Bump ray[default,rllib] from 2.0.0 to 2.5.1

[dependabot]

Bumps ray[default,rllib] from 2.0.0 to 2.5.1.

Release notes

Sourced from ray[default,rllib]'s releases.

Ray-2.5.1

The Ray 2.5.1 patch release adds wheels for MacOS for Python 3.11. It also contains fixes for multiple components, along with fixes for our documentation.

Ray Train

🔨 Fixes:

• Don't error on eventual success when running with auto-recovery (#36266)

Ray Core

🎉 New Features:

• Build Python wheels on Mac OS for Python 3.11 (#36373)

🔨 Fixes:

• [Autoscaler] Fix a bug that can cause undefined behavior when clusters attempt to scale up aggressively. (#36241)
• Fix mypy error: Module "ray" does not explicitly export attribute "remote" (#36356)

Ray-2.5.0

The Ray 2.5 release features focus on a number of enhancements and improvements across the Ray ecosystem, including:

• Training LLMs with Ray Train: New support for checkpointing distributed models, and Pytorch Lightning FSDP to enable training large models on Ray Train’s LightningTrainer
• LLM applications with Ray Serve & Core: New support for streaming responses and model multiplexing
• Improvements to Ray Data: In 2.5, strict mode is enabled by default. This means that schemas are required for all Datasets, and standalone Python objects are no longer supported. Also, the default batch format is fixed to NumPy, giving better performance for batch inference.
• RLlib enhancements: New support for multi-gpu training, along with ray-project/rllib-contrib to contain the community contributed algorithms
• Core enhancements: Enable new feature of lightweight resource broadcasting to improve reliability and scalability. Add many enhancements for Core reliability, logging, scheduler, and worker process.

Ray Libraries

Ray AIR

💫Enhancements:

• Experiment restore stress tests (#33706)
• Context-aware output engine
  • Add parameter columns to status table (#35388)
  • Context-aware output engine: Add docs, experimental feature docs, prepare default on (#35129)
  • Fix trial status at end (more info + cut off) (#35128)
  • Improve leaked mentions of Tune concepts (#35003)
  • Improve passed time display (#34951)
  • Use flat metrics in results report, use Trainable._progress_metrics (#35035)
  • Print experiment information at experiment start (#34952)
  • Print single trial config + results as table (#34788)
  • Print out worker ip for distributed train workers. (#33807)
  • Minor fix to print configuration on start. (#34575)
  • Check air_verbosity against None. (#33871)
  • Better wording for empty config. (#33811)
• Flatten config and metrics before passing to mlflow (#35074)
• Remote_storage: Prefer fsspec filesystems over native pyarrow (#34663)
• Use filesystem wrapper to exclude files from upload (#34102)
• GCE test variants for air_benchmark and air_examples (#34466)
• New storage path configuration
  • Add RunConfig.storage_path to replace SyncConfig.upload_dir and RunConfig.local_dir. (#33463)

... (truncated)

Commits

• a03efd9 Fix mypy error: Module "ray" does not explicitly export attribute "remote" (#...
• 1d09d67 [docs]change recommended install to ray[air] (#35149) (#36248)
• b42decf [Release Test] Pin library versions to fix LightningTrainer FSDP example (#36...
• 13e179e [docs] cherry-pick Cp10 2.5.1 (#36280)
• 65deb10 Fix dask on ray table (#36273)
• 7045e90 Pick (#36272)
• 58562cb [Data] [Docs] Doc picks for 2.5.1 (#36267)
• da7c2de [Cherry-pick][Train] Don't error on eventual success when running with auto-r...
• 14d3aaf Update dask on Ray table (#36240)
• c658e57 [Release] Update Version numbers to 2.5.1 (#36238)
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