Ray 2.32.0

Highlight: aDAG Developer Preview

This is a new Ray Core specific feature called Ray accelerated DAGs (aDAGs).

• aDAGs give you a Ray Core-like API but with extensibility to pre-compile execution paths across pre-allocated resources on a Ray Cluster to possible benefits for optimization on throughput and latency. Some practical examples include:
  • Up to 10x lower task execution time on single-node.
  • Native support for GPU-GPU communication, via NCCL.
• This is still very early, but please reach out on #ray-core on Ray Slack to learn more!

Ray Libraries

Ray Data

💫 Enhancements:

• Support async callable classes in map_batches() (#46129)

🔨 Fixes:

• Ensure InputDataBuffer doesn't free block references (#46191)
• MapOperator.num_active_tasks should exclude pending actors (#46364)
• Fix progress bars being displayed as partially completed in Jupyter notebooks (#46289)

📖 Documentation:

• Fix docs: read_api.py docstring (#45690)
• Correct API annotation for tfrecords_datasource (#46171)
• Fix broken links in README and in ray.data.Dataset (#45345)

Ray Train

📖 Documentation:

• Update PyTorch Data Ingestion User Guide (#45421)

Ray Serve

💫 Enhancements:

• Optimize ServeController.get_app_config() (#45878)
• Change default for max and target ongoing requests (#45943)
• Integrate with Ray structured logging (#46215)
• Allow configuring handle cache size and controller max concurrency (#46278)
• Optimize DeploymentDetails.deployment_route_prefix_not_set() (#46305)

RLlib

🎉 New Features:

• APPO on new API stack (w/ EnvRunners). (#46216)

💫 Enhancements:

• Stability: APPO, SAC, and DQN activate multi-agent learning tests (#45542, #46299)
• Make Tune trial ID available in EnvRunners (and callbacks). (#46294)
• Add env- and agent_steps to custom evaluation function. (#45652)
• Remove default-metrics from Algorithm (tune does NOT error anymore if any stop-metric is missing). (#46200)

🔨 Fixes:

• Various bug fixes: #45542

📖 Documentation:

• Example for new API stack: Offline RL (BC) training on single-agent, while evaluating w/ multi-agent setup. (#46251)
• Example for new API stack: Custom RLModule with an LSTM. (#46276)

Ray Core

🎉 New Features:

• aDAG Developer Preview.

💫 Enhancements:

• Allow env setup logger encoding (#46242)
• ray list tasks filter state and name on GCS side (#46270)
• Log ray version and ray commit during GCS start (#46341)

🔨 Fixes:

• Decrement lineage ref count of an actor when the actor task return object reference is deleted (#46230)
• Fix negative ALIVE actors metric and introduce IDLE state (#45718)
• psutil process attr num_fds is not available on Windows (#46329)

Dashboard

🎉 New Features:

• Added customizable refresh frequency for metrics on Ray Dashboard (#44037)

💫 Enhancements:

• Upgraded to MUIv5 and React 18 (#45789)

🔨 Fixes:

• Fix for multi-line log items breaking log viewer rendering (#46391)
• Fix for UI inconsistency when a job submission creates more than one Ray job. (#46267)
• Fix filtering by job id for tasks API not filtering correctly. (#45017)

Docs

🔨 Fixes:

• Re-enabled automatic cross-reference link checking for Ray documentation, with Sphinx nitpicky mode (#46279)
• Enforced naming conventions for public and private APIs to maintain accuracy, starting with Ray Data API documentation (#46261)

📖 Documentation:

• Upgrade Python 3.12 support to alpha, marking the release of the Ray wheel to PyPI and conducting a sanity check of the most critical tests.

Thanks

Many thanks to all those who contributed to this release!

@stephanie-wang, @MortalHappiness, @aslonnie, @ryanaoleary, @jjyao, @jackhumphries, @nikitavemuri, @woshiyyya, @JoshKarpel, @ruisearch42, @sven1977, @alanwguo, @GeneDer, @saihaj, @raulchen, @liuxsh9, @khluu, @cristianjd, @scottjlee, @bveeramani, @zcin, @simonsays1980, @SumanthRH, @davidxia, @can-anyscale, @peytondmurray, @kevin85421