Release Highlights

This release features stability improvements and API clean-ups across the Ray libraries.

• In Ray Serve, we are deprecating the previously experimental DAG API for deployment graphs. Model composition will be supported through deployment handles providing more flexibility and stability. The previously deprecated Ray Serve 1.x APIs have also been removed. We’ve also added a new Java APIs that aligns with the Ray Serve 2.x APIs. More API changes in the release notes below.
• In RLlib, we’ve moved 24 algorithms into rllib_contrib (still available within RLlib for Ray 2.8).
• We’ve added support for PyTorch-compatible input files shuffling for Ray Data. This allows users to randomly shuffle input files for better model training accuracy. This release also features new Ray Data datasources for Databricks and BigQuery.
• On the Ray Dashboard, we’ve added new metrics for Ray Data in the Metrics tab. This allows users to monitor Ray Data workload including real time metrics of cluster memory, CPU, GPU, output data size, etc. See the doc for more details.
• Ray Core now supports profiling GPU tasks or actors using Nvidia Nsight. See the documentation for instructions.
• We fixed 2 critical bugs raised by many kuberay / ML library users, including a child process leak issue from Ray worker that leaks the GPU memory (#40182) and an job page excessive loading time issue when Ray HA cluster restarts a head node (#40742)
• Python 3.7 support is officially deprecated from Ray.

Ray Libraries

Ray Data

🎉 New Features:

• Add support for shuffling input files (#40154)
• Support streaming read of PyTorch dataset (#39554)
• Add BigQuery datasource (#37380)
• Add Databricks table / SQL datasource (#39852)
• Add inverse transform functionality to LabelEncoder (#37785)
• Add function arg params to Dataset.map and Dataset.flat_map (#40010)

💫Enhancements:

• Hard deprecate DatasetPipeline (#40129)
• Remove BulkExecutor code path (#40200)
• Deprecate extraneous Dataset parameters and methods (#40385)
• Remove legacy iteration code path (#40013)
• Implement streaming output backpressure (#40387)
• Cap op concurrency with exponential ramp-up (#40275)
• Store ray dashboard metrics in _StatsActor (#40118)
• Slice output blocks to respect target block size (#40248)
• Drop columns before grouping by in Dataset.unique() (#40016)
• Standardize physical operator runtime metrics (#40173)
• Estimate blocks for limit and union operator (#40072)
• Store bytes spilled/restored after plan execution (#39361)
• Optimize sample_boundaries in SortTaskSpec (#39581)
• Optimization to reduce ArrowBlock building time for blocks of size 1 (#38833)

🔨 Fixes:

• Fix bug where _StatsActor errors with PandasBlock (#40481)
• Remove deprecated do_write (#40422)
• Improve error message when reading HTTP files (#40462)
• Add flag to skip get_object_locations for metrics (#39884)
• Fall back to fetch files info in parallel for multiple directories (#39592)
• Replace deprecated .pieces with updated .fragments (#39523)
• Backwards compatibility for Preprocessor that have been fit in older versions (#39173)
• Removing unnecessary data copy in convert_udf_returns_to_numpy (#39188)
• Do not eagerly free root RefBundles (#39016)

📖Documentation:

• Remove out-of-date Data examples (#40127)
• Remove unused and outdated source examples (#40271)

Ray Train

🎉 New Features:

• Add initial support for scheduling workers on neuron_cores (#39091)

💫Enhancements:

• Update PyTorch Lightning import path to support both pytorch_lightning and lightning (#39841, #40266)
• Propagate driver DataContext to RayTrainWorkers (#40116)

🔨 Fixes:

• Fix error propagation for as_directory if to_directory fails (#40025)

📖Documentation:

• Update checkpoint hierarchy documentation for RayTrainReportCallbacks. (#40174)
• Update Lightning RayDDPStrategy docstring (#40376)

🏗 Architecture refactoring:

• Deprecate LightningTrainer, AccelerateTrainer, `TransformersTrainer (#40163)
• Clean up legacy persistence mode code paths (#39921, #40061, #40069, #40168)
• Deprecate legacy DatasetConfig (#39963)
• Remove references to DatasetPipeline (#40159)
• Enable isort (#40172)

Ray Tune

💫Enhancements:

• Separate storage checkpoint index bookkeeping (#39927, #40003)
• Raise an error if Tuner.restore() is called on an instance (#39676)
  🏗 Architecture refactoring:
• Clean up legacy persistence mode code paths (#39918, #40061, #40069, #40168, #40175, #40192, #40181, #40193)
• Migrate TuneController tests (#39704)
• Remove TuneRichReporter (#40169)
• Remove legacy Ray Client tests (#40415)

Ray Serve

💫Enhancements:

• The single-app configuration format for the Serve Config (i.e. the Serve Config without the ‘applications’ field) has been deprecated in favor of the new configuration format.
  Both single-app configuration and DAG API will be removed in 2.9.
• The Serve REST API is now accessible through the dashboard port, which defaults to 8265.
• Accessing the Serve REST API through the dashboard agent port (default 52365) is deprecated. The support will be removed in a future version.
• Ray job error tracebacks are now logged in the job driver log for easier access when jobs fail during start up.
• Deprecated single-application config file
• Deprecated DAG API: InputNode and DAGDriver
• Removed deprecated Deployment 1.x APIs: Deployment.deploy(), Deployment.delete(), Deployment.get_handle()
• Removed deprecated 1.x API: serve.get_deployment and serve.list_deployments
• New Java API supported (aligns with Ray Serve 2.x API)

🔨 Fixes:

• The dedicated_cpu and detached options in serve.start() have been fully disallowed.
• Error will be raised when users pass invalid gRPC service functions and fail early.
• The proxy’s readiness check now uses a linear backoff to avoid getting stuck in an infinite loop if it takes longer than usual to start.
• grpc_options on serve.start() was only allowing a gRPCOptions object in Ray 2.7.0. Dictionaries are now allowed to be used asgrpc_options in the serve.start() call.

RLlib

💫Enhancements:

• rllib_contrib algorithms (A2C, A3C, AlphaStar #36584, AlphaZero #36736, ApexDDPG #36596, ApexDQN #36591, ARS #36607, Bandits #36612, CRR #36616, DDPG, DDPPO #36620, Dreamer(V1), DT #36623, ES #36625, LeelaChessZero #36627, MA-DDPG #36628, MAML, MB-MPO #36662, PG #36666, QMix #36682, R2D2, SimpleQ #36688, SlateQ #36710, and TD3 #36726) all produce warnings now if used. See here for more information on the rllib_contrib efforts. (36620, 36628, 3
• Provide msgpack checkpoint translation utility to convert checkpoint into msgpack format for being able to move in between python versions (#38825).

🔨 Fixes:

• Issue 35440 (JSON output writer should include INFOS #39632)
• Issue 39453 (PettingZoo wrappers should use correct multi-agent dict spaces #39459)
• Issue 39421 (Multi-discrete action spaces not supported in new stack #39534)
• Issue 39234 (Multi-categorical distribution bug #39464)
  #39654, #35975, #39552, #38555

Ray Core and Ray Clusters

Ray Core

🎉 New Features:

• Python 3.7 support is officially deprecated from Ray.
• Supports profiling GPU tasks or actors using Nvidia Nsight. See the doc for instructions.
• Ray on spark autoscaling is officially supported from Ray 2.8. See the REP for more details.
  💫Enhancements:
• IDLE node information in detail is available from ray status -v (#39638)
• Adding a new accelerator to Ray is simplified with a new accelerator interface. See the in-flight REP for more details (#40286).
• Typing_extensions is removed from a dependency requirement because Python 3.7 support is deprecated. (#40336)
• Ray state API supports case insensitive match. (#34577)
• ray start --runtime-env-agent-port is officially supported. (#39919)
• Driver exit code is available fromjob info (#39675)

🔨 Fixes:

• Fixed a worker leak when Ray is used with placement group because Ray didn’t handle SIGTERM properly (#40182)
• Fixed an issue job page loading takes a really long time when Ray HA cluster restarts a head node (#40431)
• [core] loosen the check on release object (#39570)
• [Core] ray init sigterm (#39816)
• [Core] Non Unit Instance fractional value fix (#39293)
• [Core]: Enable get_actor_name for actor runtime context (#39347)
• [core][streaming][python] Fix asyncio.wait coroutines args deprecated warnings #40292

📖Documentation:

• The Ray streaming generator doc (alpha) is officially available at https://docs.ray.io/en/master/ray-core/ray-generator.html

Ray Clusters

💫Enhancements:

• Enable GPU support for vSphere cluster launcher (#40667)

📖Documentation:

• Setup RBAC by KubeRay Helm chart
• KubeRay upgrade documentation
• RayService high availability

🔨 Fixes:

• Assorted fixes for vSphere cluster launcher (#40487, #40516, #40655)

Dashboard

🎉 New Features:

• New metrics for ray data can be found in the Metrics tab.
  🔨 Fixes:
• Fix bug where download log button did not download all logs for actors.

Thanks

Many thanks to all who contributed to this release!

@scottjlee, @chappidim, @alexeykudinkin, @ArturNiederfahrenhorst, @stephanie-wang, @chaowanggg, @peytondmurray, @maxpumperla, @arvind-chandra, @iycheng, @JalinWang, @matthewdeng, @wfangchi, @z4y1b2, @alanwguo, @Zandew, @kouroshHakha, @justinvyu, @yuanchen8911, @vitsai, @hongchaodeng, @allenwang28, @caozy623, @ijrsvt, @omus, @larrylian, @can-anyscale, @joncarter1, @ericl, @lejara, @jjyao, @Ox0400, @architkulkarni, @edoakes, @raulchen, @bveeramani, @sihanwang41, @WeichenXu123, @zcin, @Codle, @dimakis, @simonsays1980, @cadedaniel, @angelinalg, @luv003, @JingChen23, @xwjiang2010, @rynewang, @Yicheng-Lu-llll, @scrivy, @michaelhly, @shrekris-anyscale, @xxnwj, @avnishn, @woshiyyya, @aslonnie, @amogkam, @krfricke, @pcmoritz, @liuyang-my, @jonathan-anyscale, @rickyyx, @scottsun94, @richardliaw, @rkooo567, @stefanbschneider, @kevin85421, @c21, @sven1977, @GeneDer, @matthew29tang, @RocketRider, @LaynePeng, @samhallam-reverb, @scv119, @huchen2021