# Ray 2.50.0 — Ray-2.50.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2025-10-10
- Version: 2.50.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.50.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.50.0

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- **changed** — Default shuffle strategy changed from sort-based to hash-based for improved shuffle performance and lower peak memory usage for aggregations
- **added** — New expression API enables predicate-based filtering, UDF transformations with with_column, and column aliasing for more powerful data transformations
- **added** — Added support for projection pushdown into Parquet reads
- **added** — New download expression enables efficient loading of data from columns containing URIs with improved performance and error handling
- **added** — New explain() API provides insights into dataset execution plans
- **added** — Added streaming_train_test_split to avoid materialization for train/test splits
- **added** — Enabled multi-node tensor and pipeline parallelism for Ray Data LLM processing
- **added** — Added chat_template_kwargs parameter for customizing chat templates in Ray Data LLM
- **added** — Added support for OpenAI's nested image URL format in Ray Data LLM multimodal pipelines
- **added** — vLLM engines can now be shared across sequential processors for better resource utilization
- **added** — Enhanced Dataset.stats() output with input/output row counts per operator
- **added** — Added new metrics for task duration, inputs per task, and output blocks
- **added** — Added time to first batch metric for better iteration performance monitoring
- **added** — Added type-specific aggregators for numerical, categorical, and vector columns
- **added** — Added fine-grained concurrency controls with max_task_concurrency and resource allocation options
- **added** — Alpha release of Ray Direct Transport enabling GPU data to remain in GPU memory with efficient data transfer via collective communication and RDMA
- **added** — Added StepFailedRecreateEnv exception for users with unsatisfiable environments in RLLib
- **added** — Async Inference alpha release for Ray Serve with APIs supporting long-running asynchronous inference tasks and message broker support
- **added** — Added support for replica ranks in Ray Serve for large-model inference use-cases
- **added** — Added FastAPI factory pattern support in Ray Serve for using non-serializable FastAPI plugins
- **added** — zip() operator now supports combining multiple datasets, not just pairs
- **changed** — Concurrency parameter now accepts tuples for more flexible configuration
- **changed** — Resource requirements (num_cpus/gpus, memory) are now top-level parameters in most APIs for easier configuration
- **changed** — Write operations now use iterators instead of accumulating blocks in memory
- **changed** — Improved groupby performance with sort-shuffle pull-based approach
- **changed** — Improved join operations with new abstractions
- **changed** — Improved compatibility between PyArrow native types, extension types, and pandas Arrow dtypes
- **changed** — Joins now supported with list/tensor non-key columns
- **changed** — Enhanced support for variable-shaped tensor arrays with different dimensions
- **changed** — Added serialization/deserialization for PyArrow Extension Arrays
- **changed** — Reduced memory usage for OneHotEncoder
- **changed** — Reduced memory usage for schema unification
- **changed** — Eliminated unnecessary block copying and double execution of arrow conversions
- **changed** — Improved Parquet encoding ratio estimation
- **changed** — Enabled per-block limiting for Limit operator
- **changed** — Optimized schema handling with deduplication and removed unnecessary unification
- **fixed** — Fixed streaming executor to properly drain output queues
- **fixed** — Fixed retry logic for hash shuffle operations
- **fixed** — Fixed split_blocks to prevent producing empty blocks

#### Release Highlights
**Ray Data:** 
This release offers many updates to Ray Data, including: 
- The default shuffle strategy is now changed from sort-based to hash-based. This will result in much lower peak memory usage and improved shuffle performance for aggregations.
- We’ve added a new expression API enables predicate-based filtering, UDF transformations with `with_column`, and column aliasing for more powerful data transformations
- Ray Data LLM has a number of new enhancements for multimodal data pipelines, including multi-node tensor and pipeline parallelism support per replica and ability to share vLLM engines across processors.


**Ray Core:** 

Alpha release of **Ray Direct Transport** (formerly GPU objects) - simply enable it by adding the `tensor_transport` parameter to the existing native Ray Core API. This keeps GPU data in GPU memory until a transfer is needed, avoiding expensive serialization and copies to and from the Ray object store. It uses efficient data transports such as collective communication libraries (GLOO or NCCL) or point-to-point RDMA (via NVIDIA’s NIXL) to transfer data directly between devices, including both CPUs and GPUs.

**Ray Train:**

Local mode support for multi-process training with `torchrun`, enhanced checkpoint management with new upload modes and validation functions


**Ray Serve:**
- Async Inference alpha release -  New Ray Serve APIs for supporting long-running asynchronous inference tasks, such as for video or large document processing. Includes capabilities for using different message brokers, adapters like `celery` and DLQ.
- Support for replica ranks - Replica level ranks are added for supporting large-model inference use-cases such as wide Data Parallel and Expert Parallel setups.
- FastAPI factory pattern support - Enables using FastAPI plugins that are not serializable via cloudpickle.  
- Throughput optimizations - Enable these using the `RAY_SERVE_THROUGHPUT_OPTIMIZED` environment variable. 

**RLLib:**
Add `StepFailedRecreateEnv` exception for users with unsatisfiable environments

**Ray Serve/Data LLM:**

Improvements to multi node serving, loading models from remote storages, and sharing resources for efficiency (fractional gpus, sharing gpus on a data pipeline with shared stages)

#### Ray Libraries
##### Ray Data

🎉 New Features:

- **Expression and Filtering API:** New expression API enables predicate-based filtering, UDF transformations with with_column, and column aliasing for more powerful data transformations (#56716, #56313, #56550, #55915, #55788, #56193, #56596)
- Added support for projection pushdown into Parquet reads (#56500)
- New download expression enables efficient loading of data from columns containing URIs with improved performance and error handling (#55824, #56462, #56294, #56852, #57146)
- New `explain()` API provides insights into dataset execution plans (#55482)
- Added `streaming_train_test_split` to avoid materialization for train/test splits (#56803)
- **Ray Data LLM:**
  - Enabled multi-node tensor and pipeline parallelism for LLM processing (#56779)
  - Added `chat_template_kwargs` parameter for customizing chat templates (#56490)
  - Added support for OpenAI's nested image URL format in multimodal pipelines (#56584)
  - vLLM engines can now be shared across sequential processors for better resource utilization (#55179)
- Enhanced Dataset.stats() output with input/output row counts per operator (#56040)
- Added new metrics for task duration, inputs per task, and output blocks (#56958, #56379)
- Time to first batch metric for better iteration performance monitoring (#55758)
- Added type-specific aggregators for numerical, categorical, and vector columns (#56610)
- Added fine-grained concurrency controls with `max_task_concurrency` and resource allocation options (#56370, #56381)

💫 Enhancements:

- **Join and shuffle improvements:** 
  - Default shuffle strategy changed from sort-b

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