# Ray 2.48.0 — Ray-2.48.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2025-07-18
- Version: 2.48.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.48.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.48.0

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** — Add reading from Delta Lake tables and Unity Catalog integration
- **added** — Enhanced pin_memory support in iter_torch_batches
- **added** — Added broadcast_from_rank_zero and barrier collective operations
- **added** — Enhanced hardware metrics integration with Grafana dashboards
- **added** — Added support for dynamically loading callbacks via environment variables
- **added** — Added RouterConfig field to DeploymentConfig for custom RequestRouter configuration
- **added** — Added support for implementing custom request routing algorithms
- **added** — Support separate deployment config for PDProxy in PrefixAwareReplicaSet
- **added** — Support for prefix-aware request router
- **changed** — Enhanced handling of mismatched columns and pandas.NA values
- **changed** — Improved read_text trailing newline semantics
- **changed** — Optimized backpressure handling with policy-based resource management
- **changed** — Enhanced write_parquet with support for both partition_by and row limits
- **changed** — Improved execution performance for One Hot encoding in preprocessors
- **changed** — Improved checkpoint population from before_init_train_context
- **changed** — Enhanced controller state logging and metrics
- **changed** — Enhanced FastAPI ingress deployment validation for multiple deployments
- **changed** — Optimized get_live_deployments performance
- **fixed** — Fixed map_groups issues
- **fixed** — Prevented Op fusion for streaming repartition to avoid performance degradation
- **fixed** — Fixed ActorPool autoscaler scaling up logic
- **fixed** — Resolved empty dataset repartitioning issues

#### Release Highlights
- **Ray Data**: This release features a new Delta Lake and Unity Catalog integration and performance improvements to various reading/writing operators.
- **Ray Core**: Enhanced GPU object support with intra-process communication and improved Autoscaler v2 functionality
- **Ray Train**: Improved hardware metrics integration with Grafana and enhanced collective operations support
- **Ray Serve LLM**: This release features early proof of concept for prefill-decode disaggregation deployment and LLM-aware request routing such as prefix-cache aware routing. 
- **Ray Data LLM**: Improved throughput and CPU memory utilization for ray data workers. 

#### Ray Libraries

##### Ray Data

🎉 New Features:
- Add reading from Delta Lake tables and Unity Catalog integration (#53701)
- Enhanced pin_memory support in iter_torch_batches (#53792)
- Add pin_memory to iter_torch_batches (#53792)

💫 Enhancements:

- Re-enabled sorting in Ray Data tests with performance improvements (#54475)
- Enhanced handling of mismatched columns and pandas.NA values (#53861, #53859)
- Improved read_text trailing newline semantics (#53860)
- Optimized backpressure handling with policy-based resource management (#54376)
- Enhanced write_parquet with support for both partition_by and row limits (#53930)
- Prevent filename collisions on write operations (#53890)
- Improved execution performance for One Hot encoding in preprocessors (#54022)

🔨 Fixes:

- Fixing map_groups issues (#54462)
- Prevented Op fusion for streaming repartition to avoid performance degradation (#54469)
- Fixed ActorPool autoscaler scaling up logic (#53983)
- Resolved empty dataset repartitioning issues (#54107)
- Fixed PyArrow overflow handling in data processing (#53971, #54390)
- Fixed IcebergDatasink to properly generate individual file uuids (#52956)
- Avoid OOMs with read_json(..., lines=True) (#54436)
- Handle HuggingFace parquet dataset resolve URLs (#54146)
- Fixed BlockMetadata derivation for Read operator (#53908)

📖 Documentation:

- Updated AggregateFnV2 documentation to clarify finalize method (#53835)
- Improved preprocessor and vectorizer API documentation


##### Ray Train

🎉 New Features:

- Added broadcast_from_rank_zero and barrier collective operations (#54066)
- Enhanced hardware metrics integration with Grafana dashboards (#53218)
- Added support for dynamically loading callbacks via environment variables (#54233)

💫 Enhancements:

- Improved checkpoint population from before_init_train_context (#54453)
- Enhanced controller state logging and metrics (#52805)
- Added structured logging environment variable support (#52952)
- Improved handling of Noop scaling decisions for smoother scaling logic (#53180)
- Logging of controller state transitions to aid in debugging and analysis (#53344)

🔨 Fixes:

- Fixed GPU tensor reporting in ray.train.report (#53725)
- Enhanced move_tensors_to_device utility for complex tensor structures (#53109)
- Improved worker health check error handling with trace information (#53626)
- Fixed GPU transfer support for non-contiguous tensors (#52548)
- Force abort on SIGINT spam and do not abort finished runs (#54188)

📖 Documentation:

- Updated beginner PyTorch example (#54124)
- Added documentation for ray.train.collective APIs (#54340)
- Added a note about PyTorch DataLoader's multiprocessing and forkserver usage (#52924)
- Fixed various docstring format and indentation issues (#52855, #52878)
- Added note that ray.train.report API docs should mention optional checkpoint_dir_name (#54391)

🏗 Architecture refactoring:

- Removed subclass relationship between RunConfig and RunConfigV1 (#54293)
- Enhanced error handling for finished training runs (#54188)
- Deduplicated ML doctest runners in CI for efficiency (#53157)
- Converted isort configuration to Ruff for consistency (#52869)


##### Ray Tune

💫 Enhancements:

- Updated tes

_[Truncated at 4000 characters — full notes: https://github.com/ray-project/ray/releases/tag/ray-2.48.0]_