Ray-2.38.0

Ray Libraries

Ray Data

🎉 New Features:

• Add Dataset.rename_columns (#47906)
• Basic structured logging (#47210)

💫 Enhancements:

• Add partitioning parameter to read_parquet (#47553)
• Add SERVICE_UNAVAILABLE to list of retried transient errors (#47673)
• Re-phrase the streaming executor current usage string (#47515)
• Remove ray.kill in ActorPoolMapOperator (#47752)
• Simplify and consolidate progress bar outputs (#47692)
• Refactor OpRuntimeMetrics to support properties (#47800)
• Refactor plan_write_op and Datasinks (#47942)
• Link PhysicalOperator to its LogicalOperator (#47986)
• Allow specifying both num_cpus and num_gpus for map APIs (#47995)
• Allow specifying insertion index when registering custom plan optimization Rules (#48039)
• Adding in better framework for substituting logging handlers (#48056)

🔨 Fixes:

• Fix bug where Ray Data incorrectly emits progress bar warning (#47680)
• Yield remaining results from async map_batches (#47696)
• Fix event loop mismatch with async map (#47907)
• Make sure num_gpus provide to Ray Data is appropriately passed to ray.remote call (#47768)
• Fix unequal partitions when grouping by multiple keys (#47924)
• Fix reading multiple parquet files with ragged ndarrays (#47961)
• Removing unneeded test case (#48031)
• Adding in better json checking in test logging (#48036)
• Fix bug with inserting custom optimization rule at index 0 (#48051)
• Fix logging output from write_xxx APIs (#48096)

📖 Documentation:

• Add docs section for Ray Data progress bars (#47804)
• Add reference to parquet predicate pushdown (#47881)
• Add tip about how to understand map_batches format (#47394)

Ray Train

🏗 Architecture refactoring:

• Remove deprecated mosaic and sklearn trainer code (#47901)

Ray Tune

🔨 Fixes:

• Fix WandbLoggerCallback to reuse actors upon restore (#47985)

Ray Serve

🔨 Fixes:

• Stop scheduling task early when requests have been canceled (#47847)

RLlib

🎉 New Features:

• Enable cloud checkpointing. (#47682)

💫 Enhancements:

• PPO on new API stack now shuffles batches properly before each epoch. (#47458)
• Other enhancements: #47705, #47501, #47731, #47451, #47830, #47970, #47157

🔨 Fixes:

• Fix spot node preemption problem (RLlib now run stably with EnvRunner workers on spot nodes) (#47940)
• Fix action masking example. (#47817)
• Various other fixes: #47973, #46721, #47914, #47880, #47304, #47686

🏗 Architecture refactoring:

• Switch on new API stack by default for SAC and DQN. (#47217)
• Remove Tf support on new API stack for PPO/IMPALA/APPO (only DreamerV3 on new API stack remains with tf now). (#47892)
• Discontinue support for "hybrid" API stack (using RLModule + Learner, but still on RolloutWorker and Policy) (#46085)
• RLModule (new API stack) refinements: #47884, #47885, #47889, #47908, #47915, #47965, #47775

📖 Documentation:

• Add new API stack migration guide. (#47779)
• New API stack example script: BC pre training, then PPO finetuning using same RLModule class. (#47838)
• New API stack: Autoregressive actions example. (#47829)
• Remove old API stack connector docs entirely. (#47778)

Ray Core and Ray Clusters

Ray Core

🎉 New Features:

• CompiledGraphs: support multi readers in multi node when DAG is created from an actor (#47601)

💫 Enhancements:

• Add a flag to raise exception for out of band serialization of ObjectRef (#47544)
• Store each GCS table in its own Redis Hash (#46861)
• Decouple create worker vs pop worker request. (#47694)
• Add metrics for GCS jobs (#47793)

🔨 Fixes:

• Fix broken dashboard cluster page when there are dead nodes (#47701)
• Fix the ray_tasks{State="PENDING_ARGS_FETCH"} metric counting (#47770)
• Separate the attempt_number with the task_status in memory summary and object list (#47818)
• Fix object reconstruction hang on arguments pending creation (#47645)
• Fix check failure: sync_reactors_.find(reactor->GetRemoteNodeID()) == sync_reactors_.end() (#47861)
• Fix check failure RAY_CHECK(it != current_tasks_.end()); (#47659)

📖 Documentation:

• KubeRay docs: Add docs for YuniKorn Gang scheduling #47850

Dashboard

💫 Enhancements:

• Performance improvements for large scale clusters (#47617)

🔨 Fixes:

• Placement group and required resources not showing correctly in dashboard (#47754)

Thanks

Many thanks to all those who contributed to this release!
@GeneDer, @rkooo567, @dayshah, @saihaj, @nikitavemuri, @bill-oconnor-anyscale, @WeichenXu123, @can-anyscale, @jjyao, @edoakes, @kekulai-fredchang, @bveeramani, @alexeykudinkin, @raulchen, @khluu, @sven1977, @ruisearch42, @dentiny, @MengjinYan, @Mark2000, @simonsays1980, @rynewang, @PatricYan, @zcin, @sofianhnaide, @matthewdeng, @dlwh, @scottjlee, @MortalHappiness, @kevin85421, @win5923, @aslonnie, @prithvi081099, @richardsliu, @milesvant, @omatthew98, @Superskyyy, @pcmoritz

Ray-2.37.0

Ray Libraries

Ray Data

💫 Enhancements:

• Simplify custom metadata provider API (#47575)
• Change counts of metrics to rates of metrics (#47236)
• Throw exception for non-streaming HF datasets with "override_num_blocks" argument (#47559)
• Refactor custom optimizer rules (#47605)

🔨 Fixes:

• Remove ineffective retry code in plan_read_op (#47456)
• Fix incorrect pending task size if outputs are empty (#47604)

Ray Train

💫 Enhancements:

• Update run status and add stack trace to TrainRunInfo (#46875)

Ray Serve

💫 Enhancements:

• Allow control of some serve configuration via env vars (#47533)
• [serve] Faster detection of dead replicas (#47237)

🔨 Fixes:

• [Serve] fix component id logging field (#47609)

RLlib

💫 Enhancements:

• New API stack:
  • Add restart-failed-env option to EnvRunners. (#47608)
  • Offline RL: Store episodes in state form. (#47294)
  • Offline RL: Replace GAE in MARWILOfflinePreLearner with GeneralAdvantageEstimation connector in learner pipeline. (#47532)
  • Off-policy algos: Add episode sampling to EpisodeReplayBuffer. (#47500)
  • RLModule APIs: Add SelfSupervisedLossAPI for RLModules that bring their own loss and InferenceOnlyAPI. (#47581, #47572)

Ray Core

💫 Enhancements:

• [aDAG] Allow custom NCCL group for aDAG (#47141)
• [aDAG] support buffered input (#47272)
• [aDAG] Support multi node multi reader (#47480)
• [Core] Make is_gpu, is_actor, root_detached_id fields late bind to workers. (#47212)
• [Core] Reconstruct actor to run lineage reconstruction triggered actor task (#47396)
• [Core] Optimize GetAllJobInfo API for performance (#47530)

🔨 Fixes:

• [aDAG] Fix ranks ordering for custom NCCL group (#47594)

Ray Clusters

📖 Documentation:

• [KubeRay] add a guide for deploying vLLM with RayService (#47038)

Thanks

Many thanks to all those who contributed to this release!
@ruisearch42, @andrewsykim, @timkpaine, @rkooo567, @WeichenXu123, @GeneDer, @sword865, @simonsays1980, @angelinalg, @sven1977, @jjyao, @woshiyyya, @aslonnie, @zcin, @omatthew98, @rueian, @khluu, @justinvyu, @bveeramani, @nikitavemuri, @chris-ray-zhang, @liuxsh9, @xingyu-long, @peytondmurray, @rynewang

Ray-2.36.1

Ray Core

🔨 Fixes:

• Fix broken dashboard cluster page when there are dead nodes (#47701)
• Fix broken dashboard worker page (#47714)

Ray-2.36.0

Ray Libraries

Ray Data

💫 Enhancements:

• Remove limit on number of tasks launched per scheduling step (#47393)
• Allow user-defined Exception to be caught. (#47339)

🔨 Fixes:

• Display pending actors separately in the progress bar and not count them towards running resources (#46384)
• Fix bug where arrow_parquet_args aren't used (#47161)
• Skip empty JSON files in read_json() (#47378)
• Remove remote call for initializing Datasource in read_datasource() (#47467)
• Remove dead from_*_operator modules (#47457)
• Release test fixes
• Add AWS ACCESS_DENIED as retryable exception for multi-node Data+Train benchmarks (#47232)
• Get AWS credentials with boto (#47352)
• Use worker node instead of head node for read_images_comparison_microbenchmark_single_node release test (#47228)

📖 Documentation:

• Add docstring to explain Dataset.deserialize_lineage (#47203)
• Add a comment explaining the bundling behavior for map_batches with default batch_size (#47433)

Ray Train

💫 Enhancements:

• Decouple device-related modules and add Huawei NPU support to Ray Train (#44086)

🔨 Fixes:

• Update TORCH_NCCL_ASYNC_ERROR_HANDLING env var (#47292)

📖 Documentation:

• Add missing Train public API reference (#47134)

Ray Tune

📖 Documentation:

• Add missing Tune public API references (#47138)

Ray Serve

💫 Enhancements:

• Mark proxy as unready when its routers are aware of zero replicas (#47002)
• Setup default serve logger (#47229)

🔨 Fixes:

• Allow get_serve_logs_dir to run outside of Ray's context (#47224)
• Use serve logger name for logs in serve (#47205)

📖 Documentation:

• [HPU] [Serve] [experimental] Add vllm HPU support in vllm example (#45893)

🏗 Architecture refactoring:

• Remove support for nested DeploymentResponses (#47209)

RLlib

🎉 New Features:

• New API stack: Add CQL algorithm. (#47000, #47402)
• New API stack: Enable GPU and multi-GPU support for DQN/SAC/CQL. (#47179)

💫 Enhancements:

• New API stack: Offline RL enhancements: #47195, #47359
• Enhance new API stack stability: #46324, #47196, #47245, #47279
• Fix large batch size for synchronous algos (e.g. PPO) after EnvRunner failures. (#47356)
• Add torch.compile config options to old API stack. (#47340)
• Add kwargs to torch.nn.parallel.DistributedDataParallel (#47276)
• Enhanced CI stability: #47197, #47249

📖 Documentation:

• New API stack example scripts:
  • Float16 training example script. (#47362)
  • Mixed precision training example script (#47116)
  • ModelV2 -> RLModule wrapper for migrating to new API stack. (#47425)
• Remove "new API stack experimental" hint from docs. (#47301)

🏗 Architecture refactoring:

• Remove 2nd Learner ConnectorV2 pass from PPO (#47401)
• Add separate learning rates for policy and alpha to SAC. (#47078)

🔨 Fixes:

• Various bug fixes: #47401, #47194, #47259, #47271, #47277, #47382

Ray Core

💫 Enhancements:

• [ADAG] Raise proper error message for nccl within the same actor (#47250)
• [ADAG] Support multi-read of the same shm channel (#47311)
• Log why core worker is not idle during HandleExit (#47300)
• Add PREPARED state for placement groups in GCS for better fault tolerance. (#46858)

🔨 Fixes:

• Fix ray_unintentional_worker_failures_total to only count unintentional worker failures (#47368)
• Fix runtime env race condition when uploading the same package concurrently (#47482)

Dashboard

🔨 Fixes:

• Performance optimizations for dashboard backend logic (#47392) (#47367) (#47160) (#47213)
• Refactor to simplify dashboard backend logic (#47324)

Docs

💫 Enhancements:

• Add sphinx-autobuild and documentation for make local (#47275): Speed up of local docs builds with make local.
• Add Algolia search to docs (#46477)
• Update PyTorch Mnist Training doc for KubeRay 1.2.0 (#47321)
• Life-cycle of documentation policy of Ray APIs

Thanks

Many thanks to all those who contributed to this release!
@GeneDer, @Bye-legumes, @nikitavemuri, @kevin85421, @MortalHappiness, @LeoLiao123, @saihaj, @rmcsqrd, @bveeramani, @zcin, @matthewdeng, @raulchen, @mattip, @jjyao, @ruisearch42, @scottjlee, @can-anyscale, @khluu, @aslonnie, @rynewang, @edoakes, @zhanluxianshen, @venkatram-dev, @c21, @allenyin55, @alexeykudinkin, @snehakottapalli, @BitPhinix, @hongchaodeng, @dengwxn, @liuxsh9, @simonsays1980, @peytondmurray, @KepingYan, @bryant1410, @woshiyyya, @sven1977

Ray-2.35.0

Notice: Starting from this release, pip install ray[all] will not include ray[cpp], and will not install the respective ray-cpp package. To install everything that includes ray-cpp, one can use pip install ray[cpp-all] instead.

Ray Libraries

Ray Data

🎉 New Features:

• Upgrade supported Arrow version from 16 to 17 (#47034)
• Add support for reading from Iceberg (#46889)

💫 Enhancements:

• Various Progress Bar UX improvements (#46816, #46801, #46826, #46692, #46699, #46974, #46928, #47029, #46924, #47120, #47095, #47106)
• Try get size_bytes from metadata and consolidate metadata methods (#46862)
• Improve warning message when read task is large (#46942)
• Extend API to enable passing sample weights via ray.dataset.to_tf (#45701)
• Add a parameter to allow overriding LanceDB scanner options (#46975)
• Add failure retry logic for read_lance (#46976)
• Clarify warning for reading old Parquet data (#47049)
• Move datasource implementations to _internal subpackage (#46825)
• Handle logs from tensor extensions (#46943)

🔨 Fixes:

• Change type of DataContext.retried_io_errors from tuple to list (#46884)
• Make Parquet tests more robust and expose Parquet logic (#46944)
• Change pickling log level from warning to debug (#47032)
• Add validation for shuffle arg (#47055)
• Fix validation bug when size=0 in ActorPoolStrategy (#47072)
• Fix exception in async map (#47110)
• Fix wrong metrics group for Object Store Memory metrics on Ray Data Dashboard (#47170)
• Handle errors in SplitCoordinator when generating a new epoch (#47176)

📖 Documentation:

• Auto-gen GroupedData api (#46925)
• Fix signature of Rule.plan (#47094)

Ray Train

💫 Enhancements:

• [train] Updates to support xgboost==2.1.0 (#46667)
• [train] Add hardware stats (#46719)

Ray Tune

🔨 Fixes:

• [RLlib; Tune] Fix WandB metric overlap after restore from checkpoint. (#46897)

Ray Serve

💫 Enhancements:

• Improved handling of replica death and replica unavailability in deployment handle routers before controller restarts replica (#47008)
• Eagerly create routers in proxy for better GCS fault tolerance (#47031)
• Immediately send ping in router when receiving new replica set (#47053)

🏗 Architecture refactoring:

• Deprecate passing arguments that contain DeploymentResponses in nested objects to downstream deployment handle calls (#46806)

RLlib

🎉 New Features:

• Offline RL on the new API stack:
  • Record offline data (#46818, #47046, #47133, #47155) and support to directly read from episodes. (#46865)
  • RLUnplugged example. (#46792)
  • Progress on BC/MARWIL migration: #44970, #47154, #46799
  • Progress on CQL migration: #46969, #47105

💫 Enhancements:

• Add ObservationPreprocessor (ConnectorV2). (#47077)

🔨 Fixes:

• New API stack: Fix IMPALA/APPO + LSTM for single- and multi-GPU. (#47132, #47158)
• Various bug fixes: #46898, #47047, #46963, #47021, #46897
• Add more control to Algorithm.add_module/policy methods. (#46932, #46836)

📖 Documentation:

• Example scripts for new API stack:
  • Curiosity (inverse dynamics model-based) RLModule example. (#46841)
  • Add example script for Env with protobuf observation space. (#47071)
• New API stack documentation:
  • Cleanup old API stack docs (rllib-dev.rst). (#47172)
  • Episodes (SingleAgentEpisode). (#46985)
  • Redo rllib-algorithms.rst page. (#46916)

🏗 Architecture refactoring:

• Rename MultiAgent...RLModule... into MultiRL...Module for more generality. (#46840)
• Add learner_only flag to RLModuleConfig/Spec and simplify creation of RLModule specs from algo-config. (#46900)

Ray Core

💫 Enhancements:

• Emit total lineage bytes metrics (#46725)
• Adding accelerator type H100 (#46823)
• More structured logging in core worker (#46906)
• Change all callbacks to move to save copies. (#46971)
• Add ray[adag] option to pip install (#47009)

🔨 Fixes:

• Fix dashboard process reporting on windows (#45578)
• Fix Ray-on-Spark cluster crashing bug when user cancels cell execution (#46899)
• Fix PinExistingReturnObject segfault by passing owner_address (#46973)
• Fix raylet CHECK failure from runtime env creation failure. (#46991)
• Fix typo in memray command (#47006)
• [ADAG] Fix for asyncio outputs (#46845)

📖 Documentation:

• Clarify behavior of placement_group_capture_child_tasks in docs (#46885)
• Update ray.available_resources() docstring (#47018)

🏗 Architecture refactoring:

• Async APIs for the New GcsClient. (#46788)
• Replace GCS stubs in the dashboard to use NewGcsAioClient. (#46846)

Dashboard

💫 Enhancements:

• Polish and minor improvements to the Serve page (#46811)

🔨 Fixes:

• Fix CPU/GPU/RAM not being reported correctly on Windows (#44578)

Docs

💫 Enhancements:

• Add more information about developer tooling for docs contributions (#46636), including esbonio section

🔨 Fixes:

• Use PyData Sphinx theme version switcher (#46936)

Thanks

Many thanks to all those who contributed to this release!
@simonsays1980, @bveeramani, @tungh2, @zcin, @xingyu-long, @WeichenXu123, @aslonnie, @MaxVanDijck, @can-anyscale, @galenhwang, @omatthew98, @matthewdeng, @raulchen, @sven1977, @shrekris-anyscale, @deepyaman, @alexeykudinkin, @stephanie-wang, @kevin85421, @ruisearch42, @hongchaodeng, @khluu, @alanwguo, @hongpeng-guo, @saihaj, @Superskyyy, @tespent, @slfan1989, @justinvyu, @rynewang, @nikitavemuri, @amogkam, @mattip, @dev-goyal, @ryanaoleary, @peytondmurray, @edoakes, @venkatajagannath, @jjyao, @cristianjd, @scottjlee, @Bye-legumes

Release 2.34.0 Notes

Ray Libraries

Ray Data

💫 Enhancements:

• Add better support for UDF returns from list of datetime objects (#46762)

🔨 Fixes:

• Remove read task warning if size bytes not set in metadata (#46765)

📖 Documentation:

• Fix read_tfrecords() docstring to display tfx-bsl tip (#46717)
• Update Dataset.zip() docs (#46757)

Ray Train

🔨 Fixes:

• Sort workers by node ID rather than by node IP (#46163)

🏗 Architecture refactoring:

• Remove dead RayDatasetSpec (#46764)

RLlib

🎉 New Features:

• Offline RL support on new API stack:
  • Initial design for Ray-Data based offline RL Algos (on new API stack). (#44969)
  • Add user-defined schemas for data loading. (#46738)
  • Make data pipeline better configurable and tuneable for users. (#46777)

💫 Enhancements:

• Move DQN into the TargetNetworkAPI (and deprecate RLModuleWithTargetNetworksInterface). (#46752)

🔨 Fixes:

• Numpy version fix: Rename all np.product usage to np.prod (#46317)

📖 Documentation:

• Examples for new API stack: Add 2 (count-based) curiosity examples. (#46737)
• Remove RLlib CLI from docs (soon to be deprecated and replaced by python API). (#46724)

🏗 Architecture refactoring:

• Cleanup, rename, clarify: Algorithm.workers/evaluation_workers, local_worker(), etc.. (#46726)

Ray Core

🏗 Architecture refactoring:

• New python GcsClient binding (#46186)

Many thanks to all those who contributed to this release! @KyleKoon, @ruisearch42, @rynewang, @sven1977, @saihaj, @aslonnie, @bveeramani, @akshay-anyscale, @kevin85421, @omatthew98, @anyscalesam, @MaxVanDijck, @justinvyu, @simonsays1980, @can-anyscale, @peytondmurray, @scottjlee

Ray-2.33.0

Ray Libraries

Ray Core

💫 Enhancements:

• Add "last exception" to error message when GCS connection fails in ray.init() (#46516)

🔨 Fixes:

• Add object back to memory store when object recovery is skipped (#46460)
• Task status should start with PENDING_ARGS_AVAIL when retry (#46494)
• Fix ObjectFetchTimedOutError (#46562)
• Make working_dir support files created before 1980 (#46634)
• Allow full path in conda runtime env. (#45550)
• Fix worker launch time formatting in state api (#43516)

Ray Data

🎉 New Features:

• Deprecate Dataset.get_internal_block_refs() (#46455)
• Add read API for reading Databricks table with Delta Sharing (#46072)
• Add support for objects to Arrow blocks (#45272)

💫 Enhancements:

• Change offsets to int64 and change to LargeList for ArrowTensorArray (#45352)
• Prevent from_pandas from combining input blocks (#46363)
• Update Dataset.count() to avoid unnecessarily keeping BlockRefs in-memory (#46369)
• Use Set to fix inefficient iteration over Arrow table columns (#46541)
• Add AWS Error UNKNOWN to list of retried write errors (#46646)
• Always print traceback for internal exceptions (#46647)
• Allow unknown estimate of operator output bundles and ProgressBar totals (#46601)
• Improve filesystem retry coverage (#46685)

🔨 Fixes:

• Replace lambda mutable default arguments (#46493)

📖 Documentation:

• Auto-generate Dataset API documentation (#46557)
• Update outdated ExecutionPlan docstring (#46638)

Ray Train

💫 Enhancements:

• Update run status and actor status for train runs. (#46395)

🔨 Fixes:

• Replace lambda default arguments (#46576)

📖 Documentation:

• Add MNIST training using KubeRay doc page (#46123)
• Add example of pre-training Llama model on Intel Gaudi (#45459)
• Fix tensorflow example by using ScalingConfig (#46565)

Ray Tune

🔨 Fixes:

• Replace lambda default arguments (#46596)

Ray Serve

🎉 New Features:

• Fully deprecate target_num_ongoing_requests_per_replica and max_concurrent_queries, respectively replaced by max_ongoing_requests and target_ongoing_requests (#46392 and #46427)
• Configure the task launched by the controller to build an application with Serve’s logging config (#46347)

RLlib

💫 Enhancements:

• Moving sampling coordination for batch_mode=complete_episodes to synchronous_parallel_sample. (#46321)
• Enable complex action spaces with stateful modules. (#46468)

🏗 Architecture refactoring:

• Enable multi-learner setup for hybrid stack BC. (#46436)
• Introduce Checkpointable API for RLlib components and subcomponents. (#46376)

🔨 Fixes:

• Replace Mapping typehint with Dict: #46474

📖 Documentation:

• More example scripts for new API stack: Two separate optimizers (w/ different learning rates). (#46540) and custom loss function. (#46445)

Dashboard

🔨 Fixes:

• Task end time showing the incorrect time (#46439)
• Events Table rows having really bad spacing (#46701)
• UI bugs in the serve dashboard page (#46599)

Thanks

Many thanks to all those who contributed to this release!

@alanwguo, @hongchaodeng, @anyscalesam, @brucebismarck, @bt2513, @woshiyyya, @terraflops1048576, @lorenzoritter, @omrishiv, @davidxia, @cchen777, @nono-Sang, @jackhumphries, @aslonnie, @JoshKarpel, @zjregee, @bveeramani, @khluu, @Superskyyy, @liuxsh9, @jjyao, @ruisearch42, @sven1977, @harborn, @saihaj, @zcin, @can-anyscale, @veekaybee, @chungen04, @WeichenXu123, @GeneDer, @sergey-serebryakov, @Bye-legumes, @scottjlee, @rynewang, @kevin85421, @cristianjd, @peytondmurray, @MortalHappiness, @MaxVanDijck, @simonsays1980, @mjovanovic9999

Ray-2.32.0

Highlight: aDAG Developer Preview

This is a new Ray Core specific feature called Ray accelerated DAGs (aDAGs).

• aDAGs give you a Ray Core-like API but with extensibility to pre-compile execution paths across pre-allocated resources on a Ray Cluster to possible benefits for optimization on throughput and latency. Some practical examples include:
  • Up to 10x lower task execution time on single-node.
  • Native support for GPU-GPU communication, via NCCL.
• This is still very early, but please reach out on #ray-core on Ray Slack to learn more!

Ray Libraries

Ray Data

💫 Enhancements:

• Support async callable classes in map_batches() (#46129)

🔨 Fixes:

• Ensure InputDataBuffer doesn't free block references (#46191)
• MapOperator.num_active_tasks should exclude pending actors (#46364)
• Fix progress bars being displayed as partially completed in Jupyter notebooks (#46289)

📖 Documentation:

• Fix docs: read_api.py docstring (#45690)
• Correct API annotation for tfrecords_datasource (#46171)
• Fix broken links in README and in ray.data.Dataset (#45345)

Ray Train

📖 Documentation:

• Update PyTorch Data Ingestion User Guide (#45421)

Ray Serve

💫 Enhancements:

• Optimize ServeController.get_app_config() (#45878)
• Change default for max and target ongoing requests (#45943)
• Integrate with Ray structured logging (#46215)
• Allow configuring handle cache size and controller max concurrency (#46278)
• Optimize DeploymentDetails.deployment_route_prefix_not_set() (#46305)

RLlib

🎉 New Features:

• APPO on new API stack (w/ EnvRunners). (#46216)

💫 Enhancements:

• Stability: APPO, SAC, and DQN activate multi-agent learning tests (#45542, #46299)
• Make Tune trial ID available in EnvRunners (and callbacks). (#46294)
• Add env- and agent_steps to custom evaluation function. (#45652)
• Remove default-metrics from Algorithm (tune does NOT error anymore if any stop-metric is missing). (#46200)

🔨 Fixes:

• Various bug fixes: #45542

📖 Documentation:

• Example for new API stack: Offline RL (BC) training on single-agent, while evaluating w/ multi-agent setup. (#46251)
• Example for new API stack: Custom RLModule with an LSTM. (#46276)

Ray Core

🎉 New Features:

• aDAG Developer Preview.

💫 Enhancements:

• Allow env setup logger encoding (#46242)
• ray list tasks filter state and name on GCS side (#46270)
• Log ray version and ray commit during GCS start (#46341)

🔨 Fixes:

• Decrement lineage ref count of an actor when the actor task return object reference is deleted (#46230)
• Fix negative ALIVE actors metric and introduce IDLE state (#45718)
• psutil process attr num_fds is not available on Windows (#46329)

Dashboard

🎉 New Features:

• Added customizable refresh frequency for metrics on Ray Dashboard (#44037)

💫 Enhancements:

• Upgraded to MUIv5 and React 18 (#45789)

🔨 Fixes:

• Fix for multi-line log items breaking log viewer rendering (#46391)
• Fix for UI inconsistency when a job submission creates more than one Ray job. (#46267)
• Fix filtering by job id for tasks API not filtering correctly. (#45017)

Docs

🔨 Fixes:

• Re-enabled automatic cross-reference link checking for Ray documentation, with Sphinx nitpicky mode (#46279)
• Enforced naming conventions for public and private APIs to maintain accuracy, starting with Ray Data API documentation (#46261)

📖 Documentation:

• Upgrade Python 3.12 support to alpha, marking the release of the Ray wheel to PyPI and conducting a sanity check of the most critical tests.

Thanks

Many thanks to all those who contributed to this release!

@stephanie-wang, @MortalHappiness, @aslonnie, @ryanaoleary, @jjyao, @jackhumphries, @nikitavemuri, @woshiyyya, @JoshKarpel, @ruisearch42, @sven1977, @alanwguo, @GeneDer, @saihaj, @raulchen, @liuxsh9, @khluu, @cristianjd, @scottjlee, @bveeramani, @zcin, @simonsays1980, @SumanthRH, @davidxia, @can-anyscale, @peytondmurray, @kevin85421

Ray-2.31.0

Ray Libraries

Ray Data

🔨 Fixes:

• Fixed bug where preserve_order doesn’t work with file reads (#46135)

📖 Documentation:

• Added documentation for dataset.Schema (#46170)

Ray Train

💫 Enhancements:

• Add API for Ray Train run stats (#45711)

Ray Tune

💫 Enhancements:

• Missing stopping criterion should not error (just warn). (#45613)

📖 Documentation:

• Fix broken references in Ray Tune documentation (#45233)

Ray Serve

WARNING: the following default values will change in Ray 2.32:

• Default for max_ongoing_requests will change from 100 to 5.
• Default for target_ongoing_requests will change from 1 to 2.

💫 Enhancements:

• Optimize DeploymentStateManager.get_deployment_statuses (#45872)

🔨 Fixes:

• Fix logging error on passing traceback object into exc_info (#46105)
• Run del even if constructor is still in-progress (#45882)
• Spread replicas with custom resources in torch tune serve release test (#46093)
• [1k release test] don't run replicas on head node (#46130)

📖 Documentation:

• Remove todo since issue is fixed (#45941)

RLlib

🎉 New Features:

• IMPALA runs on the new API stack (with EnvRunners and ConnectorV2s). (#42085)
• SAC/DQN: Prioritized multi-agent episode replay buffer. (#45576)

💫 Enhancements:

• New API stack stability: Add systematic CI learning tests for all possible combinations of: [PPO|IMPALA] + [1CPU|2CPU|1GPU|2GPU] + [single-agent|multi-agent]. (#46162, #46161)

📖 Documentation:

• New API stack: Example script for action masking (#46146)
• New API stack: PyFlight example script cleanup (#45956)
• Old API stack: Enhanced ONNX example (+LSTM). (#43592)

Ray Core and Ray Clusters

Ray Core

💫 Enhancements:

• [runtime-env] automatically infer worker path when starting worker in container (#42304)

🔨 Fixes:

• On GCS restart, destroy not forget the unused workers. Fixing PG leaks. (#45854)
• Cancel lease requests before returning a PG bundle (#45919)
• Fix boost fiber stack overflow (#46133)

Thanks

Many thanks to all those who contributed to this release!

@jjyao, @kevin85421, @vincent-pli, @khluu, @simonsays1980, @sven1977, @rynewang, @can-anyscale, @richardsliu, @jackhumphries, @alexeykudinkin, @bveeramani, @ruisearch42, @shrekris-anyscale, @stephanie-wang, @matthewdeng, @zcin, @hongchaodeng, @ryanaoleary, @liuxsh9, @GeneDer, @aslonnie, @peytondmurray, @Bye-legumes, @woshiyyya, @scottjlee, @JoshKarpel

Ray-2.30.0

Ray Libraries

Ray Data

💫 Enhancements:

• Improve fractional CPU/GPU formatting (#45673)
• Use sampled fragments to estimate Parquet reader batch size (#45749)
• Refactoring ParquetDatasource and metadata fetching logic (#45728, #45727, #45733, #45734, #45767)
• Refactor planner.py (#45706)

Ray Tune

💫 Enhancements:

• Change the behavior of a missing stopping criterion metric to warn instead of raising an error. This enables the use case of reporting different sets of metrics on different iterations (ex: a separate set of training and validation metrics). (#45613)

Ray Serve

💫 Enhancements:

• Create internal request id to track request objects (#45761)

RLLib

💫 Enhancements:

• Stability: DreamerV3 weekly release test (#45654); Add "official" benchmark script for Atari PPO benchmarks. (#45697)
• Enhance env-rendering callback (#45682)

🔨 Fixes:

• Bug fix in new MetricsLogger API: EMA stats w/o window would lead to infinite list mem-leak. (#45752)
• Various other bug fixes: (#45819, #45820, #45683, #45651, #45753)

📖 Documentation:

• Re-do examples overview page (new API stack): #45382
  • PyFlyt QuadX WayPoints example #44758, #45956
  • RLModule inference on new API stack (#45831, #45845)
  • How to resume a tune.Tuner.fit() experiment from checkpoint. (#45681)
  • Custom RLModule (tiny CNN): #45774
  • Connector examples docstrings (#45864)
• Old API stack examples: #43592, #45829

Ray Core

🎉 New Features:

• Alpha release of job level logging configuration: users can now config the user logging to be logfmt format with logging context attached. (#45344)

💫 Enhancements:

• Integrate amdsmi in AMDAcceleratorManager (#44572)

🔨 Fixes:

• Fix the C++ GcsClient Del not respecting del_by_prefix (#45604)
• Fix exit handling of FiberState threads (#45834)

Dashboard

💫 Enhancements:

• Parse out json logs (#45853)

Many thanks to all those who contributed to this release: @liuxsh9, @peytondmurray, @pcmoritz, @GeneDer, @saihaj, @khluu, @aslonnie, @yucai, @vickytsang, @can-anyscale, @bthananjeyan, @raulchen, @hongchaodeng, @x13n, @simonsays1980, @peterghaddad, @kevin85421, @rynewang, @angelinalg, @jjyao, @BenWilson2, @jackhumphries, @zcin, @chris-ray-zhang, @c21, @shrekris-anyscale, @alanwguo, @stephanie-wang, @Bye-legumes, @sven1977, @WeichenXu123, @bveeramani, @nikitavemuri