Release Highlights

• Ray AI Runtime (AIR)
  • Better support for Image-based workloads.
    • Ray Datasets read_images() API for loading data.
    • Numpy-based API for user-defined functions in Preprocessor.
  • Ability to read TFRecord input.
    • Ray Datasets read_tfrecords() API to read TFRecord files.
• Ray Serve:
  • Add support for gRPC endpoint (alpha release). Instead of using an HTTP server, Ray Serve supports gRPC protocol and users can bring their own schema for their use case.
• RLlib:
  • Introduce decision transformer (DT) algorithm.
  • New hook for callbacks with on_episode_created().
  • Learning rate schedule to SimpleQ and PG.
• Ray Core:
  • Ray OOM prevention (alpha release).
  • Support dynamic generators as task return values.
• Dashboard:
  • Time series metrics support.
  • Export configuration files can be used in Prometheus or Grafana instances.
  • New progress bar in job detail view.

Ray Libraries

Ray AIR

💫Enhancements:

• Improve readability of training failure output (#27946, #28333, #29143)
• Auto-enable GPU for Predictors (#26549)
• Add ability to create TorchCheckpoint from state dict (#27970)
• Add ability to create TensorflowCheckpoint from saved model/h5 format (#28474)
• Add attribute to retrieve URI from Checkpoint (#28731)
• Add all allowable types to WandB Callback (#28888)

🔨 Fixes:

• Handle nested metrics properly as scoring attribute (#27715)
• Fix serializability of Checkpoints (#28387, #28895, #28935)

📖Documentation:

• Miscellaneous updates to documentation and examples (#28067, #28002, #28189, #28306, #28361, #28364, #28631, #28800)

🏗 Architecture refactoring:

• Deprecate Checkpoint.to_object_ref and Checkpoint.from_object_ref (#28318)
• Deprecate legacy train/tune functions in favor of Session (#28856)

Ray Data Processing

🎉 New Features:

• Add read_images (#29177)
• Add read_tfrecords (#28430)
• Add NumPy batch format to Preprocessor and BatchMapper (#28418)
• Ragged tensor extension type (#27625)
• Add KBinsDiscretizer Preprocessor (#28389)

💫Enhancements:

• Simplify to_tf interface (#29028)
• Add metadata override and inference in Dataset.to_dask() (#28625)
• Prune unused columns before aggregate (#28556)
• Add Dataset.default_batch_format (#28434)
• Add partitioning parameter to read_ functions (#28413)
• Deprecate "native" batch format in favor of "default" (#28489)
• Support None partition field name (#28417)
• Re-enable Parquet sampling and add progress bar (#28021)
• Cap the number of stats kept in StatsActor and purge in FIFO order if the limit exceeded (#27964)
• Customized serializer for Arrow JSON ParseOptions in read_json (#27911)
• Optimize groupby/mapgroups performance (#27805)
• Improve size estimation of image folder data source (#27219)
• Use detached lifetime for stats actor (#25271)
• Pin _StatsActor to the driver node (#27765)
• Better error message for partition filtering if no file found (#27353)
• Make Concatenator deterministic (#27575)
• Change FeatureHasher input schema to expect token counts (#27523)
• Avoid unnecessary reads when truncating a dataset with ds.limit() (#27343)
• Hide tensor extension from UDFs (#27019)
• Add repr to AIR classes (#27006)

🔨 Fixes:

• Add upper bound to pyarrow version check (#29674) (#29744)
• Fix map_groups to work with different output type (#29184)
• read_csv not filter out files by default (#29032)
• Check columns when adding rows to TableBlockBuilder (#29020)
• Fix the peak memory usage calculation (#28419)
• Change sampling to use same API as read Parquet (#28258)
• Fix column assignment in Concatenator for Pandas 1.2. (#27531)
• Doing partition filtering in reader constructor (#27156)
• Fix split ownership (#27149)

📖Documentation:

• Clarify dataset transformation. (#28482)
• Update map_batches documentation (#28435)
• Improve docstring and doctest for read_parquet (#28488)
• Activate dataset doctests (#28395)
• Document using a different separator for read_csv (#27850)
• Convert custom datetime column when reading a CSV file (#27854)
• Improve preprocessor documentation (#27215)
• Improve limit() and take() docstrings (#27367)
• Reorganize the tensor data support docs (#26952)
• Fix nyc_taxi_basic_processing notebook (#26983)

Ray Train

🎉 New Features:

• Add FullyShardedDataParallel support to TorchTrainer (#28096)

💫Enhancements:

• Add rich notebook repr for DataParallelTrainer (#26335)
• Fast fail if training loop raises an error on any worker (#28314)
• Use torch.encode_data with HorovodTrainer when torch is imported (#28440)
• Automatically set NCCL_SOCKET_IFNAME to use ethernet (#28633)
• Don't add Trainer resources when running on Colab (#28822)
• Support large checkpoints and other arguments (#28826)

🔨 Fixes:

• Fix and improve HuggingFaceTrainer (#27875, #28154, #28170, #28308, #28052)
• Maintain dtype info in LightGBMPredictor (#28673)
• Fix prepare_model (#29104)
• Fix train.torch.get_device() (#28659)

📖Documentation:

• Clarify LGBM/XGB Trainer documentation (#28122)
• Improve Hugging Face notebook example (#28121)
• Update Train API reference and docs (#28192)
• Mention FSDP in HuggingFaceTrainer docs (#28217)

🏗 Architecture refactoring:

• Improve Trainer modularity for extensibility (#28650)

Ray Tune

🎉 New Features:

• Add Tuner.get_results() to retrieve results after restore (#29083)

💫Enhancements:

• Exclude files in sync_dir_between_nodes, exclude temporary checkpoints (#27174)
• Add rich notebook output for Tune progress updates (#26263)
• Add logdir to W&B run config (#28454)
• Improve readability for long column names in table output (#28764)
• Add functionality to recover from latest available checkpoint (#29099)
• Add retry logic for restoring trials (#29086)

🔨 Fixes:

• Re-enable progress metric detection (#28130)
• Add timeout to retry_fn to catch hanging syncs (#28155)
• Correct PB2’s beta_t parameter implementation (#28342)
• Ignore directory exists errors to tackle race conditions (#28401)
• Correctly overwrite files on restore (#28404)
• Disable pytorch-lightning multiprocessing per default (#28335)
• Raise error if scheduling an empty PlacementGroupFactory#28445
• Fix trial cleanup after x seconds, set default to 600 (#28449)
• Fix trial checkpoint syncing after recovery from other node (#28470)
• Catch empty hyperopt search space, raise better Tuner error message (#28503)
• Fix and optimize sample search algorithm quantization logic (#28187)
• Support tune.with_resources for class methods (#28596)
• Maintain consistent Trial/TrialRunner state when pausing and resuming trial with PBT (#28511)
• Raise better error for incompatible gcsfs version (#28772)
• Ensure that exploited in-memory checkpoint is used by trial with PBT (#28509)
• Fix Tune checkpoint tracking for minimizing metrics (#29145)

📖Documentation:

• Miscelleanous documentation fixes (#27117, #28131, #28210, #28400, #28068, #28809)
• Add documentation around trial/experiment checkpoint (#28303)
• Add basic parallel execution guide for Tune (#28677)
• Add example PBT notebook (#28519)

🏗 Architecture refactoring:

• Store SyncConfig and CheckpointConfig in Experiment and Trial (#29019)

Ray Serve

🎉 New Features:

• Added gRPC direct ingress support [alpha version] (#28175)
• Serve cli can provide kubernetes formatted output (#28918)
• Serve cli can provide user config output without default value (#28313)

💫Enhancements:

• Enrich more benchmarks
• image objection with resnet50 mode with image preprocessing (#29096)
• gRPC vs HTTP inference performance (#28175)
• Add health check metrics to reflect the replica health status (#29154)

🔨 Fixes:

• Fix memory leak issues during inference (#29187)
• Fix unexpected http options omit warning when using serve cli to start the ray serve (#28257)
• Fix unexpected long poll exceptions (#28612)

📖Documentation:

• Add e2e fault tolerance instructions (#28721)
• Add Direct Ingress instructions (#29149)
• Bunch of doc improvements on “dev workflow”, “custom resources”, “serve cli” etc (#29147, #28708, #28529, #28527)

RLlib

🎉 New Features:

• Decision Transformer (DT) Algorithm added (#27890, #27889, #27872, #27829).
• Callbacks now have a new hook on_episode_created(). (#28600)
• Added learning rate schedule to SimpleQ and PG. (#28381)

💫Enhancements:

• Soft target network update is now supported by all off-policy algorithms (e.g DQN, DDPG, etc.) (#28135)
• Stop RLlib from "silently" selecting atari preprocessors. (#29011)
• Improved offline RL and off-policy evaluation performance (#28837, #28834, #28593, #28420, #28136, #28013, #27356, #27161, #27451).
• Escalated old deprecation warnings to errors (#28807, #28795, #28733, #28697).
• Others: #27619, #27087.

🔨 Fixes:

• Various bug fixes: #29077, #28811, #28637, #27785, #28703, #28422, #28405, #28358, #27540, #28325, #28357, #28334, #27090, #28133, #27981, #27980, #26666, #27390, #27791, #27741, #27424, #27544, #27459, #27572, #27255, #27304, #26629, #28166, #27864, #28938, #28845, #28588, #28202, #28201, #27806

📖Documentation:

• Connectors. (#27528)
• Training step API. (#27344)
• Others: #28299, #27460

Ray Workflows

🔨 Fixes:

• Fixed the object loss due to driver exit (#29092)
• Change the name in step to task_id (#28151)

Ray Core and Ray Clusters

Ray Core

🎉 New Features:

• Ray OOM prevention feature alpha release! If your Ray jobs suffer from OOM issues, please give it a try.
• Support dynamic generators as task return values. (#29082 #28864 #28291)

💫Enhancements:

• Fix spread scheduling imbalance issues (#28804 #28551 #28551)
• Widening range of grpcio versions allowed (#28623)
• Support encrypted redis connection. (#29109)
• Upgrade redis from 6.x to 7.0.5. (#28936)
• Batch ScheduleAndDispatchTasks calls (#28740)

🔨 Fixes:

• More robust spilled object deletion (#29014)
• Fix the initialization/destruction order between reference_counter_ and node change subscription (#29108)
• Suppress the logging error when python exits and actor not deleted (#27300)
• Mark run_function_on_all_workers as deprecated until we get rid of this (#29062)
• Remove unused args for default_worker.py (#28177)
• Don't include script directory in sys.path if it's started via python -m (#28140)
• Handling edge cases of max_cpu_fraction argument (#27035)
• Fix out-of-band deserialization of actor handle (#27700)
• Allow reuse of cluster address if Ray is not running (#27666)
• Fix a uncaught exception upon deallocation for actors (#27637)
• Support placement_group=None in PlacementGroupSchedulingStrategy (#27370)

📖Documentation:

• Ray 2.0 white paper is published.
• Revamp ray core docs (#29124 #29046 #28953 #28840 #28784 #28644 #28345 #28113 #27323 #27303)
• Fix cluster docs (#28056 #27062)
• CLI Reference Documentation Revamp (#27862)

Ray Clusters

💫Enhancements:

• Distinguish Kubernetes deployment stacks (#28490)

📖Documentation:

• State intent to remove legacy Ray Operator (#29178)
• Improve KubeRay migration notes (#28672)
• Add FAQ for cluster multi-tenancy support (#29279)

Dashboard

🎉 New Features:

• Time series metrics are now built into the dashboard
• Ray now exports some default configuration files which can be used for your Prometheus or Grafana instances. This includes default metrics which show common information important to your Ray application.
• New progress bar is shown in the job detail view. You can see how far along your ray job is.

🔨 Fixes:

• Fix to prometheus exporter producing a slightly incorrect format.
• Fix several performance issues and memory leaks

📖Documentation:

• Added additional documentation on the new time series and the metrics page

Many thanks to all those who contributed to this release!

@sihanwang41, @simon-mo, @avnishn, @MyeongKim, @markrogersjr, @christy, @xwjiang2010, @kouroshHakha, @zoltan-fedor, @wumuzi520, @alanwguo, @Yard1, @liuyang-my, @charlesjsun, @DevJake, @matteobettini, @jonathan-conder-sm, @mgerstgrasser, @guidj, @JiahaoYao, @Zyiqin-Miranda, @jvanheugten, @aallahyar, @SongGuyang, @clarng, @architkulkarni, @Rohan138, @heyitsmui, @mattip, @ArturNiederfahrenhorst, @maxpumperla, @vale981, @krfricke, @DmitriGekhtman, @amogkam, @richardliaw, @maldil, @zcin, @jianoaix, @cool-RR, @kira-lin, @gramhagen, @c21, @jiaodong, @sijieamoy, @tupui, @ericl, @anabranch, @se4ml, @suquark, @dmatrix, @jjyao, @clarkzinzow, @smorad, @rkooo567, @jovany-wang, @edoakes, @XiaodongLv, @klieret, @rozsasarpi, @scottsun94, @ijrsvt, @bveeramani, @chengscott, @jbedorf, @kevin85421, @nikitavemuri, @sven1977, @acxz, @stephanie-wang, @PaulFenton, @WangTaoTheTonic, @cadedaniel, @nthai, @wuisawesome, @rickyyx, @artemisart, @peytondmurray, @pingsutw, @olipinski, @davidxia, @stestagg, @yaxife, @scv119, @mwtian, @yuanchi2807, @ntlm1686, @shrekris-anyscale, @cassidylaidlaw, @gjoliver, @ckw017, @hakeemta, @ilee300a, @avivhaber, @matthewdeng, @afarid, @pcmoritz, @Chong-Li, @Catch-Bull, @justinvyu, @iycheng