# Ray 2.46.0 — Ray-2.46.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2025-05-07
- Version: 2.46.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.46.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.46.0

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** — Ray Data now supports hash-shuffle based repartitioning and aggregations
- **added** — Ray Data now supports joins using hash-shuffle
- **added** — Ray Serve LLM is now compatible with vLLM v1
- **added** — Add initial Train Grafana dashboard with built-in metrics for training workloads
- **added** — Offline Evaluation with loss function for Offline RL pipeline, introducing three new callbacks: on_offline_evaluate_start, on_offline_evaluate_end, on_offline_eval_runners_recreated
- **added** — New custom_data attribute for SingleAgentEpisode and MultiAgentEpisode to store custom metrics
- **changed** — Add memory attribute to ExecutionResources
- **changed** — Support ray_remote_args for read_tfrecords
- **changed** — Add PhysicalOperator.min_max_resource_usage_bounds
- **changed** — Support num_cpus, memory, concurrency, batch_size for preprocess
- **changed** — Support new pyiceberg version
- **changed** — Lazily import torch FSDP for ray.train.torch module to improve performance and reduce unnecessary dependencies
- **changed** — Deserialize the user-defined training function directly on workers, improving efficiency
- **changed** — Refactor LLMServer and LLMEngine to not diverge too much from vllm chat formatting logic
- **changed** — Add router replicas and batch size to llm config
- **changed** — Bump vllm from 0.8.2 to 0.8.5
- **fixed** — Handle Arrow Array null types in to_numpy
- **fixed** — Fix S3 serialization wrapper compatibility with RetryingPyFileSystem
- **fixed** — Fix Optimizer to apply rules until plan stabilizes
- **fixed** — Fix FuseOperators rule to properly handle transformations that drastically change size of the dataset
- **fixed** — Fix error when no arguments are passed into TorchTrainer

#### Release Highlights
The 2.46 Ray release comes with a couple core highlights:

1. Ray Data now supports **hash shuffling for repartition and aggregations, along with support for joins**. This enables many new data processing workloads to be run on Ray Data. Please give it a try and let us know if you have any feedback!
2. Ray Serve LLM now **supports vLLM v1** to be forward-compatible with upcoming vLLM releases. This also opens up significant performance improvements that come with vLLM's v1 refactor.
3. There is a new **Train Grafana dashboard** which provides in-depth metrics on Grafana for better metrics on training workloads.

#### Ray Libraries
##### Ray Data
🎉 New Features:
- Adding support for hash-shuffle based repartitioning and aggregations (#52664)
- Added support for Joins (using hash-shuffle) (#52728)
- [LLM] vLLM support upgrades to 0.8.5 (#52344)

💫 Enhancements:
- Add memory attribute to ExecutionResources (#51127)
- Support ray_remote_args for read_tfrecords #52450
- [data.dashboard] Skip reporting internal metrics (#52666)
- Add PhysicalOperator.min_max_resource_usage_bounds (#52502)
- Speed up printing the schema (#52612)
- [data.dashboard] Dataset logger for worker (#52706) 
- Support new pyiceberg version (#51744) 
- Support num_cpus, memory, concurrency, batch_size for preprocess (#52574)

🔨 Fixes:
- Handle Arrow Array null types in to_numpy (#52572)
- Fix S3 serialization wrapper compatibility with RetryingPyFileSystem (#52568)
- Fixing Optimizer to apply rules until plan stabilize; (#52663)
- Fixing FuseOperators rule to properly handle the case of transformations drastically changing size of the dataset (#52570)

📖 Documentation:
- [LLM] Improve concurrency settings, improve prompt to achieve better throughput (#52634)

##### Ray Train
🎉 New Features:
- Add initial Train Grafana dashboard (#52709)

💫 Enhancements:
- Lazily import torch FSDP for ray.train.torch module to improve performance and reduce unnecessary dependencies (#52707)
- Deserialize the user-defined training function directly on workers, improving efficiency (#52684)

🔨 Fixes:
- Fixed error when no arguments are passed into TorchTrainer (#52693)

📖 Documentation:
- Added new XGBoostTrainer user guide (#52355)

🏗 Architecture refactoring:
- Re-enabled isort for python/ray/train to maintain code formatting consistency (#52717)

##### Ray Tune
📖 Documentation:
- Fixed typo in Ray Tune PyTorch Lightning docs (#52756)

##### Ray Serve
💫 Enhancements:
- [LLM] Refactor LLMServer and LLMEngine to not diverge too much from vllm chat formatting logic ([#52597](https://github.com/ray-project/ray/pull/52597))
- Bump vllm from 0.8.2 to 0.8.5 in /python ([#52344](https://github.com/ray-project/ray/pull/52344))
- [LLM] Add router replicas and batch size to llm config ([#52655](https://github.com/ray-project/ray/pull/52655))

🔨 Fixes:
- Request cancellation not propagating correctly across deployments (#52591)
- BackpressureError not properly propagated in FastAPI ingress deployments (#52397)
- Hanging issue when awaiting deployment responses (#52561)
- [Serve.llm] made Ray Serve LLM compatible with vLLM v1 ([#52668](https://github.com/ray-project/ray/pull/52668))

📖 Documentation:
- [Serve][LLM] Add doc for deploying DeepSeek ([#52592](https://github.com/ray-project/ray/pull/52592))

##### RLLib
🎉 New Features:
- Offline Evaluation with loss function for Offline RL pipeline. Introduces three new callbacks, `on_offline_evaluate_start`, `on_offline_evaluate_end`, `on_offline_eval_runners_recreated` (#52308)

💫 Enhancements:
- New `custom_data` attribute for `SingleAgentEpisode` and `MultiAgentEpisode` to store custom metrics. Deprecates `add|get_temporary_timestep_data()` (#52603)

#### Ray Core
💫 Enhancements:
- Only get serialization context once for all .remote args (#52690)
- Add grpc server success and fail count metric (#52711)

🔨 Fixes:
- Fix op

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