Releases · ray-project/ray · GitHub

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 metho