# Ray 2.44.0 — Ray-2.44.0

- Product: Ray (https://whatsnew.fyi/product/ray)
- Vendor: Anyscale
- Date: 2025-03-21
- Version: 2.44.0
- Original notes: https://github.com/ray-project/ray/releases/tag/ray-2.44.0
- Permalink: https://whatsnew.fyi/product/ray/releases/2.44.0

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 Compiled Graph (beta) with less than 50us system overhead for repeatedly executed task graphs and native GPU-GPU communication via NCCL
- **added** — Iceberg write support through pyiceberg in Ray Data
- **added** — LoRA support and structured outputs enhancements to Ray Data LLM
- **added** — State export API in Ray Train
- **added** — Faster bulk imperative Serve Application deploys
- **added** — Gen-config for Ray Serve LLM
- **added** — Dataset/operator state, progress, and total metrics in Ray Data
- **changed** — Make chunk combination threshold configurable in Ray Data
- **changed** — Store average memory use per task in OpRuntimeMetrics
- **changed** — Avoid unnecessary conversion to Numpy when creating Arrow/Pandas blocks
- **changed** — Add append-mode API for preprocessors with vectorizers and hashers now outputting a single column instead of 1 column per feature
- **changed** — Fold v2.XGBoostTrainer API into the public trainer class as an alternate constructor
- **changed** — Create a default ScalingConfig if one is not provided to the trainer
- **changed** — Improve TrainingFailedError message in Ray Train
- **changed** — Clean up shutdown behavior of serve
- **changed** — Add additional_log_standard_attrs to serve logging config
- **fixed** — Fix Map Operators to avoid unconditionally overriding generator's back-pressure configuration
- **fixed** — Fix filter expr equating negative numbers in Ray Data
- **fixed** — Fix StandardScaler to handle NaN stats in Ray Data
- **deprecated** — Ray Workflows library has been deprecated and will be removed in a future version of Ray

#### Release Highlights

- This release features Ray Compiled Graph (beta). Ray Compiled Graph gives you a classic Ray Core-like API, but with (1) less than 50us system overhead for workloads that repeatedly execute the same task graph; and (2) native support for GPU-GPU communication via NCCL. Ray Compiled Graph APIs simplify high-performance multi-GPU workloads such as LLM inference and training. The beta release refines the API, enhances stability, and adds or improves features like visualization, profiling and experimental GPU compute/computation overlap. For more information, refer to Ray documentation: https://docs.ray.io/en/latest/ray-core/compiled-graph/ray-compiled-graph.html
- The experimental Ray Workflows library has been deprecated and will be removed in a future version of Ray. Ray Workflows has been marked experimental since its inception and hasn’t been maintained due to the Ray team focusing on other priorities. If you are using Ray Workflows, we recommend pinning your Ray version to 2.44.

#### Ray Libraries

##### Ray Data

🎉 New Features:
- Add Iceberg write support through pyiceberg[ (](https://github.com/ray-project/ray/commit/5e26c7fc3866921ce97db876136e04271dabf8b4)[#50590](https://github.com/ray-project/ray/pull/50590)[)](https://github.com/ray-project/ray/commit/5e26c7fc3866921ce97db876136e04271dabf8b4) 
- [LLM] Various feature enhancements to Ray Data LLM, including LoRA support #50804 and structured outputs #50901

💫 Enhancements:
- Add dataset/operator state, progress, total metrics ([#50770](https://github.com/ray-project/ray/pull/50770))
- Make chunk combination threshold configurable ([#51200](https://github.com/ray-project/ray/pull/51200))
- Store average memory use per task in OpRuntimeMetrics ([#51126](https://github.com/ray-project/ray/pull/51126))
- Avoid unnecessary conversion to Numpy when creating Arrow/Pandas blocks ([#51238](https://github.com/ray-project/ray/pull/51238))
- Append-mode API for preprocessors -- #50848, #50847, #50642, #50856, #50584. Note that vectorizers and hashers now output a single column instead 1 column per feature. In the near future, we will be graduating preprocessors to *beta*.

🔨 Fixes:
- Fixing Map Operators to avoid unconditionally overriding generator's back-pressure configuration ([#50900](https://github.com/ray-project/ray/pull/50900))
- Fix filter expr equating negative numbers ([#50932](https://github.com/ray-project/ray/pull/50932))
- Fix error message for `override_num_blocks` when reading from a HuggingFace Dataset  ([#50998](https://github.com/ray-project/ray/pull/50998))
- Make num_blocks in repartition optional ([#50997](https://github.com/ray-project/ray/pull/50997))
- Always pin the seed when doing file-based random shuffle ([#50924](https://github.com/ray-project/ray/pull/50924))
- Fix `StandardScaler` to handle `NaN` stats ([#51281](https://github.com/ray-project/ray/pull/51281))

##### Ray Train

🎉 New Features:
- Implement state export API (#50622, #51085, #51177)

💫 Enhancements:
- Folded v2.XGBoostTrainer API into the public trainer class as an alternate constructor (#50045)
- Created a default ScalingConfig if one is not provided to the trainer (#51093)
- Improved TrainingFailedError message (#51199)
- Utilize FailurePolicy factory (#51067)

🔨 Fixes:
- Fixed trainer import deserialization when captured within a Ray task (#50862)
- Fixed serialize import test for Python 3.12 (#50963)
- Fixed RunConfig deprecation message in Tune being emitted in trainer.fit usage (#51198)

📖 Documentation: 
- [Train V2] Updated API references (#51222)
- [Train V2] Updated persistent storage guide (#51202)
- [Train V2] Updated user guides for metrics, checkpoints, results, and experiment tracking (#51204)
- [Train V2] Added updated Train + Tune user guide (#51048)
- [Train V2] Added updated fault tolerance user guide (#51083)
- Improved HF Transformers example (#50896)
- Improved Train DeepSpeed ex

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