fix(greet): update dependency ray to v2.10.0

[renovate]

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
ray	2.9.3 -> 2.10.0

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Release Notes

ray-project/ray (ray)

v2.10.0

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Release Highlights

Ray 2.10 release brings important stability improvements and enhancements to Ray Data, with Ray Data becoming generally available (GA).

• [Data] Ray Data becomes generally available with stability improvements in streaming execution, reading and writing data, better tasks concurrency control, and debuggability improvement with dashboard, logging and metrics visualization.
• [RLlib] “New API Stack” officially announced as alpha for PPO and SAC.
• [Serve] Added a default autoscaling policy set via num_replicas=”auto” (#​42613).
• [Serve] Added support for active load shedding via max_queued_requests (#​42950).
• [Serve] Added replica queue length caching to the DeploymentHandle scheduler (#​42943).
  • This should improve overhead in the Serve proxy and handles.
  • max_ongoing_requests (max_concurrent_queries) is also now strictly enforced (#​42947).
  • If you see any issues, please report them on GitHub and you can disable this behavior by setting: RAY_SERVE_ENABLE_QUEUE_LENGTH_CACHE=0.
• [Serve] Renamed the following parameters. Each of the old names will be supported for another release before removal.
  • max_concurrent_queries -> max_ongoing_requests
  • target_num_ongoing_requests_per_replica -> target_ongoing_requests
  • downscale_smoothing_factor -> downscaling_factor
  • upscale_smoothing_factor -> upscaling_factor
• [Serve] WARNING: the following default values will change in Ray 2.11:
  • Default for max_ongoing_requests will change from 100 to 5.
  • Default for target_ongoing_requests will change from 1 to 2.
• [Core] Autoscaler v2 is in alpha and can be tried out with Kuberay. It has improved observability and stability compared to v1.
• [Train] Added support for accelerator types via ScalingConfig(accelerator_type).
• [Train] Revamped the XGBoostTrainer and LightGBMTrainer to no longer depend on xgboost_ray and lightgbm_ray. A new, more flexible API will be released in a future release.
• [Train/Tune] Refactored local staging directory to remove the need for local_dir and RAY_AIR_LOCAL_CACHE_DIR.

Ray Libraries

Ray Data

🎉 New Features:

• Streaming execution stability improvement to avoid memory issue, including per-operator resource reservation, streaming generator output buffer management, and better runtime resource estimation (#​43026, #​43171, #​43298, #​43299, #​42930, #​42504)
• Metadata read stability improvement to avoid AWS transient error, including retry on application-level exception, spread tasks across multiple nodes, and configure retry interval (#​42044, #​43216, #​42922, #​42759).
• Allow tasks concurrency control for read, map, and write APIs (#​42849, #​43113, #​43177, #​42637)
• Data dashboard and statistics improvement with more runtime metrics for each components (#​43790, #​43628, #​43241, #​43477, #​43110, #​43112)
• Allow to specify application-level error to retry for actor task (#​42492)
• Add num_rows_per_file parameter to file-based writes (#​42694)
• Add DataIterator.materialize (#​43210)
• Skip schema call in DataIterator.to_tf if tf.TypeSpec is provided (#​42917)
• Add option to append for Dataset.write_bigquery (#​42584)
• Deprecate legacy components and classes (#​43575, #​43178, #​43347, #​43349, #​43342, #​43341, #​42936, #​43144, #​43022, #​43023)

💫 Enhancements:

• Restructure stdout logging for better readability (#​43360)
• Add a more performant way to read large TFRecord datasets (#​42277)
• Modify ImageDatasource to use Image.BILINEAR as the default image resampling filter (#​43484)
• Reduce internal stack trace output by default (#​43251)
• Perform incremental writes to Parquet files (#​43563)
• Warn on excessive driver memory usage during shuffle ops (#​42574)
• Distributed reads for ray.data.from_huggingface (#​42599)
• Remove Stage class and related usages (#​42685)
• Improve stability of reading JSON files to avoid PyArrow errors (#​42558, #​42357)

🔨 Fixes:

• Turn off actor locality by default (#​44124)
• Normalize block types before internal multi-block operations (#​43764)
• Fix memory metrics for OutputSplitter (#​43740)
• Fix race condition issue in OpBufferQueue (#​43015)
• Fix early stop for multiple Limit operators. (#​42958)
• Fix deadlocks caused by Dataset.streaming_split for job hanging (#​42601)

📖 Documentation:

• Revamp Ray Data documentation for GA (#​44006, #​44007, #​44008, #​44098, #​44168, #​44093, #​44105)

Ray Train

🎉 New Features:

• Add support for accelerator types via ScalingConfig(accelerator_type) for improved worker scheduling (#​43090)

💫 Enhancements:

• Add a backend-specific context manager for train_func for setup/teardown logic (#​43209)
• Remove DEFAULT_NCCL_SOCKET_IFNAME to simplify network configuration (#​42808)
• Colocate Trainer with rank 0 Worker for to improve scheduling behavior (#​43115)

🔨 Fixes:

• Enable scheduling workers with memory resource requirements (#​42999)
• Make path behavior OS-agnostic by using Path.as_posix over os.path.join (#​42037)
• [Lightning] Fix resuming from checkpoint when using RayFSDPStrategy (#​43594)
• [Lightning] Fix deadlock in RayTrainReportCallback (#​42751)
• [Transformers] Fix checkpoint reporting behavior when get_latest_checkpoint returns None (#​42953)

📖 Documentation:

• Enhance docstring and user guides for train_loop_config (#​43691)
• Clarify in ray.train.report docstring that it is not a barrier (#​42422)
• Improve documentation for prepare_data_loader shuffle behavior and set_epoch (#​41807)

🏗 Architecture refactoring:

• Simplify XGBoost and LightGBM Trainer integrations. Implemented XGBoostTrainer and LightGBMTrainer as DataParallelTrainer. Removed dependency on xgboost_ray and lightgbm_ray. (#​42111, #​42767, #​43244, #​43424)
• Refactor local staging directory to remove the need for local_dir and RAY_AIR_LOCAL_CACHE_DIR. Add isolation between driver and distributed worker artifacts so that large files written by workers are not uploaded implicitly. Results are now only written to storage_path, rather than having another copy in the user’s home directory (~/ray_results). (#​43369, #​43403, #​43689)
• Split overloaded ray.train.torch.get_device into another get_devices API for multi-GPU worker setup (#​42314)
• Refactor restoration configuration to be centered around storage_path (#​42853, #​43179)
• Deprecations related to SyncConfig (#​42909)
• Remove deprecated preprocessor argument from Trainers (#​43146, #​43234)
• Hard-deprecate MosaicTrainer and remove SklearnTrainer (#​42814)

Ray Tune

💫 Enhancements:

• Increase the minimum number of allowed pending trials for faster auto-scaleup (#​43455)
• Add support to TBXLogger for logging images (#​37822)
• Improve validation of Experiment(config) to handle RLlib AlgorithmConfig (#​42816, #​42116)

🔨 Fixes:

• Fix reuse_actors error on actor cleanup for function trainables (#​42951)
• Make path behavior OS-agnostic by using Path.as_posix over os.path.join (#​42037)

📖 Documentation:

• Minor documentation fixes (#​42118, #​41982)

🏗 Architecture refactoring:

• Refactor local staging directory to remove the need for local_dir and RAY_AIR_LOCAL_CACHE_DIR. Add isolation between driver and distributed worker artifacts so that large files written by workers are not uploaded implicitly. Results are now only written to storage_path, rather than having another copy in the user’s home directory (~/ray_results). (#​43369, #​43403, #​43689)
• Deprecations related to SyncConfig and chdir_to_trial_dir (#​42909)
• Refactor restoration configuration to be centered around storage_path (#​42853, #​43179)
• Add back NevergradSearch (#​42305)
• Clean up invalid checkpoint_dir and reporter deprecation notices (#​42698)

Ray Serve

🎉 New Features:

• Added support for active load shedding via max_queued_requests (#​42950).
• Added a default autoscaling policy set via num_replicas=”auto” (#​42613).

🏗 API Changes:

• Renamed the following parameters. Each of the old names will be supported for another release before removal.
  • max_concurrent_queries to max_ongoing_requests
  • target_num_ongoing_requests_per_replica to target_ongoing_requests
  • downscale_smoothing_factor to downscaling_factor
  • upscale_smoothing_factor to upscaling_factor
• WARNING: the following default values will change in Ray 2.11:
  • Default for max_ongoing_requests will change from 100 to 5.
  • Default for target_ongoing_requests will change from 1 to 2.

💫 Enhancements:

• Add RAY_SERVE_LOG_ENCODING env to set the global logging behavior for Serve (#​42781).
• Config Serve's gRPC proxy to allow large payload (#​43114).
• Add blocking flag to serve.run() (#​43227).
• Add actor id and worker id to Serve structured logs (#​43725).
• Added replica queue length caching to the DeploymentHandle scheduler (#​42943).
  • This should improve overhead in the Serve proxy and handles.
  • max_ongoing_requests (max_concurrent_queries) is also now strictly enforced (#​42947).
  • If you see any issues, please report them on GitHub and you can disable this behavior by setting: RAY_SERVE_ENABLE_QUEUE_LENGTH_CACHE=0.
• Autoscaling metrics (tracking ongoing and queued metrics) are now collected at deployment handles by default instead of at the Serve replicas (#​42578).
  • This means you can now set max_ongoing_requests=1 for autoscaling deployments and still upscale properly, because requests queued at handles are properly taken into account for autoscaling.
  • You should expect deployments to upscale more aggressively during bursty traffic, because requests will likely queue up at handles during bursts of traffic.
  • If you see any issues, please report them on GitHub and you can switch back to the old method of collecting metrics by setting the environment variable RAY_SERVE_COLLECT_AUTOSCALING_METRICS_ON_HANDLE=0
• Improved the downscaling behavior of smoothing_factor for low numbers of replicas (#​42612).
• Various logging improvements (#​43707, #​43708, #​43629, #​43557).
• During in-place upgrades or when replicas become unhealthy, Serve will no longer wait for old replicas to gracefully terminate before starting new ones (#​43187). New replicas will be eagerly started to satisfy the target number of healthy replicas.
  • This new behavior is on by default and can be turned off by setting RAY_SERVE_EAGERLY_START_REPLACEMENT_REPLICAS=0

🔨 Fixes:

• Fix deployment route prefix override by default route prefix from serve run cli (#​43805).
• Fixed a bug causing batch methods to hang upon cancellation (#​42593).
• Unpinned FastAPI dependency version (#​42711).
• Delay proxy marking itself as healthy until it has routes from the controller (#​43076).
• Fixed an issue where multiplexed deployments could go into infinite backoff (#​43965).
• Silence noisy KeyError on disconnects (#​43713).
• Fixed the prometheus counter metrics emitted as gauge bug (#​43795, #​43901).
  • All the serve counter metrics are emitted as counters with _total suffix. The old gauge metrics are still emitted for compatibility.

📖 Documentation:

• Update serve logging config docs (#​43483).
• Added documentation for max_replicas_per_node (#​42743).

RLlib

🎉 New Features:

• The “new API stack” is now in alpha stage and available for PPO single- (#​42272) and multi-agent and for SAC single-agent (#​42571, #​42570, #​42568)
  • ConnectorV2 API (#​43669, #​43680, #​43040, #​41074, #​41212)
  • Episode APIs (SingleAgentEpisode and MultiAgentEpisode) (#​42009, #​43275, #​42296, #​43818, #​41631)
  • EnvRunner APIs (SingleAgentEnvRunner and MultiAgentEnvRunner) (#​41558, #​41825, #​42296, #​43779)
• In preparation of DQN on the new API stack: PrioritizedEpisodeReplayBuffer (#​43258, #​42832)

💫 Enhancements:

• Old API Stack cleanups:
  • Move SampleBatch column names (e.g. SampleBatch.OBS) into new class (Columns). (#​43665)
  • Remove old exec_plan API code. (#​41585)
  • Introduce OldAPIStack decorator (#​43657)
  • RLModule API: Add functionality to define kernel and bias initializers via config. (#​42137)
• Learner/LearnerGroup APIs:
  • Replace Learner/LearnerGroup specific config classes (e.g. LearnerHyperparameters) with AlgorithmConfig. (#​41296)
  • Learner/LearnerGroup: Allow updating from Episodes. (#​41235)
• In preparation of DQN on the new API stack: (#​43199, #​43196)

🔨 Fixes:

• New API Stack bug fixes: Fix policy_to_train logic (#​41529), fix multi-APU for PPO on the new API stack. (#​44001), Issue 40347: (#​42090)
• Other fixes: MultiAgentEnv would NOT call env.close() on a failed sub-env (#​43664), Issue 42152 (#​43317), issue 42396: (#​43316), issue 41518 (#​42011), issue 42385 (#​43313)

📖 Documentation:

• New API Stack examples: Self-play and league-based self-play (#​43276), MeanStdFilter (for both single-agent and multi-agent) (#​43274), Prev-actions/prev-rewards for multi-agent (#​43491)
• Other docs fixes and enhancements: (#​43438, #​41472, #​42117, #​43458)

Ray Core and Ray Clusters

Ray Core

🎉 New Features:

• Autoscaler v2 is in alpha and can be tried out with Kuberay.
• Introduced subreaper to prevent leaks of sub-processes created by user code. (#​42992)

💫 Enhancements:

• Ray state api get_task() now accepts ObjectRef (#​43507)
• Add an option to disable task tracing for task/actor (#​42431)
• Improved object transfer throughput. (#​43434)
• Ray client now compares the Ray and Python version for compatibility with the remote Ray cluster. (#​42760)

🔨 Fixes:

• Fixed several bugs for streaming generator (#​43775, #​43772, #​43413)
• Fixed Ray counter metrics emitted as gauge bug (#​43795)
• Fixed a bug where empty resource task doesn’t work with placement group (#​43448)
• Fixed a bug where CPU resource is not released for a blocked worker inside placement group (#​43270)
• Fixed GCS crashes when PG commit phase failed due to node failure (#​43405)
• Fixed a bug where Ray memory monitor prematurely kill tasks (#​43071)
• Fixed placement group resource leak (#​42942)
• Upgraded cloudpickle to 3.0 which fixes the incompatibility with dataclasses (#​42730)

📖 Documentation:

• Updated the doc for Ray accelerators support (#​41849)

Ray Clusters

💫 Enhancements:

• [spark] Add heap_memory param for setup_ray_cluster API, and change default value of per ray worker node config, and change default value of ray head node config for global Ray cluster (#​42604)
• [spark] Add global mode for ray on spark cluster (#​41153)

🔨 Fixes:

• [VSphere] Only deploy ovf to first host of cluster (#​42258)

Thanks

Many thanks to all those who contributed to this release!

@​ronyw7, @​xsqian, @​justinvyu, @​matthewdeng, @​sven1977, @​thomasdesr, @​veryhannibal, @​klebster2, @​can-anyscale, @​simran-2797, @​stephanie-wang, @​simonsays1980, @​kouroshHakha, @​Zandew, @​akshay-anyscale, @​matschaffer-roblox, @​WeichenXu123, @​matthew29tang, @​vitsai, @​Hank0626, @​anmyachev, @​kira-lin, @​ericl, @​zcin, @​sihanwang41, @​peytondmurray, @​raulchen, @​aslonnie, @​ruisearch42, @​vszal, @​pcmoritz, @​rickyyx, @​chrislevn, @​brycehuang30, @​alexeykudinkin, @​vonsago, @​shrekris-anyscale, @​andrewsykim, @​c21, @​mattip, @​hongchaodeng, @​dabauxi, @​fishbone, @​scottjlee, @​justina777, @​surenyufuz, @​robertnishihara, @​nikitavemuri, @​Yard1, @​huchen2021, @​shomilj, @​architkulkarni, @​liuxsh9, @​Jocn2020, @​liuyang-my, @​rkooo567, @​alanwguo, @​KPostOffice, @​woshiyyya, @​n30111, @​edoakes, @​y-abe, @​martinbomio, @​jiwq, @​arunppsg, @​ArturNiederfahrenhorst, @​kevin85421, @​khluu, @​JingChen23, @​masariello, @​angelinalg, @​jjyao, @​omatthew98, @​jonathan-anyscale, @​sjoshi6, @​gaborgsomogyi, @​rynewang, @​ratnopamc, @​chris-ray-zhang, @​ijrsvt, @​scottsun94, @​raychen911, @​franklsf95, @​GeneDer, @​madhuri-rai07, @​scv119, @​bveeramani, @​anyscalesam, @​zen-xu, @​npuichigo

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