# Ray 2.47.0 — Ray-2.47.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2025-06-12
- Version: 2.47.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.47.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.47.0

What's New is an index, not a publisher: every entry below links to the vendor's own release notes, which are the authoritative source. Entries are labelled where they are hand-curated sample data, pre-releases, or drawn from a secondary source such as a developer blog.

Reuse: the summaries, labels and curation here are © What's New. Quote freely with attribution and a link back; wholesale republication of the corpus is not permitted — terms: https://whatsnew.fyi/terms. The vendors' own release notes remain their publishers'.

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- **added** — Support prefill disaggregation in Ray Serve LLM for production LLM serving use cases
- **added** — Add save modes support to file data sinks
- **added** — Add flattening capability to the Concatenator preprocessor to support output vectorization use cases
- **added** — Support for configuring Ray Train worker actor runtime environments
- **added** — Include Grafana panel data in Ray Train export for improved monitoring
- **added** — Introduce a structured logging environment variable to standardize log formats
- **added** — Add metrics for TrainControllerState to enhance observability
- **added** — Add support for implementing custom request routing algorithms in Ray Serve
- **added** — Introduce an environment variable to prioritize custom resources during deployment scheduling
- **added** — Expose vLLM Metrics to serve.llm API
- **added** — Add Embedding API to Ray Serve/Data LLM
- **added** — Add GPU inference to offline evaluation in RLlib
- **changed** — Re-enable Actor locality-based scheduling with improved algorithms for ranking bundle locations
- **changed** — Disable blocking pipeline by default until Actor Pool fully scales up to min actors
- **changed** — Improve progress bar and dashboard to show name of partial functions properly
- **changed** — The ingress API now accepts a builder function in addition to an ASGI app object
- **changed** — Allow setting name_prefix in build_llm_deployment
- **changed** — Mitigate the serve.llm streaming overhead by properly batching stream chunks
- **fixed** — Make Ray Data from_torch respect Dataset len
- **fixed** — Fix race condition bug in fault tolerance by disabling on_exit hook
- **fixed** — Fix move_tensors_to_device utility for the list/tuple[tensor] case
- **fixed** — Fix ActorPool scaling to avoid scaling down when the input queue is empty
- **fixed** — Fix internal queues accounting for all Operators with an internal queue
- **fixed** — Fix backpressure for FileBasedDatasource to prevent potential OOMs for workloads using FileBasedDatasources
- **fixed** — Fixed GPU transfer support for non-contiguous tensors
- **fixed** — Fixed runtime_env validation for py_modules
- **fixed** — Disallow special characters in Serve deployment and application names
- **fixed** — Add a descriptive error message when a deployment name is not found
- **fixed** — LLMRouter.check_health() should check LLMServer.check_health()
- **fixed** — Fix runtime passthrough and auto-executor class selection
- **fixed** — Update check_health return type
- **fixed** — Bug fix for duplication of <bos> token
- **fixed** — In stream batching, first part of the stream was always consumed and not streamed back from the router
- **fixed** — Fixed weight synching in offline evaluation
- **fixed** — Fixed bug in split_and_zero_pad utility function related to complex structures vs simple values or np.arrays

#### Release Highlights
* Prefill disaggregation is now supported in initial support in Ray Serve LLM (#53092). This is critical for production LLM serving use cases.
* Ray Data features a variety of performance improvements (locality-based scheduling, non-blocking execution) as well as improvements to observability, preprocessors, and other stability fixes.
* Ray Serve now features custom request routing algorithms, which is critical for high throughput traffic for large model use cases.


#### Ray Libraries

##### Ray Data

🎉 New Features:
- Add save modes support to file data sinks (#52900)
- Added flattening capability to the Concatenator preprocessor to support output vectorization use cases (#53378)


💫 Enhancements:
- Re-enable Actor locality-based scheduling. This PR also improves algorithms for ranking the locations for the bundle. (#52861)
- Disable blocking pipeline by default until Actor Pool fully scales up to min actors (#52754)
- Progress bar and dashboard improvements to show name of partial functions properly(#52280)

🔨 Fixes:
- Make Ray Data `from_torch` respect Dataset len (#52804)
- Fixing flaky aggregation test (#53383)
- Fix race condition bug in fault tolerance by disabling `on_exit` hook (#53249)
- Fix `move_tensors_to_device` utility for the list/tuple[tensor] case (#53109)
- Fix `ActorPool` scaling to avoid scaling down when the input queue is empty (#53009)
- Fix internal queues accounting for all Operators w/ an internal queue (#52806)
- Fix backpressure for `FileBasedDatasource`. This fixes potential OOMs for workloads using `FileBasedDatasources` (#52852)

📖 Documentation:
- Fix working code snippets (#52748)
- Improve AggregateFnV2 docstrings and examples (#52911)
- Improved documentation for vectorizers and API visibility in Data (#52456)


##### Ray Train

🎉 New Features:
- Added support for configuring Ray Train worker actor runtime environments. (#52421)
- Included Grafana panel data in Ray Train export for improved monitoring. (#53072)
- Introduced a structured logging environment variable to standardize log formats. (#52952)
- Added metrics for `TrainControllerState` to enhance observability. (#52805)

💫 Enhancements:
- Logging of controller state transitions to aid in debugging and analysis. (#53344)
- Improved handling of `Noop` scaling decisions for smoother scaling logic. (#53180)

🔨 Fixes:
- Improved `move_tensors_to_device utility` to correctly handle `list` / `tuple` of tensors. (#53109)
- Fixed GPU transfer support for non-contiguous tensors. (#52548)
- Increased timeout in `test_torch_device_manager` to reduce flakiness. (#52917)

📖 Documentation:
- Added a note about PyTorch DataLoader’s multiprocessing and forkserver usage. (#52924)
- Fixed various docstring format and indentation issues. (#52855, #52878)
- Removed unused "configuration-overview" documentation page. (#52912)
- General typo corrections. (#53048)

🏗 Architecture refactoring:
- Deduplicated ML doctest runners in CI for efficiency. (#53157)
- Converted isort configuration to Ruff for consistency. (#52869)
- Removed unused `PARALLEL_CI` blocks and combined imports. (#53087, #52742)

##### Ray Tune

💫 Enhancements:
- Updated `test_train_v2_integration` to use the correct `RunConfig`. (#52882)

📖 Documentation:
- Replaced `session.report` with `tune.report` and corrected import paths. (#52801)
- Removed outdated graphics cards reference in docs. (#52922)
- Fixed various docstring format issues. (#52879)

##### Ray Serve

🎉 New Features:
- Added support for implementing custom request routing algorithms. (#53251)
- Introduced an environment variable to prioritize custom resources during deployment scheduling. (#51978)

💫 Enhancements:
- The ingress API now accepts a builder function in addition to an ASGI app object. (#52892)

🔨 Fixes:
- Fixed `runtime_env` validation for `py_modules`. (#53186)
- Disallowed special character

_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-2.47.0]_