github ray-project/ray ray-2.50.0
Ray-2.50.0

one day ago

Release Highlights

Ray Data:
This release offers many updates to Ray Data, including:

  • The default shuffle strategy is now changed from sort-based to hash-based. This will result in much lower peak memory usage and improved shuffle performance for aggregations.
  • We’ve added a new expression API enables predicate-based filtering, UDF transformations with with_column, and column aliasing for more powerful data transformations
  • Ray Data LLM has a number of new enhancements for multimodal data pipelines, including multi-node tensor and pipeline parallelism support per replica and ability to share vLLM engines across processors.

Ray Core:

Alpha release of Ray Direct Transport (formerly GPU objects) - simply enable it by adding the tensor_transport parameter to the existing native Ray Core API. This keeps GPU data in GPU memory until a transfer is needed, avoiding expensive serialization and copies to and from the Ray object store. It uses efficient data transports such as collective communication libraries (GLOO or NCCL) or point-to-point RDMA (via NVIDIA’s NIXL) to transfer data directly between devices, including both CPUs and GPUs.

Ray Train:

Local mode support for multi-process training with torchrun, enhanced checkpoint management with new upload modes and validation functions

Ray Serve:

  • Async Inference alpha release - New Ray Serve APIs for supporting long-running asynchronous inference tasks, such as for video or large document processing. Includes capabilities for using different message brokers, adapters like celery and DLQ.
  • Support for replica ranks - Replica level ranks are added for supporting large-model inference use-cases such as wide Data Parallel and Expert Parallel setups.
  • FastAPI factory pattern support - Enables using FastAPI plugins that are not serializable via cloudpickle.
  • Throughput optimizations - Enable these using the RAY_SERVE_THROUGHPUT_OPTIMIZED environment variable.

RLLib:
Add StepFailedRecreateEnv exception for users with unsatisfiable environments

Ray Serve/Data LLM:

Improvements to multi node serving, loading models from remote storages, and sharing resources for efficiency (fractional gpus, sharing gpus on a data pipeline with shared stages)

Ray Libraries

Ray Data

🎉 New Features:

  • Expression and Filtering API: New expression API enables predicate-based filtering, UDF transformations with with_column, and column aliasing for more powerful data transformations (#56716, #56313, #56550, #55915, #55788, #56193, #56596)
  • Added support for projection pushdown into Parquet reads (#56500)
  • New download expression enables efficient loading of data from columns containing URIs with improved performance and error handling (#55824, #56462, #56294, #56852, #57146)
  • New explain() API provides insights into dataset execution plans (#55482)
  • Added streaming_train_test_split to avoid materialization for train/test splits (#56803)
  • Ray Data LLM:
    • Enabled multi-node tensor and pipeline parallelism for LLM processing (#56779)
    • Added chat_template_kwargs parameter for customizing chat templates (#56490)
    • Added support for OpenAI's nested image URL format in multimodal pipelines (#56584)
    • vLLM engines can now be shared across sequential processors for better resource utilization (#55179)
  • Enhanced Dataset.stats() output with input/output row counts per operator (#56040)
  • Added new metrics for task duration, inputs per task, and output blocks (#56958, #56379)
  • Time to first batch metric for better iteration performance monitoring (#55758)
  • Added type-specific aggregators for numerical, categorical, and vector columns (#56610)
  • Added fine-grained concurrency controls with max_task_concurrency and resource allocation options (#56370, #56381)

💫 Enhancements:

  • Join and shuffle improvements:
    • Default shuffle strategy changed from sort-based to hash-based for better performance (#55510)
    • Improved groupby performance with sort-shuffle pull-based approach (#57014)
    • Improved join operations with new abstractions (#57022, #56945, #55759)
  • Tensor type handling improvements:
    • Improved compatibility between PyArrow native types, extension types, and pandas Arrow dtypes (#57566, #57176, #57057)
    • Joins now supported with list/tensor non-key columns (#5648)
    • Enhanced support for variable-shaped tensor arrays with different dimensions (#57240, #56918, #56457)
    • Added serialization/deserialization for PyArrow Extension Arrays (#51972)
  • Removing Parquet metadata fetching in ParquetDatasource (#56105)
  • Resource requirements (num_cpus/gpus, memory) are now top-level parameters in most APIs for easier configuration (#56419)
  • zip() operator now supports combining multiple datasets, not just pairs (#56524)
  • Concurrency parameter now accepts tuples for more flexible configuration (#55867)
  • Write operations now use iterators instead of accumulating blocks in memory (#57108)
  • Reduced memory usage for OneHotEncoder (#56565)
  • Reduced memory usage for schema unification (#55880)
  • Eliminated unnecessary block copying and double execution of arrow conversions (#56569, #56793)
  • Improved Parquet encoding ratio estimation (#56268)
  • Enabled per-block limiting for Limit operator (#55239)
  • Optimized schema handling with deduplication and removed unnecessary unification (#55854, #55926)
  • Improved issue detection with event emission instead of just logs (#55717)
  • Better metric organization and external queue metric handling (#55495, #56604)
  • New backpressure policy based on downstream processing capacity (#55463)

🔨 Fixes:

  • Fixed streaming executor to properly drain output queues (#56941)
  • Improved resource management and reservation for operators (#56319, #57123)
  • Fixed retry logic for hash shuffle operations (#57575)
  • Fix split_blocks produce empty blocks (#57085)
  • Initialize datacontext after setting src_fn_name in actor worker (#57117)
  • Fix mongo datasource collStats invocation (#57027)
  • Fixing empty projection handling in ParquetDataSource (#56299)
  • Fix UnboundLocalError when read_parquet with columns and no partitioning (#55820)
  • Fix high memory usage with FileBasedDatasource & ParquetDatasource when using a large number of files (#55978)
  • [llm] Fixed LLM processor deployment with Ray Serve (#57061)
  • [llm] Fixed multimodal image extraction when system prompts are absent (#56435)
  • Ignore metadata for pandas block (#56402)
  • Remove metadata for hashing + truncate warning logs (#56093)

📖 Documentation:

  • Error in ray.data.groupby example in docs. (#57036)
  • Update on ray.data.Dataset.map() type hints. (#52455)
  • Small typo fix. (#56560)
  • Fix a typo. (#56587)
  • Fix documentation for new execution options resource limits assignment. (#56051)
  • Fix broken code snippets in user guides. (#55519)
  • Add Autoscaling Config for Context docs. (#55712)
  • Make object store tuning tips consistent with other pages. (#56705)
  • New example of how to perform batch inference with embedding models (#56027)

Ray Train

🎉 New Features:

  • Local mode support for Ray Train V2
    • Add local mode support to Ray Train v2 (num_workers=0). (#55487)
    • Add PyTorch local mode support for multi-process training with torchrun. (#56218)
  • Async checkpoint and validation for Ray Train
    • Add checkpoint_upload_mode to ray.train.report. (#55637)
    • Add checkpoint_upload_function to ray.train.report. (#56208)
    • Add validate_function and validate_config to ray.train.report. (#56360)
    • Add ray.train.get_all_reported_checkpoints method. (#54555)

💫 Enhancements:

  • Ray Train V2 Migration
    • Implement BaseWorkerGroup for V1/V2 compatibility. (#57151)
  • Train Controller is always actor + fix tune integration to enable this. (#55556)
  • Refactor AcceleratorSetupCallback to use before_init_train_context. (#56509)
  • Move collective implementations to train_fn_utils. (#55689)
  • Ray Train Framework support enhancements
    • Add hf trainer support for dictionary of datasets. (#56484)
    • Add usage tag key for JaxTrainer. (#55887)
  • Add Torch process group shutdown timeout. (#56182)
  • Ray Train disables blocking get inside async warning. (#56757)
  • ThreadRunner captures exceptions from nested threads. (#55756)
  • Abort reconciliation thread catches ray.util.state.get_actor exception. (#56600)
  • Ray Data Integration
    • Minor rework of get_dataset_shard. (#55825)
    • Create a deepcopy of the data context on the split coordinator process. (#56211)
    • Enable debug logging; fix default actor_locality_enabled. (#56632)
  • Refactor call_with_retry into shared library and use it to retry checkpoint upload. (#56608)
  • Remove Placement Group on Train Run Abort. (#56011)

🔨 Fixes:

  • Fix LightGBM v2 callbacks for Tune only usage. (#57042)
  • Ignore tensorflow test for py312. (#56244)
  • Revising test_jax_trainer flaky test. (#56854)
  • Fix test_jax_trainer imports. (#55799)
  • Fix test_jax_trainer::test_minimal_multihost Flaky Test. (#56548)
  • Disable drop_last flag to fix division by zero in torch dataloader baselines. (#56395)
  • Preload a subset of modules for torch dataloader forkserver multiprocessing. (#56343)

📖 Documentation:

  • Add checkpoint_upload_mode to checkpoint docs. (#56860)
  • Add get_all_reported_checkpoints and ReportedCheckpoint to API docs. (#56174)
  • Fix typo for Instantiating in ray train doc. (#55826)

🏗 Architecture refactoring:

  • Release tests for ray train local mode. (#56862)
  • Migrate tune_rllib_connect_test & tune_cloud_long_running_cloud_storage to ray train v2. (#56844)
  • Add v2 multinode persistence release test. (#56856)
  • Attach a quick checkpoint when reporting metrics. (#56718)
  • Upgrade tune_torch_benchmark to v2. (#56804)
  • Move tune_with_frequent_pausing to Ray Train v2 and tune_tests folder. (#56799)
  • Migrate xgboost/lgbm benchmarks to train V2. (#56792)

Ray Tune

🎉 New Features:

  • Trigger Checkpointing via Trial / Tuner Callback. (#55527)

💫 Enhancements:

  • Improve _PBTTrialState for dev/debugging usage. (#56890)
  • Enable Train V2 in Tune unit tests and examples. (#56816)
  • Enable Train v2 in doc examples. (#56820)
  • Reintroduce keras tune callback. (#57121)

🔨 Fixes:

  • Increase tune checkpoint test latency threshold. (#56251)
  • Remove a bunch of low-signal/redundant train/air/tune tests. (#56477)
  • Remove tune_air_oom test. (#57089)

Ray Serve

🎉 New Features:

  • Add tests and DLQ business logic for async inference. (#55608)
  • Foundation work for aggregating metrics on controller. (#55568)
  • Include custom metrics method and report to controller. (#56005)
  • Add post scaling api. (#56135)
  • Introduce deployment rank manager. (#55729)
  • Integrated deployment ranks with deployment state. (#55829)
  • Add rank and world size in replica context. (#55827)
  • Added ssl to ray serve. (#55228)
  • Custom parameter for downscaling to zero. (#56573)
  • Add optional APIType filter to /api/serve/applications/ endpoint. (#56458)
  • Make deployment retry configurable. (#56530)

💫 Enhancements:

  • Aggregate autoscaling metrics on controller. (#56306)
  • Update metrics_utils for future global metrics aggregation in controller. (#55568)
  • Use deployment method in access logs for replicas. (#56829)
  • Cache router metrics. (#55897)
  • Allow same event loop handle shutdown from sync context. (#55551)
  • Additional deps to start with prometheus. (#57155)
  • Require prefix RAY_SERVE_ for env vars + value verification. (#55864)
  • Record queued metrics on timeseries. (#57024)
  • Add throughput opt env var for serve. (#55804)
  • Fix None pending Request. (#54775)
  • Omit unnecessary newlines in the config generated by serve build app:app. (#56609)
  • Expose actor name for target group api. (#56738)

🔨 Fixes:

  • Fix proxy lua dependency in dockerfile. (#57221)
  • Fix non thread safe asyncio task creation in router. (#56124)
  • Fix throughput optimized benchmarks. (#56173)
  • Move ingress validation for multiple fastapi deployment into client. (#56706)
  • Explicitly close choose_replicas_with_backoff async generator. (#56357)
  • Fix buffered logging reusing request context. (#56094)
  • Use default gc frequency for proxy. (#56511)
  • Fixing deployment scoped custom autoscaling. (#56192)

📖 Documentation:

  • Stable links for Ray serve. (#56241)
  • Add document for using fastapi factory pattern in serve. (#56607)
  • Add documentation for async inference (#56453)

🏗 Architecture refactoring:

  • Add microbenchmark for throughput optimized configuration. (#55900)
  • Only checkpoint controller state when it is confirmed that target state has changed (#55848)
  • Proxy Actor Interface. (#56288)
  • Allow ProxyActor to return true/false for health check. (#56660)

Ray Serve/Data LLM

🎉 New Features:

  • Score API Integration for Serve LLM. (#55914)
  • Add start/stop_profile method to LLMServer. (#55920)
  • Add prefix cache hit rate to Serve LLM dashboard. (#55675)
  • Configure aggregation interval for dashboard. (#56591)

💫 Enhancements:

  • Bump vLLM to 0.10.2. (#56535)
  • Vllm bump -> 0.10.1.1. (#56099)
  • Refactor: Improve Deployment Builder Ergonomics and Code Organization. (#57181)
  • Fix build_llm_processor for ServeDeploymentProcessor. (#57061)
  • Allow setting data_parallel_size=1 in engine_kwargs. (#55750)
  • Allow tuple for concurrency arg. (#55867)
  • Fix multimodal image extraction when no system prompt is present. (#56435)
  • Support azure and abfss in LLM config. (#56441)
  • Support custom s3 endpoint when downloading models from remote. (#55458)
  • Skip safetensor file downloads for runai streamer mode. (#55662)
  • Support colocating local DP ranks in DPRankAssigner. (#55720)
  • Adjust LLM engine timing logic. (#55595)
  • Fixed DP DSV3 issues. (#55802)
  • Gracefully return timeouts as HTTPException. (#56264)
  • Remove upstreamed workarounds 1/3. (#54512)

🔨 Fixes:

  • Changed LMCache dependency to use 0.3.3 to avoid regressions in the release test. (#56104)
  • Fix doc test for Working with LLMs guide. (#55917)
  • Fix sglang byod on release. (#55885)

📖 Documentation:

  • Add gpt oss deployment example. (#56400)
  • Add serve llm example to index page + other minor fix. (#56788)
  • Example serve llm deployment. (#55819)
  • Fix serve llm examples. (#56382)
  • Docs: serve llm deployment examples refinement. (#56287)
  • Add example of serving a VLLM model on fractional gpu. (#57197)
  • Add main pytest code snippet to those tests that were missing it. (#57167)

RLlib

🎉 New Features:

  • Add StepFailedRecreateEnv exception. (#55146)

💫 Enhancements:

  • Add tags to envrunner calls, count in flight requests in ActorManager. (#56930, #56953)
  • Add spaces in case only offline data is used. (#56141)
  • Add Footsies environment and tests. (#55041)

🔨 Fixes:

  • Fix failing env step in MultiAgentEnvRunner. (#55567)
  • Fix Metrics/Stats lifetime count and throughput measurement for async remote actors. (#56047)
  • Fixes Implementation of Shared Encoder. (#54571)
  • Fix MetricsLogger/Stats throughput bugs. (#55696)

📖 Documentation:

  • [RLlib] [DOC] Fix documentation typos and grammatical issues in RLlib docs (#56130)
  • Update rllib-env.rst - typo. (#56140)
  • Fixing typo in the RLlib documentation. (#55752)
  • Fix formatting of class references. (#55764)

🏗 Architecture refactoring:

  • Remove checkpoint release tests. (#57105)
  • Remove long_running_apex test. (#57097)
  • LINT: Enable ruff imports for multiple directories in rllib. (#56736)
  • Upgrade g3 to g4 machine for aws release test. (#56248)

Ray Core

🎉 New Features:

  • Alpha release of Ray Direct Transport
    • Support ray.put() and ray.get() with nixl in gpu objects. (#56146)
    • Support using ray.get with nixl to retrieve data from GPU object refs created by remote tasks. (#56559)
    • Support tensor transfer from outside owners of actors. (#56485)
    • Automatically enable tensor transport for the actor if any method specifies one. (#55324)
    • Support cpu tensor transfer with NIXL in GPU Objects. (#55793)
    • Handle multiple transfers of the same object to an actor. (#55628)
    • Support NIXL as tensor transport backend. (#54459)
    • Add a user-facing call to wait for tensor to be freed. (#55076)
    • Always write to GPUObjectStore to avoid _get_tensor_meta() from hanging indefinitely. (#55433)
    • Add warning when GPU object refs passed back to the same actor. (#55639)
    • Avoid triggering a KeyError by the GPU object GC callback for intra-actor communication. (#54556)
  • Enable autoscaler v2 on clusters launched by the cluster launcher. (#55865)
  • Ray Symmetric Run Script and ray symmetric-run command. (#55111, #56497)

💫 Enhancements:

  • Ray Event Export
    • GCS AddEvent support. (#55528)
    • Actor event: add proto schema. (#56221)
    • Node event: add proto schema and send node events to the aggregator. (#56031, #56426)
    • Job event: add schema for driver job event and send job events to the aggregator. (#55032, #55213)
    • Emit actor events to Event aggregator. (#56617)
    • Export node event by default. (#56810)
  • Security
    • Bind ray internal servers to the specified node ip instead of 0.0.0.0. (#55178, #55210, #55298, #55484)
    • Bind dashboard agent http server to localhost in addition to the node ip. (#55910)
    • Bind dashboard agent grpc to specified ip instead of 0.0.0.0. (#55732)
    • Bind runtime env agent and dashboard agent http server to specified ip instead of 0.0.0.0. (#55431)
  • RPC network fault tolerance
    • Making core worker pub sub RPCs fault tolerant. (#56436)
    • Make RequestWorkerLease RPC Fault Tolerant. (#56191)
    • Making ReturnWorkerLease Idempotent. (#56073)
    • Making CancelWorkerLease RPC Fault Tolerant. (#56195)
    • Make Free Objects RPC Fault Tolerant. (#56293)
    • Make PinObjectIDs RPC Fault Tolerant. (#56443)
    • Make Unsubscribe Idempotent. (#57546)
    • Core Worker GetObjStatus GRPC Fault Tolerance. (#54567)
  • Not overriding accelerator id env vars when num_accelerators is 0 or not set. (#54928)
  • Migrate metric collection from opencensus to opentelemetry. (#53098, #53740)
  • Add per worker process group and deprecate process subreaper in favor of cleanup using process group. (#56476)
  • Add node_id validation in NodeAffinitySchedulingStrategy. (#56708)
  • Add io_context metrics to gcs and raylet. (#55762)
  • Modify RedisDelKeyPrefixSync to use the Redis SCAN command instead of KEYS. (#56907)
  • Add error_type to job failures. (#55578)
  • Add PID to structured logs for tasks and actors. (#55176)
  • Log actor name when warning about excess queueing. (#57124)
  • Output the error log on the driver side if the failed task will still retry. (#56472)
  • Prometheus http service discovery API. (#55656)
  • Add node ip in runtime env error message to improve debug observability. (#56837)
  • Fallback unserializable exceptions to their string representation. (#55476)
  • Introduce new exception type for un-pickleable exceptions. (#55878)
  • Improve docs for custom serialization for exceptions + add test. (#56156)
  • Add a warning when returning an object w/ num_returns=0. (#56213)
  • Adding ability to specify availability zones for ray cluster node pools on Azure. (#55532)
  • Query for supported Microsoft.Network/virtualNetworks API versions instead of relying on resource_client.DEFAULT_API_VERSION. (#54874)
  • Loosen Ray self-dependency check to allow matching versions. (#57019)
  • Add support for pip_install_options for pip. (#53551)
  • Proper typing for ObjectRef. (#55566)

🔨 Fixes:

  • Use subscription id from azure profile if not provided in config during AzureNodeProvider init. (#56640)
  • Always create standard public IP addresses (basic sku is deprecated). (#57131)
  • Fix: bug with config key pairs when launching worker nodes. (#57107)
  • If azure cluster launcher keypair doesnt exist create one automatically + doc typo fix. (#54596)
  • Fix "objects_valid" for the case that multiple instances of the same task are storing returns. (#54904)
  • Fix objects_valid check failure with except from BaseException. (#55602)
  • Preserve err type in case of task cancellation due to actor death. (#57538)
  • Fix checking for uv existence during ray_runtime setup. (#54141)
  • Prevent sending SIGTERM after calling Worker::MarkDead. (#54377)
  • Fixed the bug where the head was unable to submit tasks after redis is turned on. (#54267)
  • Fix possible race by checking node cache status instead of just subscription. (#54745)
  • Fix get actor timeout multiplier. (#54525)
  • Use a temporary file to share default worker path in runtime env. (#53653)
  • Fix check fail when task buffer periodical runner runs before RayEvent is initialized. (#55249)
  • Patch grpc with RAY_num_grpc_threads to control grpc thread count. (#54988)
  • Fix HandleRefRemoved thread safety. (#56445)
  • Fix error handling for plasma put errors. (#56070)
  • Fix batching logic in CoreWorkerPlasmaStoreProvider::Get. (#56041)
  • Fix RAY_CHECK failure during shutdown due to plasma store race condition. (#55367)
  • Fix autoscaler RAY_CHECK when GcsAutoscalerStateManager is out of sync with NodeManager. (#57010)
  • Fix bug where inflight requests are not taken into account by retryable. (#57142)
  • A timeout should be set when submitting patch requests for autoscaler. (#56605)
  • Fix the bug in memray regarding the default configuration of -o {output_file_path}. (#56732)
  • Fixed the issue of RemoveActorNameFromRegistry being called repeatedly. (#54955)
  • Fixed an issue where the command executed when use_podman=true and run_env=None was not prefixed with podman exec. (#56619)
  • Fix data race when using async gpu to gpu transfer. (#57112)
  • Retry + Make FreeActorObject idempotent. (#56447)
  • Fix check crash on gpu obj free if driver knows actor is dead. (#56404)
  • Handle system errors with a background monitor thread. (#56513)
  • Fix GPU metrics. (#56009)
  • Don't disconnect worker client on OBOD unless the worker is dead. (#57185)
  • Prevent stale GET request being registered if its lease was cleared. (#56766)
  • Drop messages received after ClientConnection::Close. (#56240)
  • Fix cancel race that leads to RAY_CHECK it->second.submitted_task_ref_count > 0. (#56123)
  • Reorder asyncio actor shutdown to terminate asyncio thread first. (#56827)
  • Fix actor import error message for async actors. (#55722)
  • Allow task manager access with submitter mutex + unify retry. (#56216)
  • Fix bug in restore_from_path such that connector states are also restored on remote EnvRunners. (#54672)
  • Fix S3 access issue in AKS. (#56358)
  • Add S3 public bucket fallback to handle NoCredentialsError. (#56334)
  • Fix ABFSS (Azure Blob File System Secure) protocol support problems during E2E test. (#56188)
  • Ray cluster commands (up, attach, status, etc) updates to work on Windows. (#54982)
  • Update cluster scheduler to handle label selector hard node id constraint. (#56235)

📖 Documentation:

  • Added guide on using type hints with Ray Core. (#55013)
  • Lifecycle of a task. (#55496)
  • Add OSS Document for Task Events. (#56203)
  • Fix Missing Events Issue in Task Events. (#55916)
  • Add docs for asyncio and object mutability. (#56790)
  • Update getting started and set up document for ray on vsphere. (#56954)
  • Docfix - rst annotation showing up in render. (#57104)
  • Add threading requirement to NodeProvider interface. (#56349)
  • Add guidance for matching Ray and Python versions with uv envs. (#56597)
  • Fix documentation typos, grammar, and terminology inconsistencies. (#56066, #56067, #56068, #56069, #56128, #56129, #56130, #56131, #56132, #56272, #56273, #56274, #56275, #56277, #56278, #56279)
  • Update SLURM docs with symmetric-run. (#56775)
  • Update Kueue integration documentation to include RayService & RayCluster support. (#56781)
  • Application Gateway for Containers as ingress to access Ray Cluster. (#56574)
  • Update DLAMI Information in aws.md. (#55702)

Dashboard

💫 Enhancements:

  • Use pynvml for GPU metrics. (#56000)
  • Default dashboard usability improvements. (#55620)
  • Make Ray Train Dashboard Panel Ids Static. (#55559)
  • Small fixes to Metrics Tab for kube-ray clusters. (#57149)
  • Add metadata to indicate full dashboard embedding is supported. (#56077)
  • Use ray node id instead of ip for profilinglink. (#55439)
  • Fix grafana dashboard generation bug. (#56346)
  • Catch OSError when detecting the GPU. (#56158)

🔨 Fixes:

  • Removed references to a deleted Data metrics panel. (#55478)
  • Fix typo in memory_utils and adjust display formatting for clarity. (#56217)

Ray Images

🎉 New Features:

  • Add support for building and publishing ray-extra images. (#56543)
  • Add ray-llm and ray-ml extra images. (#56800)
  • Build ray-extra images for aarch64. (#56818)
  • Add slim image to the image build matrix. (#55723)

💫 Enhancements:

  • Add haproxy binary, for ray serve use. (#56845)
  • Add ~/.local/bin to PATH in slim image. (#56920)
  • Remove slim's dependency on normal bases. (#56544)
  • Add label for ray version and commit. (#56493)
  • Refactor apt package installation. (#55701)
  • Allow using explicit base type. (#56545)
  • Add extra-test stage in image building. (#55725)
  • Add test rules for image building files. (#56554)
  • Add ray-llm image type check. (#56542)
  • Unify label and tag conventions. (#56189)
  • GKE GPU compat paths: PATH, LD_LIBRARY_PATH (temporarily). (#55569)
  • Stop publishing ray-ml images. (#57070)
  • Stop building and releasing x86 osx wheels. (#57077)

📖 Documentation:

  • Update latest Docker dependencies for 2.49.0 release. (#55966)
  • Update latest Docker dependencies for 2.49.2 release. (#56760)

Wheels and images

💫 Enhancements:

Thanks!

Thank you to everyone who contributed to this release!

@alexeykudinkin, @richardliaw, @nrghosh, @ljstrnadiii, @Daraan, @kouroshHakha, @Bye-legumes, @kamil-kaczmarek, @jugalshah291, @sampan-s-nayak, @jjyao, @Evelynn-V, @gangsf, @omatthew98, @TimothySeah, @kshanmol, @goutamvenkat-anyscale, @axreldable, @jiangwu300, @simonsays1980, @400Ping, @JasonLi1909, @chuang0221, @weiliango, @Myasuka, @win5923, @liulehui, @khluu, @ok-scale, @eicherseiji, @tianyi-ge, @MengjinYan, @kevin85421, @Yevet, @orangeQWJ, @vie-serendipity, @edoakes, @wyhong3103, @israbbani, @vickytsang, @HassamSheikh, @acrewdson, @czgdp1807, @daiping8, @carolynwang, @thc1006, @jeffreyjeffreywang, @Stack-Attack, @Catch-Bull, @elliot-barn, @Levi080513, @BestVIncent, @dragongu, @jmajety-dev, @jcarlson212, @tohtana, @abrarsheikh, @crypdick, @Yicheng-Lu-llll, @ZacAttack, @justinvyu, @lk-chen, @alanwguo, @mcoder6425, @my-vegetable-has-exploded, @yancanmao, @arcyleung, @rjpower, @codope, @harshit-anyscale, @dayshah, @stephanie-wang, @KaisennHu, @ryanaoleary, @saihaj, @mattip, @rueian, @Kunchd, @pavitrabhalla, @owenowenisme, @Aydin-ab, @gvspraveen, @minerharry, @JackGammack, @jpatra72, @coqian, @zcin, @dstrodtman, @aslonnie, @ahao-anyscale, @GuyStone, @iamjustinhsu, @seanlaii, @ruisearch42, @akyang-anyscale, @ArturNiederfahrenhorst, @bveeramani, @OneSizeFitsQuorum, @xinyuangui2, @sb-hakunamatata, @22quinn, @Sparks0219, @sven1977, @snehachhabria, @dioptre, @nadongjun, @eric-higgins-ai, @marosset, @MatthewCWeston, @pcmoritz, @can-anyscale, @pimdh, @roshankathawate, @matthewdeng, @martinbomio, @GokuMohandas, @alimaazamat, @ali-corpo, @landscapepainter, @Qiaolin-Yu, @vaishdho1, @avigyabb, @srinathk10, @tannerdwood

Don't miss a new ray release

NewReleases is sending notifications on new releases.