Bump ray from 2.39.0 to 2.43.0 in /infrastructure/efficient model inference/ray-server

[dependabot]

Bumps ray from 2.39.0 to 2.43.0.

Release notes

Sourced from ray's releases.

Ray-2.43.0

Highlights

• This release features new modules in Ray Serve and Ray Data for integration with large language models, marking the first step of addressing #50639. Existing Ray Data and Ray Serve have limited support for LLM deployments, where users have to manually configure and manage the underlying LLM engine. In this release, we offer APIs for both batch inference and serving of LLMs within Ray in ray.data.llm and ray.serve.llm. See the below notes for more details. These APIs are marked as alpha -- meaning they may change in future releases without a deprecation period.
• Ray Train V2 is available to try starting in Ray 2.43! Run your next Ray Train job with the RAY_TRAIN_V2_ENABLED=1 environment variable. See the migration guide for more information.
• A new integration with uv run that allows easily specifying Python dependencies for both driver and workers in a consistent way and enables quick iterations for development of Ray applications (#50160, 50462), check out our blog post

Ray Libraries

Ray Data

🎉 New Features:

• Ray Data LLM: We are introducing a new module in Ray Data for batch inference with LLMs (currently marked as alpha). It offers a new Processor abstraction that interoperates with existing Ray Data pipelines. This abstraction can be configured two ways:
  • Using the vLLMEngineProcessorConfig, which configures vLLM to load model replicas for high throughput model inference
  • Using the HttpRequestProcessorConfig, which sends HTTP requests to an OpenAI-compatible endpoint for inference.
  • Documentation for these features can be found here.
• Implement accurate memory accounting for UnionOperator (#50436)
• Implement accurate memory accounting for all-to-all operations (#50290)

💫 Enhancements:

• Support class constructor args for filter() (#50245)
• Persist ParquetDatasource metadata. (#50332)
• Rebasing ShufflingBatcher onto try_combine_chunked_columns (#50296)
• Improve warning message if required dependency isn't installed (#50464)
• Move data-related test logic out of core tests directory (#50482)
• Pass executor as an argument to ExecutionCallback (#50165)
• Add operator id info to task+actor (#50323)
• Abstracting common methods, removing duplication in ArrowBlockAccessor, PandasBlockAccessor (#50498)
• Warn if map UDF is too large (#50611)
• Replace AggregateFn with AggregateFnV2, cleaning up Aggregation infrastructure (#50585)
• Simplify Operator.repr (#50620)
• Adding in TaskDurationStats and on_execution_step callback (#50766)
• Print Resource Manager stats in release tests (#50801)

🔨 Fixes:

• Fix invalid escape sequences in grouped_data.py docstrings (#50392)
• Deflake test_map_batches_async_generator (#50459)
• Avoid memory leak with pyarrow.infer_type on datetime arrays (#50403)
• Fix parquet partition cols to support tensors types (#50591)
• Fixing aggregation protocol to be appropriately associative (#50757)

📖 Documentation:

• Remove "Stable Diffusion Batch Prediction with Ray Data" example (#50460)

Ray Train

🎉 New Features:

• Ray Train V2 is available to try starting in Ray 2.43! Run your next Ray Train job with the RAY_TRAIN_V2_ENABLED=1 environment variable. See the migration guide for more information.

💫 Enhancements:

• Add a training ingest benchmark release test (#50019, #50299) with a fault tolerance variant (#50399)
• Add telemetry for Trainer usage in V2 (#50321)
• Add pydantic as a ray[train] extra install (#46682)

... (truncated)

Commits

• 744eaa9 [serve.llm] Cherry-pick - Fix quickstart serve LLM docs (#50910) (#50953)
• ecdcdc6 [llm.serving] Reconfigure router to better perform under high concurrency (#5...
• 84f2764 cherrypick #50860 (#50867)
• ecd0709 [llm.serving] Fix using uni executor when world size == 1 (#50849) (#50863)
• cd9e467 [core] Guard concurrent access to generator IDs with a mutex (#50845)
• df8546c cherrypick #50841 (#50842)
• 6d46ba3 cherrypick #50836 (#50840)
• 034ca06 cherrypick #50816 and #50829 (#50834)
• acda02d cherrypick #50805 (#50832)
• bece652 [release] change version to 2.43.0 (#50831)
• Additional commits viewable in compare view

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