Ray 2.41.0

Highlights

• Major update of RLlib docs and example scripts for the new API stack.

Ray Libraries

Ray Data

🎉 New Features:

• Expression support for filters (#49016)
• Support partition_cols in write_parquet (#49411)
• Feature: implement multi-directional sort over Ray Data datasets (#49281)

💫 Enhancements:

• Use dask 2022.10.2 (#48898)
• Clarify schema validation error (#48882)
• Raise ValueError when the data sort key is None (#48969)
• Provide more messages when webdataset format is error (#48643)
• Upgrade Arrow version from 17 to 18 (#48448)
• Update hudi version to 0.2.0 (#48875)
• webdataset: expand JSON objects into individual samples (#48673)
• Support passing kwargs to map tasks. (#49208)
• Add ExecutionCallback interface (#49205)
• Add seed for read files (#49129)
• Make select_columns and rename_columns use Project operator (#49393)

🔨 Fixes:

• Fix partial function name parsing in map_groups (#48907)
• Always launch one task for read_sql (#48923)
• Reimplement of fix memory pandas (#48970)
• webdataset: flatten return args (#48674)
• Handle numpy > 2.0.0 behaviour in _create_possibly_ragged_ndarray (#48064)
• Fix DataContext sealing for multiple datasets. (#49096)
• Fix to_tf for List types (#49139)
• Fix type mismatch error while mapping nullable column (#49405)
• Datasink: support passing write results to on_write_completes (#49251)
• Fix groupby hang when value contains np.nan (#49420)
• Fix bug where file_extensions doesn't work with compound extensions (#49244)
• Fix map operator fusion when concurrency is set (#49573)

Ray Train

🎉 New Features:

• Output JSON structured log files for system and application logs (#49414)
• Add support for AMD ROCR_VISIBLE_DEVICES (#49346)

💫 Enhancements:

• Implement Train Tune API Revamp REP (#49376, #49467, #49317, #49522)

🏗 Architecture refactoring:

• LightGBM: Rewrite get_network_params implementation (#49019)

Ray Tune

🎉 New Features:

• Update optuna_search to allow users to configure optuna storage (#48547)

🏗 Architecture refactoring:

• Make changes to support Train Tune API Revamp REP (#49308, #49317, #49519)

Ray Serve

💫 Enhancements:

• Improved request_id generation to reduce proxy CPU overhead (#49537)
• Tune GC threshold by default in proxy (#49720)
• Use pickle.dumps for faster serialization from proxy to replica (#49539)

🔨 Fixes:

• Handle nested ‘=’ in serve run arguments (#49719)
• Fix bug when ray.init() is called multiple times with different runtime_envs (#49074)

🗑️ Deprecations:

• Adds a warning that the default behavior for sync methods will change in a future release. They will be run in a threadpool by default. You can opt into this behavior early by setting RAY_SERVE_RUN_SYNC_IN_THREADPOOL=1. (#48897)

RLlib

🎉 New Features:

• Add support for external Envs to new API stack: New example script and custom tcp-capable EnvRunner. (#49033)

💫 Enhancements:

• Offline RL:
  • Add sequence sampling to EpisodeReplayBuffer. (#48116)
  • Allow incomplete SampleBatch data and fully compressed observations. (#48699)
  • Add option to customize OfflineData. (#49015)
  • Enable offline training without specifying an environment. (#49041)
  • Various fixes: #48309, #49194, #49195
• APPO/IMPALA acceleration (new API stack):
  • Add support for AggregatorActors per Learner. (#49284)
  • Auto-sleep time AND thread-safety for MetricsLogger. (#48868)
  • Activate APPO cont. actions release- and CI tests (HalfCheetah-v1 and Pendulum-v1 new in tuned_examples). (#49068)
  • Add "burn-in" period setting to the training of stateful RLModules. (#49680)
• Callbacks API: Add support for individual lambda-style callbacks. (#49511)
• Other enhancements: #49687, #49714, #49693, #49497, #49800, #49098

📖 Documentation:

• New example scripts:
  • How to write a custom algorithm (VPG) from scratch. (#49536)
  • How to customize an offline data pipeline. (#49046)
  • GPUs on EnvRunners. (#49166)
  • Hierarchical training. (#49127)
  • Async gym vector env. (#49527)
  • Other fixes and enhancements: #48988, #49071
• New/rewritten html pages:
  • Rewrite checkpointing page. (#49504)
  • New scaling guide. (#49528)
  • New callbacks page. (#49513)
  • Rewrite RLModule page. (#49387)
  • New AlgorithmConfig page and redo package_ref page for algo configs. (#49464)
  • Rewrite offline RL page. (#48818)
  • Rewrite “key concepts" rst page. (#49398)
  • Rewrite RL environments pages. (#49165, #48542)
  • Fixes and enhancements: #49465, #49037, #49304, #49428, #49474, #49399, #49713, #49518

🔨 Fixes:

• Add on_episode_created callback to SingleAgentEnvRunner. (#49487)
• Fix train_batch_size_per_learner problems. (#49715)
• Various other fixes: #48540, #49363, #49418, #49191

🏗 Architecture refactoring:

• RLModule: Introduce Default[algo]RLModule classes (#49366, #49368)
• Remove RLlib dependencies from setup.py; add ormsgpack (#49489)

🗑️ Deprecations:

• #49488, #49144

Ray Core and Ray Clusters

Ray Core

💫 Enhancements:

• Add task_name, task_function_name and actor_name in Structured Logging (#48703)
• Support redis/valkey authentication with username (#48225)
• Add v6e TPU Head Resource Autoscaling Support (#48201)
• compiled graphs: Support all driver and actor read combinations (#48963)
• compiled graphs: Add ascii based CG visualization (#48315)
• compiled graphs: Add ray[cg] pip install option (#49220)
• Allow uv cache at installation (#49176)
• Support != Filter in GCS for Task State API (#48983)
• compiled graphs: Add CPU-based NCCL communicator for development (#48440)
• Support gcs and raylet log rotation (#48952)
• compiled graphs: Support nsight.nvtx profiling (#49392)

🔨 Fixes:

• autoscaler: Health check logs are not visible in the autoscaler container's stdout (#48905)
• Only publish WORKER_OBJECT_EVICTION when the object is out of scope or manually freed (#47990)
• autoscaler: Autoscaler doesn't scale up correctly when the KubeRay RayCluster is not in the goal state (#48909)
• autoscaler: Fix incorrectly terminating nodes misclassified as idle in autoscaler v1 (#48519)
• compiled graphs: Fix the missing dependencies when num_returns is used (#49118)
• autoscaler: Fuse scaling requests together to avoid overloading the Kubernetes API server (#49150)
• Fix bug to support S3 pre-signed url for .whl file (#48560)
• Fix data race on gRPC client context (#49475)
• Make sure draining node is not selected for scheduling (#49517)

Ray Clusters

💫 Enhancements:

• Azure: Enable accelerated networking as a flag in azure vms (#47988)

📖 Documentation:

• Kuberay: Logging: Add Fluent Bit DaemonSet and Grafana Loki to "Persist KubeRay Operator Logs" (#48725)
• Kuberay: Logging: Specify the Helm chart version in "Persist KubeRay Operator Logs" (#48937)

Dashboard

💫 Enhancements:

• Add instance variable to many default dashboard graphs (#49174)
• Display duration in milliseconds if under 1 second. (#49126)
• Add RAY_PROMETHEUS_HEADERS env for carrying additional headers to Prometheus (#49353)
• Document about the RAY_PROMETHEUS_HEADERS env for carrying additional headers to Prometheus (#49700)

🏗 Architecture refactoring:

• Move memray dependency from default to observability (#47763)
• Move StateHead's methods into free functions. (#49388)

Thanks

@raulchen, @alanwguo, @omatthew98, @xingyu-long, @tlinkin, @yantzu, @alexeykudinkin, @andrewsykim, @win5923, @csy1204, @dayshah, @richardliaw, @stephanie-wang, @gueraf, @rueian, @davidxia, @fscnick, @wingkitlee0, @KPostOffice, @GeneDer, @MengjinYan, @simonsays1980, @pcmoritz, @petern48, @kashiwachen, @pfldy2850, @zcin, @scottjlee, @Akhil-CM, @Jay-ju, @JoshKarpel, @edoakes, @ruisearch42, @gorloffslava, @jimmyxie-figma, @bthananjeyan, @sven1977, @bnorick, @jeffreyjeffreywang, @ravi-dalal, @matthewdeng, @angelinalg, @ivanthewebber, @rkooo567, @srinathk10, @maresb, @gvspraveen, @akyang-anyscale, @mimiliaogo, @bveeramani, @ryanaoleary, @kevin85421, @richardsliu, @hartikainen, @coltwood93, @mattip, @Superskyyy, @justinvyu, @hongpeng-guo, @ArturNiederfahrenhorst, @jecsand838, @Bye-legumes, @hcc429, @WeichenXu123, @martinbomio, @HollowMan6, @MortalHappiness, @dentiny, @zhe-thoughts, @anyadontfly, @smanolloff, @richo-anyscale, @khluu, @xushiyan, @rynewang, @japneet-anyscale, @jjyao, @sumanthratna, @saihaj, @aslonnie

Many thanks to all those who contributed to this release!