Ray-2.54.0

Ray Data

🎉 New Features

• Add checkpointing support to Ray Data (#59409)
• Compute Expressions: list operations (#59346), fixed-size arrays (#58741), string padding (#59552), logarithmic (#59549), trigonometric (#59712), arithmetic (#59678), and rounding (#59295)
• Add sql_params support to read_sql (#60030)
• Add AsList aggregation (#59920)
• Support CountDistinct aggregate (#59030)
• Add credential provider abstraction for Databricks UC datasource (#60457)
• Support callable classes for UDFExpr (#56725)
• Add autoscaler metrics to Data Dashboard (#60472)
• Add optional filesystem parameter to download expression (#60677)
• Allow specifying partitioning style or flavor in write_parquet() (#59102)
• New cluster autoscaler enabled by default (#60474)

💫 Enhancements

• Improve numerical stability in scalers by handling near-zero values (#60488)
• Export dataset operator output schema to event logger (#60086)
• Iceberg: add retry policy for Storage + Catalog writes (#60620)
• Iceberg: remove calls to Catalog Table in write tasks (#60476)
• Expose logical operators and rules via package exports (#60297, #60296)
• Demote Sort from requiring preserve_order (#60555)
• Improve appearance of repr(dataset) (#59631)
• Allow configuring DefaultClusterAutoscalerV2 thresholds via env vars (#60133)
• Use Arrow IPC for Arrow Schema serialization/deserialization (#60195)
• Store _source_paths in object store to prevent excessive spilling during read task serialization (#59999)
• Add more shuffle fusion rules (#59985)
• Enable and tune DownstreamCapacityBackpressurePolicy (#59753)
• Enable concurrency cap backpressure with tuning (#59392)
• Set default actor pool scale up threshold to 1.75 (#59512)
• Don't downscale actors if the operator hasn't received any inputs (#59883)
• Don't reserve GPU budget for non-GPU tasks (#59789)
• Only return selected data columns in hive-partitioned Parquet files (#60236)
• Ordered + FIFO bundle queue (#60228)
• Add node_id, pid, attempt number for hanging tasks (#59793)
• Revise resource allocator task scheduling to factor in pending task outputs (#60639)
• Track block serialization time (#60574)
• Use metrics from OpRuntimeMetrics for progress (#60304)
• Tabular form for streaming executor op metrics (#59774)
• Info-log cluster scale-up decisions (#60357)
• Use plain mode instead of grid mode for OpMetrics logging (#59907)
• Progress reporting refactors (#59350, #59629, #59880)
• Remove deprecated TENSOR_COLUMN_NAME constant (#60573)
• Remove meta_provider parameter (#60379)
• Decouple Ray Train from Ray Data by removing top-level ray.data imports (#60292)
• Move extension types to ray.data (#59420)
• Skip upscaling validation warning for fixed-size actor pools (#60569)
• Make StatefulShuffleAggregation.finalize allow incremental streaming (#59972)
• Revisit OutputSplitter semantics to avoid unnecessary buffer accumulation (#60237)
• Update to PyArrow 23 (#60739, #59489)
• Add BackpressurePolicy to streaming executor progress bar (#59637)
• Support Arrow-based transformations for preprocessors (#59810)
• StandardScaler preprocessor with Arrow format (#59906)
• OneHotEncoder with Arrow format (#59890)

🔨 Fixes

• Fuse MapBatches even if they modify the row count (#60756)
• Don't push limit past map_batches by default (#60448)
• Fix wrong type hint of other dataset in zip and union (#60653)
• Fix ActorPoolMapOperator to guarantee dispatch of all given inputs (#60763)
• Fix ArrowInvalid error when backfilling missing fields from map tasks (#60643)
• Fix attribute error in UnionOperator.clear_internal_output_queue (#60538)
• Fix DefaultClusterAutoscalerV2 raising KeyError: 'CPU' (#60208)
• Fix ReorderingBundleQueue handling of empty output sequences (#60470)
• Fix task completion time without backpressure grafana panel metric name (#60481)
• Fix Union operator blocking when preserve_order is set (#59922)
• Fix autoscaler requesting empty resources instead of previous allocation when not scaling up (#60321)
• Fix autoscaler not respecting user-configured resource limits (#60283)
• Fix DefaultAutoscalerV2 not scaling nodes from zero (#59896)
• Fix Iceberg warning message (#60044)
• Fix Parquet datasource path column support (#60046)
• Fix ProgressBar with use_ray_tqdm (#59996)
• Fix stale stats on refit for preprocessors (#60031)
• Fix StreamingRepartition hang with empty upstream results (#59848)
• Fix operator fusion bug to preserve UDF modifying row count (#59513)
• Fix AutoscalingCoordinator double-allocating resources for multiple datasets (#59740)
• Fix DownstreamCapacityBackpressurePolicy issues (#59990)
• Fix AutoscalingCoordinator crash when requesting 0 GPUs on CPU-only cluster (#59514)
• Fix TensorArray to Arrow tensor conversion (#59449)
• Fix resource allocator not respecting max resource requirement (#59412)
• Fix GPU autoscaling when max_actors is set (#59632)
• Fix checkpoint filter PyArrow zero-copy conversion error (#59839)
• Restore class aliases to fix deserialization of existing datasets (#59828, #59818)
• Fix DataContext deserialization issue with StatsActor (#59471)

📖 Documentation

• Sort references in "Loading data and Saving data" pages (#60084)
• Fix inconsistent heading levels in "How to write tests" guide (#60706)
• Clarify resource_limits refers to logical resources (#60109)
• Update read_lance doc (#59673)
• Fix broken link in read_unity_catalog docstring (#59745)
• Fix bug in docs for enable_true_multi_threading (#60515)
• Add more education around transformations (#59415)

Ray Serve

🎉 New Features

• Queue-based autoscaling for TaskConsumer deployments (phase 1). Introduces a QueueMonitor actor that queries message brokers (Redis, RabbitMQ) for queue length, enabling TaskConsumer scaling based on pending tasks rather than HTTP load. (#59430)
• Default autoscaling parameters for custom policies. New apply_autoscaling_config decorator allows custom autoscaling policies to automatically benefit from Ray Serve's standard parameters (delays, scaling factors, bounds) without reimplementation. (#58857)
• label_selector and bundle_label_selector in Serve deployments. Deployments can now specify node label selectors for scheduling and bundle-level label selectors for placement groups, useful for targeting specific hardware (e.g., TPU topologies). (#57694)
• Deployment-level autoscaling observability. The controller now emits a structured JSON serve_autoscaling_snapshot log per autoscaling-enabled deployment each control-loop tick, with an event summarizer that reduces duplicate logs. (#56225)
• Batching with multiplexing support. Batching now guarantees each batch contains requests for the same multiplexed model, enabling correct multiplexed model serving with @serve.batch. (#59334)

💫 Enhancements

• Replica routing data structure optimizations. O(1) pending-request lookups, cached replica lists, lazy cleanup, optimized retry insertion, and metrics throttling yield significant routing performance improvements. (#60139)
• New operational metrics suite. Added long-poll metrics, replica lifecycle metrics, app/deployment status metrics, proxy health and request routing delay metrics, event loop utilization metrics, and controller health metrics — greatly improving monitoring and debugging capabilities. (#59246, #59235, #59244, #59238, #59535, #60473)
• Autoscaling config validation. lookback_period_s must now be greater than metrics_interval_s, preventing silent misconfigurations. (#59456)
• Cross-version root_path support for uvicorn. root_path now works correctly across all uvicorn versions, including >=0.26.0 which changed how root_path is processed. (#57555)
• Preserve user-set gRPC status codes. When deployments raise exceptions after setting a gRPC status code on the context, that code is now correctly propagated to the client instead of being overwritten with INTERNAL. Error messages are truncated to 4 KB to respect HTTP/2 trailer limits. (#60482)
• Replica ThreadPoolExecutor capped to num_cpus. The user-code event loop's default ThreadPoolExecutor is now limited to the deployment's num_cpus, preventing oversubscription when using asyncio.to_thread. (#60271)
• Generic actor registration API for shutdown cleanup. Deployments can register auxiliary actors (e.g., PrefixTreeActor) with the controller for automatic cleanup on serve.shutdown(), eliminating cross-library import dependencies. (#60067)
• Deployment config logging in controller. Deployment configurations are now logged in the controller for easier debugging and auditability. (#59222, #59501)
• Pydantic v1 deprecation warning. A FutureWarning is now emitted at ray.init() when Pydantic v1 is detected, as support will be removed in Ray 2.56. (#59703)

🔨 Fixes

• Fixed tracing signature mismatch across processes. Resolved TypeError: got an unexpected keyword argument _ray_trace_ctx when calling actors from a different process than the one that created them (e.g., serve start + dashboard interaction). (#59634)
• Fixed ingress deployment name collision. Ingress deployment name was incorrectly modified when a child deployment shared the same name, causing routing failures. (#59577)
• Fixed downstream deployment over-provisioning. Downstream deployments no longer over-provision replicas when receiving DeploymentResponse objects. (#60747)
• Fixed replicas hanging forever during draining. Replicas no longer hang indefinitely when requests are stuck during the draining phase. (#60788)
• Fixed TaskProcessorAdapter shutdown during rolling updates. Removed shutdown() from __del__, which was broadcasting a kill signal to all Celery workers instead of just the local one, breaking rolling updates. (#59713)
• Fixed Windows test failures. Resolved tracing file handle cleanup on Windows, skipped incompatible gRPC and tracing tests on Windows. (#60078, #60356, #60393, #59771)
• Fixed flaky tests. Addressed gauge throttling race in test_router_queue_len_metric, ensured proxy replica queue cache is populated before GCS failure tests, and added metrics server readiness checks. (#60333, #60466, #60468)
• Fixed distilbert test segfault. Worked around a pyarrow/jemalloc crash triggered by specific import ordering of FastAPI, torch, and TensorFlow. (#60478)

📖 Documentation

• Improved autoscaling documentation. Clarified the relationship between delays, metric push intervals, and the autoscaling control loop. (#59475)
• New example: video analysis inference. End-to-end notebook demonstrating a Serve application for scene change detection, - tagging, and video description. (#59859)
• New examples: model multiplexing and model composition. Published workload-based examples for forecasting with model multiplexing and recommendation systems with model composition. (#59166)
• Model registry integration guide. Added documentation for integrating Serve with model registries (e.g., MLflow). (#59080)
• Fixed broken documentation links. Resolved 404 errors for async inference, MLflow registry example, and LLM code examples. (#59917, #60071, #59520, #59521, #60181)
• Fixed monitoring docs. Corrected target replicas metric emission to enable time-series comparison with actual replicas. (#59571)
• Async inference template. Added an end-to-end template for building asynchronous inference applications with Ray Serve. (#58393, #59926)

🏗 Architecture refactoring

• Environment variable cleanup (5-part series). Removed deprecated and redundant env vars (RAY_SERVE_DEFAULT_HTTP_HOST, RAY_SERVE_DEFAULT_HTTP_PORT, RAY_SERVE_DEFAULT_GRPC_PORT, RAY_SERVE_HTTP_KEEP_ALIVE_TIMEOUT_S, RAY_SERVE_REQUEST_PROCESSING_TIMEOUT_S, RAY_SERVE_ENABLE_JSON_LOGGING, RAY_SERVE_ALWAYS_RUN_PROXY_ON_HEAD_NODE), cleaned up legacy constant fallbacks, and added documentation for previously undocumented env vars (e.g., RAY_SERVE_CONTROLLER_MAX_CONCURRENCY, RAY_SERVE_ROOT_URL, proxy health check settings, and fault tolerance params). Users relying on removed env vars should migrate to the Serve config API (http_options, grpc_options, LoggingConfig). (#59470, #59619, #59647, #59963, #60093)

Ray Train

🎉 New Features

• Add TPU multi-slice support to JaxTrainer (#58629)
• Update async validation API (#59428)
• Add a CallbackManager and guardrail some callback hooks (#60117)
• Add inter-execution file shuffling for deterministic multi-epoch training (#59528)
• Resume validations on driver restoration (#59270)

💫 Enhancements

• Pass ray remote args to validation task (#60203)
• Deprecate Predictor API (#60305)
• Increase worker group start default timeout to 60s (#60376)
• Unify PlacementGroup and SlicePlacementGroup interface in WorkerGroup (#60116)
• Cleanup zombie RayTrainWorker actors (#59872)
• Add usage telemetry for checkpointing and validation (#59490)
• Validate that validation is called with a checkpoint (#60548)
• Replace pg.ready() with pg.wait() in worker group (#60568)
• Rename DatasetsSetupCallback to DatasetsCallback (#59423)
• Update "Checkpoint Report Time" metric title to "Cumulative Checkpoint Report Time" (#58470)
• Add training failed error back to failure policy log (#59957)
• Decouple Ray Train from Ray Data by removing top-level imports (#60292)

🔨 Fixes

• Add try-except for pg.wait() (#60743)
• TrainController reraises AsyncioActorExit (#59461)

📖 Documentation

• Add a JaxTrainer template (#59842)
• Update Jax doc to include GPU and multi-slice TPU support (#60593)
• Document checkpoint_upload_fn backend and cuda:nccl backend support (#60541)
• Rename checkpoint_upload_func to checkpoint_upload_fn in docs (#60390)
• Fix Ray Train workloads and PyTorch with ASHA templates (#60537)
• Publish Ray Train workload example (#58936)

Ray Tune

🔨 Fixes

• Avoid file deletion race by using unique tmp file names (#60556)

Ray LLM

🎉 New Features

• Add /tokenize and /detokenize endpoints (#59787)
• Add /collective_rpc endpoint for RLHF weight synchronization (#59529)
• Add Control Plane API for Sleep/Wakeup (#59455)
• Add Pause/Resume Control Plane API (#59523)
• Add support for classification and scoring models (#59499)
• Add pooling parameter (#59534)
• Support vLLM structured outputs with backward-compat for guided_decoding (#59421)
• Add CPU support to Ray Serve LLM (#58334)
• Add should_continue_on_error support for ServeDeploymentStage (#59395)
• Support configuring HttpRequestUDF resources (#60313)

💫 Enhancements

• Upgrade vLLM to 0.15.0 (#60679)
• Unify schema of success and failure rows (#60572)
• Prefer uniproc executor over mp executor when world_size==1 (#60403)
• Use compute instead of concurrency to specify ActorPool size (#59645)
• Remove DataContext overrides in Ray Data LLM Processor (#60142)
• Use numpy arrays for embeddings to avoid torch.Tensor serialization overhead (#59919)
• Make PrefixCacheAwareRouter imbalance threshold less surprising (#59390)
• Allow tokenized_prompt without prompt in vLLMEngineStage (#59801)
• Avoid passing enums through fn_constructor_kwargs (#59806)
• Refactor Control Plane endpoints into mixins (#59502)
• Remove CUDA_VISIBLE_DEVICES deletion workaround (#60502)

🔨 Fixes

• Fix nested dict to Namespace conversion in vLLM engine initialization (#60380)
• Fix JSON non-serializable ndarray exception in http_request_stage (#60299)
• Exit actor on EngineDeadError to enable recovery (#60145)
• Fix NIXL port conflict in prefill-decode disaggregation test (#60057)

📖 Documentation

• Batch inference docs reorg and update to reflect per-stage config refactor (#59214)
• Add resiliency section and refine doc code (#60594)
• Add video/audio examples for vLLMEngineProcessor (#59446)
• Add SGLang integration example (#58366)
• Remove inaccurate statement in docs (#60425)

Ray RLlib

🎉 New Features

• Add TQC (Truncated Quantile Critics) algorithm implementation (#59808)
• Add LR scheduling ability to BC and MARWIL (#59067)
• RLlib and Ray Tune: Hyperparameter Optimisation example (#60182)

💫 Enhancements

• 🔥 APPO improvements: learner pipeline performance improvements (#59544)
• Improve stateful model training on offline data (#59345)
• Create resource bundle per learner (#59620)
• Improve env runner sampling by replacing recursive solution with iterative solution (#56082)
• Improve IMPALA examples and premerge (#59927)
• Remove MLAgents dependency (#59524)
• Upgrade to gymnasium v1.2.2 (#59530)
• Decrease log quantity for learning tests (#59005)
• Update learner state warnings to the debug level (#60178)
• Don't log np.nanmean warnings in EMA stats (#60408)

🔨 Fixes

• Fix DQN RLModule forward methods to handle dict spaces (#60451)
• Fix LearnerGroup.load_module_state() and mark as deprecated (#60354)
• Fix static dimension issue in ONNX export of Torch attention models (#60102)
• Fix Multi-Agent Episode concatenation for sequential environments (#59895)
• Fix module episode returns metrics accumulation for shared module IDs (#60234)
• Fix rollout fragment length calculation in AlgorithmConfig (#59438)
• Fix checkpointable issues with cloud storages (#60440)
• Update flatten_observations.py for nested spaces for ignored multi-agent (#59928)

Ray Core

🎉 New Features

• Resource Isolation: unify config construction, add public docs, and expose cgroup_path in ray.init() (#59372, #60183, #60726)
• Support tensor-level deduplication for NIXL (#60509)
• Add CUDA IPC transport for RDT (#59838)
• Register custom transport at runtime for RDT (#59255)
• Support TPU v7x accelerator type for device discovery (#60338)
• Introduce local port service discovery (#59613)
• Cancel sync actor by checking is_canceled() (#58914)
• Support labels for ray job submit --entrypoint-resource (#59735)
• Add --ip option in ray attach (#59931)
• Add bearer token support for remote URI downloads (#60050)
• Support HTTP redirection download (#59384)
• Add ray kill-actor --name/--namespace for force/graceful shutdown (#60258)

💫 Enhancements

• Bound object spilling file size to avoid disk increase pressure (#60098)
• Replace SHA-1 with SHA-256 for internal hash operations (#60242)
• Use whitelist approach to block mutation requests from browser (#60526)
• Pass authentication headers to WebSocket connections in tail_job_logs (#60346)
• Add auth to Dashboard HTTP agent and client (#59891)
• Use dedicated service account path for Ray auth tokens (#60409)
• Update Kubernetes token auth verb to ray:write (#60411)
• Replace RAY_AUTH_MODE=k8s with separate config for Kubernetes token auth (#59621)
• Optimize token auth: use shared_ptr caching and avoid per-RPC construction (#59500)
• Optimize OpenTelemetry metric recording calls (#59337)
• Throttle infeasible resource warning (#59790)
• Add default excludes for working_dir uploads (#59566)
• Tell users why objects cannot be reconstructed (#59625)
• Extend instance allocation timeout in autoscaler v2 (#60392)
• Remove GCS centralized scheduling (#59979, #60121, #60188)
• Demote stale sync message drop log to DEBUG in RaySyncer (#59616)
• Migrate remaining std::unordered_map to absl::flat_hash_map (#59921)
• Add missing fields to NodeDefinitionEvent proto (#60314)
• Add actor and task event missing fields (#60287)
• Add node id to the base event (#59242)
• Add repr_name to actor_lifecycle_event (#59925)
• Support ALL in exposable event config (#59878)
• Support publishing events from aggregator to GCS (#55781)
• Update the attempt number of actor creation task when actor restarts (#58877)
• Unify node feasibility and availability checking for GPU fractions (#59278)
• Update TPU utils for multi-slice compatibility (#59136)
• Improve SubprocessModuleHandle.destroy_module() resource cleanup (#60172)
• Support viewing PIDs for Dashboard and Runtime Env Agent (#58701)
• Optimize autoscaler monitor by moving resource demand parsing outside loop (#59190)
• Avoid GCS query for is_head in dashboard agent startup (#59378)
• Skip reporter and event aggregator client creation in minimal mode (#59846)
• Support out-of-order actors by extracting metadata when creating (RDT) (#59610)
• Synchronize CUDA stream before registering for NIXL (#60072)
• Atomically send/recv for two-sided ordering (RDT) (#60202)
• Add get_session_name() to RuntimeContext (#59469)
• Make MAX_APPLICATION_ERROR_LEN configurable via env var (#59543)
• Preserve function signatures through Ray decorators (#60479)

🔨 Fixes

• Fix idle_time_ms resetting for nodes not running tasks (#60581)
• Fix task event loss during shutdown (#60247)
• Filter bad subscriber messages from taking down GCS publisher (#60252)
• Fix RAY_EXPERIMENTAL_NOSET_* environment variable parsing in accelerator managers (#60577)
• Fix ray start --no-redirect-output crash (#60394)
• Fix drain state propagation race condition (#59536)
• Fix use-after-free race condition in OpenTelemetry gauge metric callback during shutdown (#60048)
• Fix PSUTIL_PROCESS_ATTRS returning empty list on Windows (#60173)
• Fix deadlock in garbage collection when holding lock (#60014)
• Fix incorrect error handling in autoscaler for available_node_types on on-prem clusters (#60184)
• Fix invalid status transitions in autoscaler v2 (#60412, #59550)
• Fix GCS crash from race condition in MetricsAgentClient exporter initialization (#59611)
• Fix tracing signature mismatch when calling actors from different processes (#59634)
• Fix crash when killing actor handle from previous session (#59425)
• Fix multiple deployment same name resolve (#59577)
• Handle dual task errors with read-only args (#59507)
• Handle exceptions raised by internal_ip() within StandardAutoscaler (#57279)
• Fix uv_runtime_env_hook.py to pin worker Python version (#59768)
• Fix STRICT_PACK placement groups ignoring bundle label selectors (#60170)
• Fix logging bug when log value is an empty string (#59434)
• Fix aggregator-to-GCS event conversion (#59783)
• Raise error on tail log job error in newer Ray versions (#59506)
• Fix num retries left message (#59829)
• Fix psutil internal API usage in dashboard disk usage reporting (#59659)
• Fix event exporter init ray check (#60073)
• Prevent use-after-free error in core worker shutdown (#58435)
• Fix task name inconsistency in RUNNING vs FINISHED metrics (#59893)
• Fix symmetric_run using wrong condition to check GCS readiness (#59794)
• Preserve Pydantic details when serialization fails (#59401)
• Retry GCP project metadata updates on HTTP 412 errors (#60429)
• Fix v1 autoscaler TypeError when using bundle_label_selectors (#59850)
• Shorten SHA-256 hex with base32 to comply with GCP label limits (#60722)

📖 Documentation

• Add initial user guide for Ray resource isolation with writable cgroups (#59051)
• Add token authentication internals documentation (#59299)
• Update metric exporter docs (#59874)
• Add internal documentation for Port Service Discovery (#59844)
• Update misleading Ray job diagram (#59940)
• Add debugging logs related to pinned argument size limit (#60175)
• Add slow startup tip to podman troubleshooting docs (#59942)
• Clarify ray.shutdown() behavior for local vs remote clusters (#59845)
• Improve placement group fault tolerance doc (#59830)
• Add head-node memory growth and OOM guidance (#58695)
• Add documentation for RAY_RUNTIME_ENV_BEARER_TOKEN env var (#60136)

Dashboard

💫 Enhancements

• Support more panels in dashboard (#60018)
• Add autoscaler metrics to Data Dashboard (#60472)
• Support viewing PIDs for Dashboard and Runtime Env Agent (#58701)

🔨 Fixes

• Update total for dark mode color (#60106)

Ray Wheels and Images

• Add experimental Python 3.13 images (#59811, #60549)
• Drop Python 3.9 support completely (#59824, #59825, #59769); wheel Python version now declared as 3.10+ (#59835)
• ray-cpp wheels are now py3-none, without specific Python versions. (#59969)

Documentation

• Add committership documentation (#60069)
• Update contribution guide with common labels (#59473)
• Add KubeRay & Volcano integration docs update (#59636)
• Add RayJob InTreeAutoscaling with Kueue docs after Kueue 0.16.0 release (#59648)
• Refactor LLM batch inference template (#59897)
• Add async inference template (#58393)
• Add RunLLM chat widget for Ray docs (#59126)
• Fix various typos and broken links (#60249, #59901, #60181)
• Replace Ray Tune + Train example with vanilla Ray Tune in homepage (#60229)
• Add Ray technical charter (#60068)

Thanks

Thank you to everyone who contributed to this release!
@KaisennHu, @MiXaiLL76, @slfan1989, @krisselberg, @JasonLi1909, @Priya-753, @pseudo-rnd-thoughts, @zzchun, @ZacAttack, @pushpavanthar, @jjyao, @ryanaoleary, @pcmoritz, @akshay-anyscale, @HassamSheikh, @yurekami, @Hyunoh-Yeo, @ruoliu2, @nrghosh, @wxwmd, @myandpr, @J-Meyers, @trilamsr, @kouroshHakha, @limarkdcunha, @manhld0206, @jreiml, @preneond, @yuchen-ecnu, @Yicheng-Lu-llll, @AchimGaedkeLynker, @vaishdho1, @israbbani, @OneSizeFitsQuorum, @Sathyanarayanaa-T, @nadongjun, @xinyuangui2, @Rob12312368, @as-jding, @lee1258561, @popojk, @coqian, @rajeshg007, @jeffreywang-anyscale, @kamil-kaczmarek, @alexeykudinkin, @Aydin-ab, @mgchoi239, @dragongu, @edoakes, @smortime, @tk42, @abrarsheikh, @jakubzimny, @Future-Outlier, @axreldable, @owenowenisme, @g199209, @cem-anyscale, @dayshah, @akelloway, @daiping8, @dlwh, @robertnishihara, @400Ping, @matthewdeng, @antoine-galataud, @cristianjd, @Partth101, @goutamvenkat-anyscale, @codope, @seanlaii, @andrew-anyscale, @andrewsykim, @liulehui, @simonsays1980, @Sparks0219, @yifanmai, @landscapepainter, @win5923, @kangwangamd, @srinarayan-srikanthan, @KeeProMise, @srinathk10, @my-vegetable-has-exploded, @MengjinYan, @yancanmao, @yuhuan130, @ArturNiederfahrenhorst, @akyang-anyscale, @rushikeshadhav, @kongjy, @harshit-anyscale, @justinvyu, @dancingactor, @Vito-Yang, @cr7258, @marwan116, @muyihao, @DeborahOlaboye, @bveeramani, @kriyanshii, @khluu, @machichima, @Kunchd, @jonded94, @iamjustinhsu, @sampan-s-nayak, @wingkitlee0, @sunsetxh, @dkhachyan, @can-anyscale, @TimothySeah, @raulchen, @elliot-barn, @ryankert01, @xyuzh, @stephanie-wang, @hao-aaron, @simeetnayan81, @cszhu, @richardliaw, @yuanjiewei, @kyuds, @eicherseiji, @RedGrey1993, @rueian, @jeffreyjeffreywang, @crypdick, @ankur-anyscale, @aslonnie