam, @DmitriGekhtman, @ijrsvt, @kk-55, @lixin-wei, @mvindiola1, @hauntsaninja, @sven1977, @Hankpipi, @qbphilip, @hckuo, @newmanwang, @clay4444, @edoakes, @liuyang-my, @iasoon, @WangTaoTheTonic, @fgogolli, @dproctor, @gramhagen, @krfricke, @richardliaw, @bveeramani, @pcmoritz, @ericl, @simonsays1980, @carlogrisetti, @stephanie-wang, @AmeerHajAli, @mwtian, @xwjiang2010, @shrekris-anyscale, @n30111, @lchu-ibm, @Scalsol, @seonggwonyoon, @gjoliver, @qicosmos, @xychu, @iamhatesz, @architkulkarni, @jwyyy, @rkooo567, @mattip, @ckw017, @MissiontoMars, @clarkzinzow

Contributors

ericl, pcmoritz, and 69 other contributors
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Ray-1.9.2

11 Jan 19:46
@architkulkarni architkulkarni
ray-1.9.2
ef593fe
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Ray-1.9.2

Patch release to bump the log4j version from 2.16.0 to 2.17.0. This resolves the security issue CVE-2021-45105.

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Ray-1.9.1

22 Dec 00:59
@architkulkarni architkulkarni
ray-1.9.1
2cdbf97
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Ray-1.9.1

Patch release to bump the log4j2 version from 2.14 to 2.16. This resolves the security vulnerabilities https://nvd.nist.gov/vuln/detail/CVE-2021-44228 and https://nvd.nist.gov/vuln/detail/CVE-2021-45046.

No library or core changes included.

Thanks @seonggwonyoon and @ijrsvt for contributing the fixes!

Contributors

ijrsvt and seonggwonyoon
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Ray-1.9.0

03 Dec 19:08
@fishbone fishbone
ray-1.9.0
92599d9
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Ray-1.9.0

Highlights

  • Ray Train is now in beta! If you are using Ray Train, we’d love to hear your feedback here!
  • Ray Docker images for multiple CUDA versions are now provided (#19505)! You can specify a -cuXXX suffix to pick a specific version.
    • ray-ml:cpu images are now deprecated. The ray-ml images are only built for GPU.
  • Ray Datasets now supports groupby and aggregations! See the groupby API and GroupedDataset docs for usage.
  • We are making continuing progress in improving Ray stability and usability on Windows. We encourage you to try it out and report feedback or issues at https://github.com/ray-project/ray/issues.
  • We are launching a Ray Job Submission server + CLI & SDK clients to make it easier to submit and monitor Ray applications when you don’t want an active connection using Ray Client. This is currently in alpha, so the APIs are subject to change, but please test it out and file issues / leave feedback on GitHub & discuss.ray.io!

Ray Autoscaler

💫Enhancements:

  • Graceful termination of Ray nodes prior to autoscaler scale down (#20013)
  • Ray Clusters on AWS are colocated in one Availability Zone to reduce costs & latency (#19051)

Ray Client

🔨 Fixes:

  • ray.put on a list of of objects now returns a single object ref (​​#19737)

Ray Core

🎉 New Features:

💫Enhancements:

🔨 Fixes:

  • Fix runtime_env hanging issues (#19823)
  • Fix specifying runtime env in @ray.remote decorator with Ray Client (#19626)
  • Threaded actor / core worker / named actor race condition fixes (#19751, #19598, #20178, #20126)

📖Documentation:

  • New page “Handling Dependencies”
  • New page “Ray Job Submission: Going from your laptop to production”

Ray Java

API Changes:

Note:

  • Use Ray.getActor(name, namespace) API to get a named actor between jobs instead of Ray.getGlobalActor(name).
  • Use PlacementGroup.getPlacementGroup(name, namespace) API to get a placement group between jobs instead of PlacementGroup.getGlobalPlacementGroup(name).

Ray Datasets

🎉 New Features:

🔨 Fixes:

  • Support custom CSV write options (#19378)

🏗 Architecture refactoring:

  • Optimized block compaction (#19681)

Ray Workflow

🎉 New Features:

  • Workflow right now support events (#19239)
  • Allow user to specify metadata for workflow and steps (#19372)
  • Allow in-place run a step if the resources match (#19928)

🔨 Fixes:

  • Fix the s3 path issue (#20115)

RLlib

🏗 Architecture refactoring:

  • “framework=tf2” + “eager_tracing=True” is now (almost) as fast as “framework=tf”. A check for tf2.x eager re-traces has been added making sure re-tracing does not happen outside the initial function calls. All CI learning tests (CartPole, Pendulum, FrozenLake) are now also run as framework=tf2. (#19273, #19981, #20109)
  • Prepare deprecation of build_trainer/build_(tf_)?policy utility functions. Instead, use sub-classing of Trainer or Torch|TFPolicy. POCs done for PGTrainer, PPO[TF|Torch]Policy. (#20055, #20061)
  • V-trace (APPO & IMPALA): Don’t drop last ts can be optionally switch on. The default is still to drop it, but this may be changed in a future release. (#19601)
  • Upgrade to gym 0.21. (#19535)

🔨 Fixes:

📖Documentation:

Tune

💫Enhancements:

🔨Fixes:

  • Documentation fixes (#20130, #19791)
  • Tutorial fixes (#20065, #19999)
  • Drop 0 value keys from PGF (#20279)
  • Fix shim error message for scheduler (#19642)
  • Avoid looping through _live_trials twice in _get_next_trial. (#19596)
  • clean up legacy branch in update_avail_resources. (#20071)
  • fix Train/Tune integration on Client (#20351)

Train

Ray Train is now in Beta! The beta version includes various usability improvements for distributed PyTorch training and checkpoint management, support for Ray Client, and an integration with Ray Datasets for distributed data ingest.

Check out the docs here, and the migration guide from Ray SGD to Ray Train here. If you are using Ray Train, we’d love to hear your feedback here!

🎉 New Features:

  • New train.torch.prepare_model(...) and train.torch.prepare_data_loader(...) API to automatically handle preparing your PyTorch model and DataLoader for distributed training (#20254).
  • Checkpoint management and support for custom checkpoint strategies (#19111).
  • Easily configure what and how many checkpoints to save to disk.
  • Support for Ray Client (#20123, #20351).

💫Enhancements:

  • Simplify workflow for training with a single worker (#19814).
  • Ray Placement Groups are used for scheduling the training workers (#20091).
  • PACK strategy is used by default but can be changed by setting the TRAIN_ENABLE_WORKER_SPREAD environment variable.
  • Automatically unwrap Torch DDP model and convert to CPU when saving a model as checkpoint (#20333).

🔨Fixes:

  • Fix HorovodBackend to automatically detect NICs- thanks @tgaddair! (#19533).

📖Documentation:

  • Denote public facing APIs with beta stability (#20378)
  • Doc updates (#20271)

Serve

We would love to hear from you! Fill out the Ray Serve survey here.

🎉 New Features:

🔨Fixes:

  • Serve deployment functions or classes can take no parameters (#19708)
  • Replica slow start message is improved. You can now see whether it is slow to allocate resources or slow to run constructor. (#19431)
  • pip install ray[serve] will now install ray[default] as well. (#19570)

🏗 Architecture refactoring:

Dashboard

  • Ray Dashboard is now enabled on Windows! (#19575)

Thanks

Many thanks to all those who contributed to this release!
@krfricke, @stefanbschneider, @ericl, @nikitavemuri, @qicosmos, @worldveil, @triciasfu, @AmeerHajAli, @javi-redondo, @architkulkarni, @pdames, @clay4444, @mGalarnyk, @liuyang-my, @matthewdeng, @suquark, @rkooo567, @mwtian, @chenk008, @dependabot[bot], @iycheng, @jiaodong, @scv119, @oscarknagg, @Rohan138, @stephanie-wang, @Zyiqin-Miranda, @ijrsvt, @roireshef, @tkaymak, @simon-mo, @ashione, @jovany-wang, @zenoengine, @tgaddair, @11rohans, @amogkam, @zhisbug, @lchu-ibm, @shrekris-anyscale, @pcmoritz, @yiranwang52, @mattip, @sven1977, @Yard1, @DmitriGekhtman, @ckw017, @WangTaoTheTonic, @wuisawesome, @kcpevey, @kfstorm, @rhamnett, @renos, @TeoZosa, @SongGuyang, @clarkzinzow, @avnishn, @iasoon, @gjoliver, @jjyao, @xwjiang2010, @dmatrix, @edoakes, @czgdp1807, @heng2j, @sungho-joo, @lixin-wei

Contributors

ericl, pcmoritz, and 65 other contributors
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Ray-1.8.0

02 Nov 18:33
@xwjiang2010 xwjiang2010
ray-1.8.0
72fdf3b
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Ray-1.8.0

Highlights

  • Ray SGD has been rebranded to Ray Train! The new documentation landing page can be found here.
  • Ray Datasets is now in beta! The beta release includes a new integration with Ray Train yielding scalable ML ingest for distributed training. Check out the docs here, try it out for your ML ingest and batch inference workloads, and let us know how it goes!
  • This Ray release supports Apple Silicon (M1 Macs). Check out the installation instructions for more information!

Ray Autoscaler

🎉 New Features:

  • Fake multi-node mode for autoscaler testing (#18987)

💫Enhancements:

  • Improve unschedulable task warning messages by integrating with the autoscaler (#18724)

Ray Client

💫Enhancements

  • Use async rpc for remote call and actor creation (#18298)

Ray Core

💫Enhancements

🔨 Fixes:

Ray Data

Ray Datasets is now in beta! The beta release includes a new integration with Ray Train yielding scalable ML ingest for distributed training. It supports repeating and rewindowing pipelines, zipping two pipelines together, better cancellation of Datasets workloads, and many performance improvements. Check out the docs here, try it out for your ML ingest and batch inference workloads, and let us know how it goes!

🎉 New Features:

  • Ray Train integration (#17626)
  • Add support for repeating and rewindowing a DatasetPipeline (#19091)
  • .iter_epochs() API for iterating over epochs in a DatasetPipeline (#19217)
  • Add support for zipping two datasets together (#18833)
  • Transformation operations are now cancelled when one fails or the entire workload is killed (#18991)
  • Expose from_pandas()/to_pandas() APIs that accept/return plain Pandas DataFrames (#18992)
  • Customize compression, read/write buffer size, metadata, etc. in the IO layer (#19197)
  • Add spread resource prefix for manual round-robin resource-based task load balancing

💫Enhancements:

  • Minimal rows are now dropped when doing an equalized split (#18953)
  • Parallelized metadata fetches when reading Parquet datasets (#19211)

🔨 Fixes:

  • Tensor columns now properly support table slicing (#19534)
  • Prevent Datasets tasks from being captured by Ray Tune placement groups (#19208)
  • Empty datasets are properly handled in most transformations (#18983)

🏗 Architecture refactoring:

  • Tensor dataset representation changed to a table with a single tensor column (#18867)

RLlib

🎉 New Features:

  • Allow n-step > 1 and prioritized replay for R2D2 and RNNSAC agents. (18939)

🔨 Fixes:

  • Fix memory leaks in TF2 eager mode. (#19198)
  • Faster worker spaces inference if specified through configuration. (#18805)
  • Fix bug for complex obs spaces containing Box([2D shape]) and discrete components. (#18917)
  • Torch multi-GPU stats not protected against race conditions. (#18937)
  • Fix SAC agent with dict space. (#19101)
  • Fix A3C/IMPALA in multi-agent setting. (#19100)

🏗 Architecture refactoring:

  • Unify results dictionary returned from Trainer.train() across agents regardless of (tf or pytorch, multi-agent, multi-gpu, or algos that use >1 SGD iterations, e.g. ppo) (#18879)

Ray Workflow

🎉 New Features:

  • Introduce workflow.delete (#19178)

🔨Fixes:

  • Fix the bug which allow workflow step to be executed multiple times (#19090)

🏗 Architecture refactoring:

  • Object reference serialization is decoupled from workflow storage (#18328)

Tune

🎉 New Features:

  • PBT: Add burn-in period (#19321)

💫Enhancements:

  • Optional forcible trial cleanup, return default autofilled metrics even if Trainable doesn't report at least once (#19144)
  • Use queue to display JupyterNotebookReporter updates in Ray client (#19137)
  • Add resume="AUTO" and enhance resume error messages (#19181)
  • Provide information about resource deadlocks, early stopping in Tune docs (#18947)
  • Fix HEBOSearch installation docs (#18861)
  • OptunaSearch: check compatibility of search space with evaluated_rewards (#18625)
  • Add save and restore methods for searchers that were missing it & test (#18760)
  • Add documentation for reproducible runs (setting seeds) (#18849)
  • Depreciate max_concurrent in TuneBOHB (#18770)
  • Add on_trial_result to ConcurrencyLimiter (#18766)
  • Ensure arguments passed to tune remote_run match (#18733)
  • Only disable ipython in remote actors (#18789)

🔨Fixes:

  • Only try to sync driver if sync_to_driver is actually enabled (#19589)
  • sync_client: Fix delete template formatting (#19553)
  • Force no result buffering for hyperband schedulers (#19140)
  • Exclude trial checkpoints in experiment sync (#19185)
  • Fix how durable trainable is retained in global registry (#19223, #19184)
  • Ensure loc column in progress reporter is filled (#19182)
  • Deflake PBT Async test (#19135)
  • Fix Analysis.dataframe() documentation and enable passing of mode=None (#18850)

Ray Train (SGD)

Ray SGD has been rebranded to Ray Train! The new documentation landing page can be found here. Ray Train is integrated with Ray Datasets for distributed data loading while training, documentation available here.

🎉 New Features:

  • Ray Datasets Integration (#17626)

🔨Fixes:

  • Improved support for multi-GPU training (#18824, #18958)
  • Make actor creation async (#19325)

📖Documentation:

  • Rename Ray SGD v2 to Ray Train (#19436)
  • Added migration guide from Ray SGD v1 (#18887)

Serve

🎉 New Features:

  • Add ability to recover from a checkpoint on cluster failure (#19125)
  • Support kwargs to deployment constructors (#19023)

🔨Fixes:

  • Fix asyncio compatibility issue (#19298)
  • Catch spurious ConnectionErrors during shutdown (#19224)
  • Fix error with uris=None in runtime_env (#18874)
  • Fix shutdown logic with exit_forever (#18820)

🏗 Architecture refactoring:

Dashboard

🎉 New Features:

  • Basic support for the dashboard on Windows (#19319)

🔨Fixes:

  • Fix healthcheck issue causing the dashboard to crash under load (#19360)
  • Work around aiohttp 4.0.0+ issues (#19120)

🏗 Architecture refactoring:

  • Improve dashboard agent retry logic (#18973)

Thanks

Many thanks to all those who contributed to this release!
@rkooo567, @lchu-ibm, @scv119, @pdames, @suquark, @antoine-galataud, @sven1977, @mvindiola1, @krfricke, @ijrsvt, @sighingnow, @marload, @jmakov, @clay4444, @mwtian, @pcmoritz, @iycheng, @ckw017, @chenk008, @jovany-wang, @jjyao, @hauntsaninja, @franklsf95, @jiaodong, @wuisawesome, @odp, @matthewdeng, @duarteocarmo, @czgdp1807, @gjoliver, @mattip, @richardliaw, @max0x7ba, @Jasha10, @acxz, @xwjiang2010, @SongGuyang, @simon-mo, @zhisbug, @ccssmnn, @Yard1, @hazeone, @o0olele, @froody, @robertnishihara, @amogkam, @sasha-s, @xychu, @lixin-wei, @architkulkarni, @edoakes, @clarkzinzow, @DmitriGekhtman, @avnishn, @liuyang-my, @stephanie-wang, @Chong-Li, @ericl, @juliusfrost, @carlogrisetti

Contributors

ericl, pcmoritz, and 58 other contributors
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Ray-1.6.0

23 Aug 20:22
@krfricke krfricke
ray-1.6.0
7916500
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Ray-1.6.0

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:
  • 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:

🔨 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:

🏗 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:

🏗 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:

SGD

🎉 New Features:

💫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:

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

Contributors

ericl, pcmoritz, and 48 other contributors
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Ray-1.5.2

12 Aug 19:24
@jiaodong jiaodong
ray-1.5.2
88666d3
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Ray-1.5.2

Cherrypick release to address RLlib issue, no library or core changes included.

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Ray-1.5.1

31 Jul 01:47
@jiaodong jiaodong
ray-1.5.1
7d69ebb
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Ray-1.5.1

Cherrypick release to address a few external integration and documentation issues, no library or core changes included.

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Ray-1.5.0

26 Jul 18:43
@jiaodong jiaodong
ray-1.5.0
ddad6a2
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Ray-1.5.0

Ray 1.5.0 Release Note

Highlight

Ray Autoscaler

🎉 New Features:

💫 Enhancements:

  • [Kubernetes] Operator refactored to use Kopf package (#15787)
  • Flag to control config bootstrap for rsync (#16667)
  • Prometheus metrics for Autoscaler (#16066, #16198)
  • Allows launching in subnets where public IP assignments off by default (#16816)

🔨 Fixes:

  • [Kubernetes] Fix GPU=0 resource handling (#16887)
  • [Kubernetes] Release docs updated with K8s test instructions (#16662)
  • [Kubernetes] Documentation update (#16570)
  • [Kubernetes] All official images set to rayproject/ray:latest (#15988 #16205)
  • [Local] Fix bootstrapping ray at a given static set of ips (#16202, #16281)
  • [Azure] Fix Azure Autoscaling Failures (#16640)
  • Handle node type key change / deletion (#16691)
  • [GCP] Retry GCP BrokenPipeError (#16952)

Ray Client

🎉 New Features:

💫 Enhancements:

  • Better support for multi-threaded client-side applications (#16731, #16732)
  • Improved error messages and warnings when misusing Ray Client (#16454, #16508, #16588, #16163)
  • Made Client Object & Actor refs a subclass of their non-client counterparts (#16110)

🔨 Fixes:

Ray Core

🎉 New Features:

  • Ray dataset alpha is available!

🔨 Fixes:

🏗 Architecture refactoring:

  • From Ray 1.5.0, liveness of Ray jobs are guaranteed as long as there’s enough disk space in machines with the “fallback allocator” mechanism which allocates plasma objects to the disk directly when objects cannot be created in memory or spilled to the disk.

RLlib

🎉 New Features:

  • Support for adding/deleting Policies to a Trainer on-the-fly (#16359, #16569, #16927).
  • Added new “input API” for customizing offline datasets (shoutout to Julius F.). (#16957)
  • Allow for external env PolicyServer to listen on n different ports (given n rollout workers); No longer require creating an env on the server side to get env’s spaces. (#16583).

🔨 Fixes:

📖 Documentation and testing:

🏗 Architecture refactoring:

  • All RLlib algos operating on Box action spaces now operate on normalized actions by default (ranging from -1.0 to 1.0). This enables PG-style algos to learn in skewed action spaces. (#16531)

Tune

🎉 New Features:

💫 Enhancements:

  • Pass in configurations that have already been evaluated separately to Searchers. This is useful for warm-starting or for meta-searchers, for example (#16485)
  • Sort trials in reporter table by metric (#16576)
  • Add option to keep random values constant over grid search (#16501)
  • Read trial results from json file (#15915)

🔨 Fixes:

📖 Documentation and testing:

SGD

🎉 New Features:

  • Torch native mixed precision is now supported! (#16382)

🔨 Fixes:

  • Use target label count for training batch size (#16400)

📖 Documentation and testing:

Serve

💫 Enhancements: UX improvements (#16227, #15909), Improved logging (#16468)
🔨 Fixes: Fix shutdown logic (#16524), Assorted bug fixes (#16647, #16760, #16783)
📖 Documentation and testing: #16042, #16631, #16759, #16786

Thanks

Many thanks to all who contributed to this release:

@Tonyhao96, @simon-mo, @scv119, @Yard1, @llan-ml, @xcharleslin, @jovany-wang, @ijrsvt, @max0x7ba, @annaluo676, @rajagurunath, @zuston, @amogkam, @yorickvanzweeden, @mxz96102, @chenk008, @Bam4d, @mGalarnyk, @kfstorm, @crdnb, @suquark, @ericl, @marload, @jiaodong, @Thexiang, @ellimac54, @qicosmos, @mwtian, @jkterry1, @sven1977, @howardlau1999, @mvindiola1, @stefanbschneider, @juliusfrost, @krfricke, @matthewdeng, @zhuangzhuang131419, @brandonJY, @Eleven1Liu, @nikitavemuri, @richardliaw, @iycheng, @stephanie-wang, @HuangLED, @clarkzinzow, @fyrestone, @asm582, @qingyun-wu, @ckw017, @yncxcw, @DmitriGekhtman, @benjamindkilleen, @Chong-Li, @kathryn-zhou, @pcmoritz, @rodrigodelazcano, @edoakes, @dependabot[bot], @pdames, @frenkowski, @loicsacre, @gabrieleoliaro, @achals, @thomasjpfan, @rkooo567, @dibgerge, @clay4444, @architkulkarni, @lixin-wei, @ConeyLiu, @WangTaoTheTonic, @AnnaKosiorek, @wuisawesome, @gramhagen, @zhisbug, @franklsf95, @vakker, @jenhaoyang, @liuyang-my, @chaokunyang, @SongGuyang, @tgaddair

Contributors

ericl, pcmoritz, and 80 other contributors
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