chore(deps): update dependency ray to ~=2.49.0

[red-hat-konflux]

This PR contains the following updates:

Package	Change	Age	Confidence
ray	~=2.47.1 -> ~=2.49.0
ray	~=2.47.0 -> ~=2.49.0

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Release Notes

ray-project/ray (ray)

v2.49.0

Compare Source

Release Highlights

Ray Data:

• We’ve implemented a variety of performance enhancements, including improved actor/node autoscaling with budget-aware decisions; faster/more accurate shuffle accounting; reduced Parquet metadata footprint; and out-of-order execution for higher throughput.
• We’ve also implemented anti/semi joins, stratified train_test_split, and added Snowflake connectors.

Ray Core:

• Performance/robustness cleanups around GCS publish path and raylet internals; simpler OpenTelemetry flagging; new user-facing API to wait for GPU tensor free; plus assorted test/infra tidy-ups

Ray Train:

• We’ve introduced a new JaxTrainer with SPMD support for TPUs.

Ray Serve:

• Custom Autoscaling per Deployment Serve now supports user-defined autoscaling policies via AutoscalingContext and AutoscalingPolicy, enabling fine-grained scaling logic at the deployment level. This is part of a large effort where we are adding support for autoscaling based on custom metrics in Serve, see this RFC for more details.
• Async Inference (Initial Support): Ray Serve introduces asynchronous inference execution, laying the foundation for better throughput and latency in async workloads. Please see this RFC for more details.
• Major Performance Gains: This version of ray serve brings double digit % performance improvements both in throughput and latency. See release notes for more details.

Ray Serve/Data LLM:

• We’ve refactored Ray Serve LLM to be fully compatible with the default vllm serve and also now supports vLLM=0.10.
• We’ve added a prefix cache-aware router with PrefixCacheAffinityRouter for optimized cache utilization; dynamic cache management via reset prefix cache remote methods; enhanced LMCacheConnectorV1 with kv_transfer_config support.

Ray Libraries

Ray Data

🎉 New Features:

• Wrapped batch indices in a BatchMetadata object to make per-batch metadata explicit. (#​55643)
• Added support for Anti/Semi Join types. (#​55272)
• Introduced an Issue Detection Framework. (#​55155)
• Added an option to enable out-of-order execution for better performance. (#​54504)
• Introduced a StreamingSplit logical operator for DAG rewrite. (#​54994)
• Added a stratify parameter to train_test_split. (#​54624)
• Added Snowflake connectors. (#​51429)
• Updated Hudi integration to support incremental query. (#​54301)
• Added an Actor location tracker. (#​54590)
• Added BundleQueue.has_next. (#​54710)
• Made DEFAULT_OBJECT_STORE_MEMORY_LIMIT_FRACTION configurable. (#​54873)
• Added Expression support & a with_columns API. (#​54322)
• Allocate GPU resources in ResourceManager. (#​54445)

💫 Enhancements:

• Decoupled actor and node autoscaling; autoscaling now also considers budget. (#​55673, #​54902)
• Faster hash-shuffle resource usage calculation; more accurate shuffle progress totals. (#​55503, #​55543)
• Reduced Parquet metadata storage usage. (#​54821)
• Export API improvements: refresh dataset/operator state, sanitize metadata, and truncate exported metadata. (#​55355, #​55379, #​55216, #​54623)
• Metrics & observability: task metric improvements, external-buffer block-count metric, row-based metrics, clearer operator names in logs, single debug log when aggregators are ready. (#​55429, #​55022, #​54693, #​52949, #​54483)
• Dashboard: added “Max Bytes to Read” panel/budget, panels for blocks-per-task and bytes-per-block, and streaming executor duration. (#​55024, #​55020, #​54614)
• Planner/execution & infra cleanups: ExecutionResources and StatsManager cleanup, planner interface refactor, node trackers init, removed ray.get in _MapWorker ctor, removed target_shuffle_max_block_size. (#​54694, #​55400, #​55018, #​54665, #​54734, #​55158)
• Behavior/interop tweaks: map_batches defaults to row_modification=False and avoids pushing past limit; limited operator pushdown; prefetch for PandasJSONDatasource; use cloudpickle for Arrow tensor extension ser/des; bumped Arrow to 21.0; schema warning tone change. (#​54992, #​54457, #​54667, #​54831, #​55426, #​54630)
• Removed randomize-blocks reorder rule for more stable behavior. (#​55278)

🔨 Fixes:

• AutoscalingActorPool now properly downscales after execution. (#​55565)
• StatsManager handles StatsActor loss on disconnect. (#​55163)
• Handle missing chunks key when Databricks UC query returns zero rows. (#​54526)
• Handle empty fragments in sampling when num_row_groups=0. (#​54822)
• Restored handling of PyExtensionType to keep compatibility with previously written datasets. (#​55498)
• Prevent negative resource budget when concurrency exceeds the global limit; fixed resource-manager log calculation. (#​54986, #​54878)
• Default write_parquet warning removed; handled unhashable types in OneHotEncoding. (#​54864, #​54863)
• Overwrite mode now maps to the correct Arrow behavior for parallel writes. (#​55118)
• Added back from_daft Arrow-version checks. (#​54907)
• Pandas chained in-place assignment warning resolved. (#​54486)
• Test stability/infra: fixed flaky tests, adjusted bounds and sizes, added additional release tests/chaos variants for image workloads, increased join test size, adjusted sorting release test to produce 1 GB blocks. (#​55485, #​55489, #​54806, #​55120, #​54716, #​55402, #​54971)

📖 Documentation:

• Added a user guide for aggregations. (#​53568)
• Added a code snippet in docs for partitioned writes. (#​55002)
• Updated links to Lance documentation. (#​54836)

Ray Train

🎉 New Features:

• Introduced JaxTrainer with SPMD support on TPUs (#​55207)

💫 Enhancements:

• ray.train.get_dataset_shard now lazily configures dataset sharding for better startup behavior (#​55230)
• Clearer worker error logging (#​55222)
• Fail fast when placement group requirements can never be satisfied (#​54402)
• New ControllerError surfaced and handled via failure policy for improved resiliency (#​54801, #​54833)
• TrainStateActor periodically checks controller health and aborts when necessary (#​53818)

🔨 Fixes:

• Resolve circular import in ray.train.v2.lightning.lightning_utils (#​55668)
• Fix XGBoost v2 callback behavior (#​54787)
• Suppress a spurious type error (#​50994)
• Reduce test flakiness: remove randomness and bump a data-integration test size (#​55315, #​55633)

📖 Documentation:

• New LightGBMTrainer user guide (#​54492)
• Fix code-snippet syntax highlighting (#​54909)
• Minor correction in experiment-tracking guide comment (#​54605)

🏗 Architecture refactoring:

• Public Train APIs routed through TrainFnUtils for consistency (#​55226)
• LoggingManager utility for Train logging (#​55121)
• Convert DEFAULT variables from strings to bools (#​55581)

Ray Tune

🎉 New Features:

• Add video FPS support to WandbLoggerCallback (#​53638)

💫 Enhancements:

• Typing: reset_config now explicitly returns bool (#​54581)
• CheckpointManager supports recording scoring metric only (#​54642)

🔨 Fixes:

• Fix XGBoost v2 callback integration (#​54787)
• Correct type for RunConfig.progress_reporter (#​48439)

📖 Documentation:

• Minor fixes (#​55125, #​54942)

Ray Serve

🎉 New Features:

• Async inference support in Ray Serve (initial phase). Provides basic asynchronous inference execution, with follow-up work planned for failed/unprocessed queues and additional tests. #​54824
• Per-deployment custom autoscaling controls. Introduces AutoscalingContext and AutoscalingPolicy classes, enabling user-defined autoscaling strategies at the deployment level. #​55253
• Same event loop router. Adds option to run the Serve router in the same event loop as the proxy, yielding ~17% throughput improvement. #​55030

💫 Enhancements:

• Async get_current_servable_instance(). Converts the FastAPI dependency to async def, removing threadpool overhead and boosting performance: 35% higher RPS and reduced latency. #​55457
• Access log optimization. Cached contexts in request path logging improved request throughput by ~16% with lower average latency. #​55166
• Batching improvements. Default batch wait timeout increased from 0.0s to 0.01s (10ms) to enable meaningful batching. #​55126
• HTTP receive refactor. Cleaned up handling of replica-side HTTP receive tasks. #​54543 / #​54565
• Configurable replica router backoff. Added knobs for retry/backoff control when routing to replicas. #​54723
• Autoscaling ergonomics. Marked per-deployment autoscaling metrics push interval config as deprecated for consistency. #​55102
• Health check & env var safety. Introduced warnings for invalid/zero/negative environment variable values, with migration path planned for Ray 2.50.0. #​55464, #​54944
• Improved CLI UX. serve config now prints No configuration was found. instead of an empty string. #​54767

🔨 Fixes:

• Removed brittle ray._private dependency usage. #​55659
• HTTP route test fixes. Migrated to get_application_url() to avoid hardcoded URLs, reducing flakiness on Windows. #​55623, #​54974, #​54924, #​54911, #​54704, #​54903, #​54882, #​54877, #​54631, #​53933
• Semaphore bug fix. Corrected race where more workers than allowed could acquire the semaphore. #​55147
• LongPollClient cancellation. Prevented spurious cancellation of listen_for_change. #​54832
• Backpressure error code. gRPC now returns RESOURCE_EXHAUSTED instead of UNAVAILABLE on overload. #​54537
• Logging improvements. Added request IDs to proxy access logs; avoided duplicate shutdown log lines. #​54657, #​54534
• Test stability. Various waits, deflakes, and sync fixes across Serve tests. #​54794, #​54522, #​54585

📖 Documentation:

• Unexpected queuing behavior. Documented quirks in handle request queuing. #​54542

🏗 Architecture refactoring:

• Router/handle internals refactored for clarity and future feature expansion. #​55635
• Model composition benchmarks. Added benchmarking to track performance of common composition patterns. #​55549
• Constants refactor. Utility functions moved out of constants.py for better readability and stricter env var validation. #​54944, #​55464
• Ray internals migration. Moved usage, ray_option_utils, and selected constants from _private to _common. #​54915, #​54578

Ray Serve/Data LLM

🎉 New Features:

• Prefix cache-aware router with PrefixCacheAffinityRouter for optimized cache utilization. (#​55218, #​55588)
• Reset prefix cache remote method for dynamic cache management. (#​55658)
• LMCacheConnectorV1 support for kv_transfer_config to enhance key-value transfer configurations. (#​54579)
• LLMServer and LLMEngine major refactor for 100% vLLM serve frontend compatibility. (#​54554)

💫 Enhancements:

• vLLM engine upgrade to version 0.10.0 with improved performance and compatibility. (#​55067)
• Enhanced error handling for invalid model_id parameters with clearer error messages. (#​55589)
• Improved telemetry handling with better race condition management for push operations. (#​55558)
• Optimized deployment defaults with better configuration values to prevent bottlenecks. (#​54696)
• LoRA workflow improvements with refactored downloading and utility functions. (#​54946)
• LLMServer refactor to synchronous initialization for better reliability. (#​54835)
• Mistral tokenizer support for tekken tokenizer compatibility. (#​54666)
• Smart batching logic that skips batching when batch_interval_ms == 0. (#​54751)
• Dashboard enhancements with improved LLM metrics and monitoring capabilities. (#​54797)

🔨 Fixes:

• Pyright linting corrections for Ray Serve LLM examples. (#​55284)
• Test stability improvements for DeepSeek model and vLLM engine processor tests. (#​55401, #​55120)
• Serialization fixes for ChatCompletionRequest tool_calls ValidatorIterator objects. (#​55538)

📖 Documentation:

• Prefix cache router documentation with comprehensive usage examples. (#​55218)
• Multi-LoRA documentation improvements with clearer setup instructions. (#​54788)
• STRICT_PACK strategy FAQ documentation explaining data.llm packing behavior. (#​55505)

🏗 Architecture refactoring:

• Docker image optimizations with UCX and NCCL updates, plus GKE GPU operator compatibility paths. (#​54598, #​55206)

RLlib

🎉 New Features:

• Implemented Implicit Q-Learning (IQL). (#​55304, #​55422)
• DreamerV3 is now available in PyTorch. (#​45463, #​55140)
• Discrete actions support for SAC. (#​53982)

💫 Enhancements:

• Upgraded RLlink protocol for external env/simulator training. (#​53550)
• Performance improvements in Offline RL API through switching to iter_torch_batches. (#​54277)
• Added an example for curriculum learning in Atari Pong. (#​55304)

🔨 Fixes:

• Corrected TensorType handling. (#​55694)
• Fixed a bug with multi-learner setups in Offline RL API. (#​55693)
• Addressed ImportError in Atari examples. (#​54967)
• Fixed some bugs in the docs for IQL and CQL. (#​55614)
• Increased default timesteps on two experiments. (#​54185)
• Fixed TorchMultiCategorical.to_deterministic when having different number of categories and logits with time dimension. (#​54414)
• Added missing documentation for SACConfig's training(). (#​53918)
• Fixed bug in restore_from_path such that connector states are also restored on remote EnvRunners. (#​54672)
• Fixed missing support for config.count_steps_by = "agent_steps". (#​54885)
• Added missing colon to CUBLAS_WORKSPACE_CONFIG. (#​53913)
• Removed rllib_contrib completely from RLlib. (#​55182)

🏗 Architecture refactoring:

• Deprecated TensorFlow support from new API stack. (#​55042)
• Deprecated input/output specs from RLModule. (#​55141)
• Deprecated --enable-new-api-stack flag from all scripts. (#​54853, #​54702)

Ray Core

🎉 New Features:

• [Core] Add AcceleratorManager implementation for Rebellions NPU (#​53985)

💫 Enhancements:

• [core][gpu-objects] Garbage collection (#​53911)
• [core] Support pip_install_options for pip (#​53551)
• [core][gpu-objects] Move data transfers to a background thread (#​54256)
• [core][gpu-objects] Pass tensor_transport to store_task_errors even if the actor task throws an exception (#​55427)
• [core][gpu-objects] Exception handling for application errors (#​55442)
• [core][gpu-object] Add a user-facing call to wait for tensor to be freed (#​55076)
• [Core] Bind ray internal servers to the specified node ip instead of 0.0.0.0 which improves the security (#​55178, #​55210, #​55298)
• [core] Fallback unserializable exceptions to their string representation (#​55476)

🔨 Fixes:

• [core] Fix objects_valid check failure with except from BaseException (#​55602)
• [core][gpu-objects] Avoid triggering a KeyError by the GPU object GC callback for intra-actor communication (#​54556)
• [core] fix checking for uv existence during ray_runtime setup (#​54141)
• [core][autoscaler][v1] add heartbeat timeout logic to determine node activity status (#​54030)
• [core] prevent sending SIGTERM after calling Worker::MarkDead (#​54377)
• [Core] Fixed the bug where the head was unable to submit tasks after redis is turned on. (#​54267)
• [Core] [Azure] query for supported Microsoft.Network/virtualNetworks API versions instead of relying on resource_client.DEFAULT_API_VERSION (#​54874)
• [core] Fix possible race by checking node cache status instead of just subscription (#​54745)
• [core] Fix get actor timeout multiplier (#​54525)
• [core]: Use a temporary file to share default worker path in runtime env (#​53653)
• [core] Fix check fail when task buffer periodical runner runs before RayEvent is initialized (#​55249)
• [core] Patch grpc with RAY_num_grpc_threads to control grpc thread count (#​54988)
• [core][gpu-objects] Always write to GPUObjectStore to avoid _get_tensor_meta() from hanging indefinitely. (#​55433)
• [Core] Core Worker GetObjStatus GRPC Fault Tolerance (#​54567)

📖 Documentation:

• Added guide on using type hints with Ray Core. (#​55013)

🏗 Architecture refactoring:

• Migrate metric collection from opencensus to opentelemetry (#​53098, #​53740)

Dashboard

💫 Enhancements:

• Grafana: new Operator filter for Data; Prometheus adds RayNodeType label on for nodes. (#​55493, #​55192)

🔨 Fixes:

• Removed references to a deleted Data metrics panel. (#​55478)

Ray Images

🎉 New Features:

• Added cuda 12.6 based images (#​55425, #​55444)

💫 Enhancements:

• Upgraded protobuf to v4 (#​54496)

Docs

💫 Enhancements:

• KubeRay docs: added InteractiveMode quick-start details; expanded Core type-hints guidance; Serve LLM example coverage; Data LLM batching FAQ (#​55570, #​55284)

🔨 Fixes:

• Various formatting/mis-highlighting and lints across Train/Tune/Serve LLM docs. (#​55284, #​54763)

Thanks!

Thank you to everyone who contributed to this release!
@​pavitrabhalla, @​Daraan, @​Sparks0219, @​daiping8, @​abrarsheikh, @​sven1977, @​Toshaksha, @​bveeramani, @​MengjinYan, @​GokuMohandas, @​codope, @​nadongjun, @​SolitaryThinker, @​matthewdeng, @​elliot-barn, @​isimluk, @​avibasnet31, @​OneSizeFitsQuorum, @​Future-Outlier, @​marosset, @​jackfrancis, @​kshanmol, @​eicherseiji, @​dayshah, @​iamjustinhsu, @​Qiaolin-Yu, @​goutamvenkat-anyscale, @​Yicheng-Lu-llll, @​yantarou, @​rclough, @​zcin, @​NeilGirdhar, @​VarunBhandary, @​400Ping, @​akshay-anyscale, @​vickytsang, @​xushiyan, @​JasonLi1909, @​n-elia, @​simonsays1980, @​dragongu, @​Kishanthan, @​ruisearch42, @​jectpro7, @​TimothySeah, @​liulehui, @​rueian, @​HollowMan6, @​akyang-anyscale, @​axreldable, @​czgdp1807, @​alanwguo, @​justinvyu, @​ok-scale, @​my-vegetable-has-exploded, @​landscapepainter, @​fscnick, @​machichima, @​mpashkovskii, @​ZacAttack, @​gvspraveen, @​sword865, @​lmsh7, @​Ziy1-Tan, @​rebel-scottlee, @​sampan-s-nayak, @​coqian, @​can-anyscale, @​Bye-legumes, @​win5923, @​MortalHappiness, @​angelinalg, @​khluu, @​aslonnie, @​krishnakalyan3, @​minosvasilias, @​x-tong, @​xinyuangui2, @​raulchen, @​Yangruipis, @​edoakes, @​kevin85421, @​wingkitlee0, @​Fokko, @​cristianjd, @​srinathk10, @​owenowenisme, @​JoshKarpel, @​MengqingCao, @​leopardracer, @​westonpace, @​LeslieWongCV, @​VassilisVassiliadis, @​crypdick, @​alexeykudinkin, @​mjacar, @​kunling-anyscale, @​saihaj, @​kouroshHakha, @​ema-pe, @​markjm, @​avigyabb, @​dshepelev15, @​mauvilsa, @​omatthew98, @​nrghosh, @​ryanaoleary, @​Aydin-ab, @​lk-chen, @​stephanie-wang, @​harshit-anyscale, @​jjyao, @​bullgom, @​Yevet, @​israbbani

v2.48.0

Compare Source

Release Highlights

• Ray Data: This release features a new Delta Lake and Unity Catalog integration and performance improvements to various reading/writing operators.
• Ray Core: Enhanced GPU object support with intra-process communication and improved Autoscaler v2 functionality
• Ray Train: Improved hardware metrics integration with Grafana and enhanced collective operations support
• Ray Serve LLM: This release features early proof of concept for prefill-decode disaggregation deployment and LLM-aware request routing such as prefix-cache aware routing.
• Ray Data LLM: Improved throughput and CPU memory utilization for ray data workers.

Ray Libraries

Ray Data

🎉 New Features:

• Add reading from Delta Lake tables and Unity Catalog integration (#​53701)
• Enhanced pin_memory support in iter_torch_batches (#​53792)
• Add pin_memory to iter_torch_batches (#​53792)

💫 Enhancements:

• Re-enabled sorting in Ray Data tests with performance improvements (#​54475)
• Enhanced handling of mismatched columns and pandas.NA values (#​53861, #​53859)
• Improved read_text trailing newline semantics (#​53860)
• Optimized backpressure handling with policy-based resource management (#​54376)
• Enhanced write_parquet with support for both partition_by and row limits (#​53930)
• Prevent filename collisions on write operations (#​53890)
• Improved execution performance for One Hot encoding in preprocessors (#​54022)

🔨 Fixes:

• Fixing map_groups issues (#​54462)
• Prevented Op fusion for streaming repartition to avoid performance degradation (#​54469)
• Fixed ActorPool autoscaler scaling up logic (#​53983)
• Resolved empty dataset repartitioning issues (#​54107)
• Fixed PyArrow overflow handling in data processing (#​53971, #​54390)
• Fixed IcebergDatasink to properly generate individual file uuids (#​52956)
• Avoid OOMs with read_json(..., lines=True) (#​54436)
• Handle HuggingFace parquet dataset resolve URLs (#​54146)
• Fixed BlockMetadata derivation for Read operator (#​53908)

📖 Documentation:

• Updated AggregateFnV2 documentation to clarify finalize method (#​53835)
• Improved preprocessor and vectorizer API documentation

Ray Train

🎉 New Features:

• Added broadcast_from_rank_zero and barrier collective operations (#​54066)
• Enhanced hardware metrics integration with Grafana dashboards (#​53218)
• Added support for dynamically loading callbacks via environment variables (#​54233)

💫 Enhancements:

• Improved checkpoint population from before_init_train_context (#​54453)
• Enhanced controller state logging and metrics (#​52805)
• Added structured logging environment variable support (#​52952)
• Improved handling of Noop scaling decisions for smoother scaling logic (#​53180)
• Logging of controller state transitions to aid in debugging and analysis (#​53344)

🔨 Fixes:

• Fixed GPU tensor reporting in ray.train.report (#​53725)
• Enhanced move_tensors_to_device utility for complex tensor structures (#​53109)
• Improved worker health check error handling with trace information (#​53626)
• Fixed GPU transfer support for non-contiguous tensors (#​52548)
• Force abort on SIGINT spam and do not abort finished runs (#​54188)

📖 Documentation:

• Updated beginner PyTorch example (#​54124)
• Added documentation for ray.train.collective APIs (#​54340)
• Added a note about PyTorch DataLoader's multiprocessing and forkserver usage (#​52924)
• Fixed various docstring format and indentation issues (#​52855, #​52878)
• Added note that ray.train.report API docs should mention optional checkpoint_dir_name (#​54391)

🏗 Architecture refactoring:

• Removed subclass relationship between RunConfig and RunConfigV1 (#​54293)
• Enhanced error handling for finished training runs (#​54188)
• Deduplicated ML doctest runners in CI for efficiency (#​53157)
• Converted isort configuration to Ruff for consistency (#​52869)

Ray Tune

💫 Enhancements:

• Updated test_train_v2_integration to use the correct RunConfig (#​52882)

🔨 Fixes:

• Fixed RayTaskError serialization logic (#​54396)
• Improved experiment restore timeout handling (#​53387)

📖 Documentation:

• Replaced session.report with tune.report and corrected import paths (#​52801)
• Removed outdated graphics cards reference in docs (#​52922)
• Fixed various docstring format issues (#​52879)

Ray Serve

🎉 New Features:

• Added RouterConfig field to DeploymentConfig for custom RequestRouter configuration (#​53870)
• Added support for implementing custom request routing algorithms (#​53251)

💫 Enhancements:

• Enhanced FastAPI ingress deployment validation for multiple deployments (#​53647)
• Optimized get_live_deployments performance (#​54454)
• Progress towards making ray.serve.llm compatible with vLLM serve frontend (#​54481, #​54443, #​54440)

🔨 Fixes:

• Fixed deployment scheduler issues with component scheduling (#​54479)
• Fixed runtime_env validation for py_modules (#​53186)
• Added descriptive error message when deployment name is not found (#​45181)

📖 Documentation:

• Added troubleshooting guide for DeepSeek/multi-node GPU deployment on KubeRay (#​54229)
• Updated the guide on serving models with Triton Server in Ray Serve
• Added documentation for custom request routing algorithms
• Added custom request router docs (#​53511)

🏗 Architecture refactoring:

• Remove indirection layers of node initialization (#​54481)
• Incremental refactor of LLMEngine (#​54443)
• Remove random v0 logic from serve endpoints (#​54440)
• Remove usage of internal_api.memory_summary() (#​54417)
• Remove usage of ray._private.state (#​54140)

Ray Serve/Data LLM

🎉 New Features

• Support separate deployment config for PDProxy in PrefixAwareReplicaSet (#​53935)
• Support for prefix-aware request router (#​52725)

💫 Enhancements

• Log engine stats after each batch task is done. (#​54360)
• Decouple max_tasks_in_flight from max_concurrent_batches (#​54362)
• Make llm serve endpoints compatible with vLLM serve frontend, including streaming, tool_code, and health check support (#​54440)
• Remove botocore dependency in Ray Serve LLM (#​54156)
• Update vLLM version to 0.9.2 (#​54407)

🔨 Fixes

• Fix health check in prefill disagg (#​53937)
• Fix doc to only support int concurrency (#​54196)
• Fix vLLM batch test by changing to Pixtral (#​53744)
• Fix pickle error with remote code models in vLLM Ray workloads (#​53868)
• Adaption of the change of vllm.PoolingOutput (#​54467)

📖 Documentation

• Ray serve/lora doc fix (#​53553)
• Add Ray serve/LLM doc (#​52832)
• Add a doc snippet to inform users about existing diffs between vLLM and Ray Serve LLM behavior in some APIs like streaming, tool_code, and health check (#​54123)
• Troubleshooting DeepSeek/multi-node GPU deployment on KubeRay (#​54229)

🏗 Architecture refactoring

• Make llm serve endpoints compatible with vLLM serve frontend, including streaming, tool_code, and health check support (#​54490)
• Prefix-aware scheduler [2/N] Configure PrefixAwareReplicaSet to correctly handle the number of available GPUs for each worker and to ensure efficient GPU utilization in vLLM (#​53192)
• Organize spread out utils.py (#​53722)
• Remove ImageRetriever class and related tests from the LLM serving codebase. (#​54018)
• Return a batch of rows in the udf instead of row by row (#​54329)

RLlib

🎉 New Features:

• Implemented Offline Policy Evaluation (OPE) via Importance Sampling (#​53702)
• Enhanced ConnectorV2 ObservationPreprocessor APIs with multi-agent support (#​54209)
• Add GPU inference to offline evaluation (#​52718)

💫 Enhancements:

• Enhanced MetricsLogger to handle tensors in state management (#​53514)
• Improved env seeding in EnvRunners with deterministic training example rewrite (#​54039)
• Cleanup of meta learning classes and examples (#​52680)

🔨 Fixes:

• Fixed EnvRunner restoration when no local EnvRunner is available (#​54091)
• Fixed shapes in explained_variance for recurrent policies (#​54005)
• Resolved device check issues in Learner implementation (#​53706)
• Enhanced numerical stability in MeanStdFilter (#​53484)
• Fixed weight synching in offline evaluation (#​52757)
• Fixed bug in split_and_zero_pad utility function (#​52818)

📖 Documentation:

• Do-over of examples for connector pipelines (#​52604)
• Remove "new API stack" banner from all RLlib docs pages as it's now the default (#​54282)

Ray Core

🎉 New Features:

• Enhanced GPU object support with intra-process communication (#​53798)
• Integrated single-controller collective APIs with GPU objects (#​53720)
• Added support for ray.get on driver process for GPU objects (#​53902)
• Supporting allreduce on list of input nodes in compiled graphs (#​51047)
• Add single-controller API for ray.util.collective and torch gloo backend (#​53319)

💫 Enhancements:

• Improved autoscaler v2 functionality with cloud instance ID reusing (#​54397)
• Enhanced cluster task manager with better resource management (#​54413)
• Upgraded OpenTelemetry SDK for better observability (#​53745)
• Improved actor scheduling to prevent deadlocks in ordered actors (#​54034)
• Enhanced get_max_resources_from_cluster_config functionality (#​54455)
• Use std::move in cluster task manager constructor (#​54413)
• Improve status messages and add comments about stale seq_no handling (#​54470)
• uv run integration is now enabled by default (#​53060)

🔨 Fixes:

• Fixed race conditions in object eviction and repinning for recovery (#​53934)
• Resolved GCS crash issues on duplicate MarkJobFinished RPCs (#​53951)
• Enhanced actor restart handling on node failures (#​54088)
• Improved reference counting during worker graceful shutdown (#​53002)
• Fix race condition when canceling task that hasn't started yet (#​52703)
• Fix the issue where a valid RestartActor rpc is ignored (#​53330)
• Fixed "Check failed: it->second.num_retries_left == -1" error (#​54116)
• Fix detached actor being unexpectedly killed (#​53562)

📖 Documentation:

• Enhanced troubleshooting guides and API documentation
• Updated reStructuredText

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