Update ray[tune] requirement from ~=1.11.0 to ~=2.5.0

[dependabot[bot]]

Updates the requirements on ray[tune] to permit the latest version.

Release notes

Sourced from ray[tune]'s releases.

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)
  • Use Ray storage URI as default storage path, if configured [no_early_kickoff] (#34470)
  • Move to new storage_path API in tests and examples (#34263)

🔨 Fixes:

• Store unflattened metrics in _TrackedCheckpoint (#35658) (#35706)
• Fix test_tune_torch_get_device_gpu race condition (#35004)
• Deflake test_e2e_train_flow.py (#34308)
• Pin deepspeed version for now to unblock ci. (#34406)
• Fix AIR benchmark configuration link failure. (#34597)
• Fix unused config building function in lightning MNIST example.

📖Documentation:

• Change doc occurrences of ray.data.Dataset to ray.data.Datastream (#34520)
• DreamBooth example: Fix code for batch size > 1 (#34398)
• Synced tabs in AIR getting started (#35170)
• New Ray AIR link for try it out (#34924)
• Correctly Render the Enumerate Numbers in convert_torch_code_to_ray_air (#35224)

... (truncated)

Commits

• 586c376 [RLlib][Docs] Fix failing catalog docs include (#36083) (#36103)
• 9a9d853 Remove rc0 from version numbers (#36096)
• 657e2f4 [Serve] Improve the replica recover scenario (#35946) (#36086)
• df3ef84 [docs] Fixing inconsistent height of cards on landing page (#35995) (#36031)
• 9e28c2f [doc][core][dashboard][state] Promote state api and dashboard usage in core u...
• 8793d80 [Serve][Doc] Multiplexing doc (#35701) (#35885)
• 34b5620 [docs] kapa hotfix (#36036) (#36048)
• c9ce481 Cherry pick docs PRs for contributor page, landing page, o11y (#35969)
• 5bb3d90 [Java] Upgrade TestNG to patch CVEs (#36027) (#36055)
• a391dea Deflake test_worker_kv_calls (#35744) (#36010)
• Additional commits viewable in compare view

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[dependabot[bot]]

Superseded by #283.