build(deps): bump ray from 2.35.0 to 2.44.1

[dependabot]

Bumps ray from 2.35.0 to 2.44.1.

Release notes

Sourced from ray's releases.

Ray-2.44.1

Under screen-lit skies A ray of bliss in each patch Joy at any scale

Ray-2.44.0

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 (#50590)
• [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)
• Make chunk combination threshold configurable (#51200)
• Store average memory use per task in OpRuntimeMetrics (#51126)
• Avoid unnecessary conversion to Numpy when creating Arrow/Pandas blocks (#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)
• Fix filter expr equating negative numbers (#50932)
• Fix error message for override_num_blocks when reading from a HuggingFace Dataset (#50998)
• Make num_blocks in repartition optional (#50997)
• Always pin the seed when doing file-based random shuffle (#50924)
• Fix StandardScaler to handle NaN stats (#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)

... (truncated)

Commits

• daca7b2 version change for 2.44.1 (#51463)
• 36bed82 properly build cpp wheels (#51452)
• f468b3e release 2.44.0 version change
• 8ee3f00 [Release Test] Fixing test names of the release tests (#51377)
• ef47aec [core][dashboard] Return the correct error message when trying to kill non-ex...
• 936539c [data] Move core arrow util dependencies to arrow_utils.py (#51306)
• 81755fb add additional_log_standard_attrs to serve logging config (#51144)
• 65514ea [serve] Log rejected requests at router side (#51346)
• adfb750 [usage] add a haiku at the end of usage proto (#51362)
• 185e69e [Autoscaler] Update YAML example for CoordinatorSenderNodeProvider (#51292)
• Additional commits viewable in compare view

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