# Ray 2.51.0 — Ray-2.51.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2025-10-29
- Version: 2.51.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.51.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.51.0

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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- **changed** — Ray Train v2 is now enabled by default, providing usability and stability improvements and new features
- **added** — Application-level autoscaling in Ray Serve with custom autoscaling policies that operate across all deployments in an application
- **added** — Enhanced autoscaling context with replica-level metrics including total_running_requests, total_queued_requests, and total_num_requests
- **added** — Autoscaling metrics aggregation functions supporting min, max, and time-weighted average aggregation
- **added** — Support for multiple task consumers in a single Ray Serve application
- **added** — Enhanced support for Unity Catalog integration in Ray Data
- **added** — New expression evaluator infrastructure for improved query optimization in Ray Data
- **added** — Support for SaveMode in Ray Data write operations
- **added** — Approximate quantile aggregator in Ray Data
- **added** — MCAP datasource support for robotics data in Ray Data
- **added** — Callback-based stat computation for preprocessors and ValueCounter in Ray Data
- **added** — Support for multiple download URIs with improved error handling in Ray Data
- **added** — Top-level ray.train aliases for public APIs
- **changed** — Improved projection pushdown handling with renamed columns in Ray Data
- **changed** — Enhanced hash-shuffle performance with better retry policies in Ray Data
- **changed** — Reduced memory usage with improved streaming generator backpressure in Ray Data
- **fixed** — Fixed renamed columns to be appropriately dropped from output in Ray Data
- **fixed** — Fixed driver hang during streaming generator block metadata retrieval in Ray Data
- **fixed** — Fixed ControllerError triggered by after_worker_group_poll_status errors in Ray Train
- **fixed** — Fixed iter_torch_batches use of ray.train.torch.get_device outside Train

#### Release Highlights

**Ray Train:**
- Ray Train v2 is now enabled by default! Ray Train v2 provides usability and stability improvements, as well as new features. For more details, see the [REP](https://github.com/ray-project/enhancements/blob/main/reps/2024-10-18-train-tune-api-revamp/2024-10-18-train-tune-api-revamp.md)  and [Migration Guide](https://github.com/ray-project/ray/issues/49454). To disable Ray Train v2, set the environment variable `RAY_TRAIN_V2_ENABLED=0`.


**Ray Serve:**

- Application-level autoscaling: Introduces custom autoscaling policies that operate across all deployments in an application, enabling coordinated scaling decisions based on aggregate metrics. This is a significant advancement over per-deployment autoscaling, allowing for more intelligent resource management at the application level.
- Enhanced autoscaling capabilities with replica-level metrics: Wires up `AutoscalingContext` with `total_running_requests`, `total_queued_requests`, and `total_num_requests`, plus adds support for min, max, and time-weighted average aggregation functions. These improvements give users fine-grained control to implement sophisticated custom autoscaling policies based on real-time workload metrics.

#### Ray Libraries
##### Ray Data
🎉 New Features:

- Added enhanced support for Unity Catalog integration (#57954, #58049)
- New expression evaluator infrastructure for improved query optimization (#57778, #57855)
- Support for SaveMode in write operations (#57946)
- Added approximate quantile aggregator (#57598)
- MCAP datasource support for robotics data (#55716)
- Callback-based stat computation for preprocessors and ValueCounter (#56848)
- Support for multiple download URIs with improved error handling (#57775)

💫 Enhancements:

- Improved projection pushdown handling with renamed columns (#58033, #58037, #58040, #58071)
- Enhanced hash-shuffle performance with better retry policies (#57572)
- Streamlined concurrency parameter semantics (#57035)
- Improved execution progress rendering (#56992)
- Better handling of empty columns in pandas blocks (#57740)
- Enhanced support for complex data types and column operations (#57271)
- Reduced memory usage with improved streaming generator backpressure (#57688)
- Enhanced preemption testing and utilities (#57883)
- Improved Download operator display names (#57773)
- Better handling of variable-shaped tensors and tensor columns (#57240)
- Optimized aggregator execution with out-of-order processing by default (#57753)

🔨 Fixes:

- Fixed renamed columns to be appropriately dropped from output (#58040, #58071)
- Fixed handling of renames in projection pushdown (#58033, #58037)
- Fixed vLLMEngineStage field name inconsistency for images (#57980)
- Fixed driver hang during streaming generator block metadata retrieval (#56451)
- Fixed retry policy for hash-shuffle tasks (#57572)
- Fixed prefetch loop to avoid blocking on fetches (#57613)
- Fixed empty projection handling (#57740)
- Fixed errors with concatenation of mixed pyarrow native and extension types (#56811)

📖 Documentation:

- Updated document embedding benchmark to use canonical Ray Data API (#57977)
- Improved concurrency-related documentation (#57658)
- Updated preprocessing and data handling examples

##### Ray Train

🎉 New features
- Turn on Train v2 by default (#57857)
- Top-level `ray.train` aliases for public APIs (#57758)

💫 Enhancements
- Raise clear errors when mixing v1/v2 APIs (#57570)
- JAX backend: add `jax.distributed.shutdown()` for `JaxBackend` (#57802)
- Update `TrainingFailedError` module (#57865)
- Improve deprecation handling when `ray.train` methods are called from `ray.tune` (#57810)
- Enable deprecation warnings for legacy XGBoost/LightGBM trainers (#57280)

🔨 Fixes
- Fix `ControllerError` triggered by `after_worker_group_poll_status` errors (#57869)
- Fix `iter_torch_batches` use of `ray.train.torch.get_device` outsi

_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-2.51.0]_