fix(deps): update dependency ray to v2.40.0

[renovate]

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
ray	2.5.1 -> 2.40.0

---

Release Notes

ray-project/ray (ray)

v2.40.0

Compare Source

Ray Libraries

Ray Data

🎉 New Features:

• Added read_hudi (https://github.com/ray-project/ray/pull/46273)

💫 Enhancements:

• Improved performance of DelegatingBlockBuilder (https://github.com/ray-project/ray/pull/48509)
• Improved memory accounting of pandas blocks (https://github.com/ray-project/ray/pull/46939)

🔨 Fixes:

• Fixed bug where you can’t specify a schema with write_parquet (https://github.com/ray-project/ray/issues/48630)
• Fixed bug where to_pandas errors if your dataset contains Arrow and pandas blocks (https://github.com/ray-project/ray/pull/48583)
• Fixed bug where map_groups doesn’t work with pandas data (https://github.com/ray-project/ray/pull/48287)
• Fixed bug where write_parquet errors if your data contains nullable fields (https://github.com/ray-project/ray/pull/48478)
• Fixed bug where “Iteration Blocked Time” charts looks incorrect (https://github.com/ray-project/ray/pull/48618)
• Fixed bug where unique fails with null values (https://github.com/ray-project/ray/pull/48750)
• Fixed bug where “Rows Outputted” is 0 in the Data dashboard (https://github.com/ray-project/ray/pull/48745)
• Fixed bug where methods like drop_columns cause spilling (https://github.com/ray-project/ray/pull/48140)
• Fixed bug where async map tasks hang (https://github.com/ray-project/ray/pull/48861)

🗑️ Deprecations:

• Deprecated read_parquet_bulk https://github.com/ray-project/ray/pull/48691
• Deprecated iter_tf_batches https://github.com/ray-project/ray/pull/48693
• Deprecated meta_provider parameter of read functions (https://github.com/ray-project/ray/pull/48690)
• Deprecated to_torch (https://github.com/ray-project/ray/pull/48692)

Ray Train

🔨 Fixes:

• Fix StartTracebackWithWorkerRank serialization (#​48548)

📖 Documentation:

• Add example for fine-tuning Llama3.1 with AWS Trainium (#​48768)

Ray Tune

🔨 Fixes:

• Remove the clear_checkpoint function during Trial restoration error handling. (#​48532)

Ray Serve

🎉 New Features:

• Initial version of local_testing_mode (#​48477)

💫 Enhancements:

• Handle multiple changed objects per LongPollHost.listen_for_change RPC (#​48803)
• Add more nuanced checks for http proxy status errors (#​47896)
• Improve replica access log messages to include HTTP status info and better resemble standard log format (#​48819)
• Propagate replica constructor error to deployment status message and print num retries left (#​48531)

🔨 Fixes:

• Pending requests that are cancelled before they were assigned to a replica now also return a serve.RequestCancelledError (#​48496)

RLlib

💫 Enhancements:

• Release test enhancements. (#​45803, #​48681)
• Make opencv-python-headless default over opencv-python (#​48776)
• Reverse learner queue behavior of IMPALA/APPO (consume oldest batches first, instead of newest, BUT drop oldest batches if queue full). (#​48702)

🔨 Fixes:

• Fix torch scheduler stepping and reporting. (#​48125)
• Fix accumulation of results over n training_step calls within same iteration (new API stack). (#​48136)
• Various other fixes: #​48563, #​48314, #​48698, #​48869.

📖 Documentation:

• Upgrade examples script overview page (new API stack). (#​48526)
• Enable RLlib + Serve example in CI and translate to new API stack. (#​48687)

🏗 Architecture refactoring:

• Switch new API stack on by default, APPO, IMPALA, BC, MARWIL, and CQL. (#​48516, #​48599)
• Various APPO enhancements (new API stack): Circular buffer (#​48798), minor loss math fixes (#​48800), target network update logic (#​48802), smaller cleanups (#​48844).
• Remove rllib_contrib from repo. (#​48565)

Ray Core and Ray Clusters

Ray Core

🎉 New Features:

• [Core] uv runtime env support (#​48479, #​48486, #​48611, #​48619, #​48632, #​48634, #​48637, #​48670, #​48731)
• [Core] GCS FT with redis sentinel (#​47335)

💫 Enhancements:

• [CompiledGraphs] Refine schedule visualization (#​48594)

🔨 Fixes:

• [CompiledGraphs] Don't persist input_nodes in _CollectiveOperation to avoid wrong understanding about DAGs (#​48463)
• [Core] Fix Ascend NPU discovery to support 8+ cards per node (#​48543)
• [Core] Make Placement Group Wildcard and Indexed Resource Assignments Consistent (#​48088)
• [Core] Stop the GRPC server before Shut down the Object Store (#​48572)

Ray Clusters

🔨 Fixes:

• [KubeRay]: Fix ConnectionError on Autoscaler CR lookups in K8s clusters with custom DNS for Kubernetes API. (#​48541)

Dashboard

💫 Enhancements:

• Add global UTC timezone button in navbar with local storage (#​48510)
• Add memory graphs optimized for OOM debugging (#​48530)
• Improve tasks/actors metric naming and add graph for running tasks (#​48528)
  add actor pid to dashboard (#​48791)

🔨 Fixes:

• Fix Placement Group Table table cells overflow (#​47323)
• Fix Rows Outputted being zero on Ray Data Dashboard (#​48745)
• fix confusing dataset operator name (#​48805)

Thanks

Thanks to all those who contributed to this release!
@​rynewang, @​rickyyx, @​bveeramani, @​marwan116, @​simonsays1980, @​dayshah, @​dentiny, @​KepingYan, @​mimiliaogo, @​kevin85421, @​SeaOfOcean, @​stephanie-wang, @​mohitjain2504, @​azayz, @​xushiyan, @​richardliaw, @​can-anyscale, @​xingyu-long, @​kanwang, @​aslonnie, @​MortalHappiness, @​jjyao, @​SumanthRH, @​matthewdeng, @​alexeykudinkin, @​sven1977, @​raulchen, @​andrewsykim, @​zcin, @​nadongjun, @​hongpeng-guo, @​miguelteixeiraa, @​saihaj, @​khluu, @​ArturNiederfahrenhorst, @​ryanaoleary, @​ltbringer, @​pcmoritz, @​JoshKarpel, @​akyang-anyscale, @​frances720, @​BeingGod, @​edoakes, @​Bye-legumes, @​Superskyyy, @​liuxsh9, @​MengjinYan, @​ruisearch42, @​scottjlee, @​angelinalg

v2.39.0

Compare Source

Ray Libraries

Ray Data

🔨 Fixes:

• Fixed InvalidObjectError edge case with Dataset.split() (https://github.com/ray-project/ray/pull/48130)
• Made Concatenator preserve order of concatenated columns (https://github.com/ray-project/ray/pull/47997)

📖 Documentation:

• Improved documentation around Parquet column and predicate pushdown (https://github.com/ray-project/ray/pull/48095)
• Marked num_rows_per_file parameter of write APIs as experimental (https://github.com/ray-project/ray/pull/48208)
• One hot encoder now returns an encoded vector (https://github.com/ray-project/ray/pull/48173)
• transform_batch no longer fails on missing columns (https://github.com/ray-project/ray/pull/48137)

🏗 Architecture refactoring:

• Dataset.count() now uses a Count logical operator (https://github.com/ray-project/ray/pull/48126)

🗑 Deprecations:

• Removed long-deprecated set_progress_bars (https://github.com/ray-project/ray/pull/48203)

Ray Train

🔨 Fixes:

• Safely check if the storage filesystem is pyarrow.fs.S3FileSystem (#​48216)

Ray Tune

🔨 Fixes:

• Safely check if the storage filesystem is pyarrow.fs.S3FileSystem (#​48216)

Ray Serve

💫 Enhancements:

• Cancelled requests now return a serve.RequestCancelledError (https://github.com/ray-project/ray/pull/48444)
• Exposed application source in app details model (https://github.com/ray-project/ray/pull/45522)

🔨 Fixes:

• Basic HTTP deployments will now return “Internal Server Error” instead of a traceback to match FastAPI behavior (https://github.com/ray-project/ray/pull/48491)
• Fixed an issue where high values of max_ongoing_requests couldn’t be reached due to an interaction with core’s max_concurrency (https://github.com/ray-project/ray/pull/48274)
• Fixed an edge case where pending requests were not canceled properly (https://github.com/ray-project/ray/pull/47873)
• Removed deprecated API to set route_prefix per-deployment (https://github.com/ray-project/ray/pull/48223)

📖 Documentation:

• Added ProxyStatus model to reference docs (https://github.com/ray-project/ray/pull/48299)
• Added ApplicationStatus model to reference docs (https://github.com/ray-project/ray/pull/48220)

RLlib

💫 Enhancements:

• Upgrade to gymnasium==1.0.0 (support new API for vector env resets). (#​48443, #​45328)
• Add off-policy'ness metric to new API stack. (#​48227)
• Validate episodes before adding them to the buffer. (#​48083)

📖 Documentation:

• New example script for custom metrics on EnvRunners (using MetricsLogger API on the new stack). (#​47969)
• Do-over: New RLlib index page. (#​48285, #​48442)
• Do-over: Example script for AutoregressiveActionsRLM. (#​47972)

🏗 Architecture refactoring:

• New API stack on by default for PPO. (#​48284)
• Change config.fault_tolerance default behavior (from recreate_failed_env_runners=False to True). (#​48286)

🔨 Fixes:

• Various bug and CI fixes: #​47993, #​48450, #​48213
• Cleanup evaluation folder (#​48493)

Ray Core

🎉 New Features:

• [CompiledGraphs] Support all reduce collective in aDAG (#​47621)
• [CompiledGraphs] Add visualization of compiled graphs (#​47958)

💫 Enhancements:

• [Distributed Debugger] The distributed debugger can now be used without having to set RAY_DEBUG=1, see https://github.com/ray-project/ray/pull/48301 and https://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.html. If you want to restore the previous behavior and use the CLI based debugger, you need to set RAY_DEBUG=legacy.
• [Core] Add more infos to each breakpoint for ray debug CLI (#​48202)
• [Core] Add demands info to GCS debug state (#​48115)
• [Core] Add PENDING_ACTOR_TASK_ARGS_FETCH and PENDING_ACTOR_TASK_ORDERING_OR_CONCURRENCY TaskStatus (#​48242)
• [Core] Add metrics ray_io_context_event_loop_lag_ms. (#​47989)
• [Core] Better log format when show the disk size (#​46869)
• [CompiledGraphs] Support asyncio.gather on multiple CompiledDAGFutures (#​47860)
• [CompiledGraphs] Raise an exception if a leaf node is found during compilation (#​47757)

🔨 Fixes:

• [Core] Posts CoreWorkerMemoryStore callbacks onto io_context to fix deadlock (#​47833)

Dashboard

🔨 Fixes:

• [Dashboard] Reworking dashboard_max_actors_to_cache to RAY_maximum_gcs_destroyed_actor_cached_count (#​48229)

Thanks

Many thanks to all those who contributed to this release!

@​akyang-anyscale, @​rkooo567, @​bveeramani, @​dayshah, @​martinbomio, @​khluu, @​justinvyu, @​slfan1989, @​alexeykudinkin, @​simonsays1980, @​vigneshka, @​ruisearch42, @​rynewang, @​scottjlee, @​jjyao, @​JoshKarpel, @​win5923, @​MengjinYan, @​MortalHappiness, @​ujjawal-khare-27, @​zcin, @​ccoulombe, @​Bye-legumes, @​dentiny, @​stephanie-wang, @​LeoLiao123, @​dengwxn, @​richo-anyscale, @​pcmoritz, @​sven1977, @​omatthew98, @​GeneDer, @​srinathk10, @​can-anyscale, @​edoakes, @​kevin85421, @​aslonnie, @​jeffreyjeffreywang, @​ArturNiederfahrenhorst

v2.38.0

Compare Source

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

v2.37.0

Compare Source

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

v2.36.1

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Ray Core

🔨 Fixes:

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

v2.36.0

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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

v2.35.0

Compare Source

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]

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[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

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