Bump ray[default] from 2.5.1 to 2.6.0

[dependabot]

Bumps ray[default] from 2.5.1 to 2.6.0.

Release notes

Sourced from ray[default]'s releases.

Ray-2.6.0

Release Highlights

• Serve: Better streaming support -- In this release, Support for HTTP streaming response and WebSockets is now on by default. Also, @serve.batch-decorated methods can stream responses.
• Train and Tune: Users are now expected to provide cloud storage or NFS path for distributed training or tuning jobs instead of a local path. This means that results written to different worker machines will not be directly synced to the head node. Instead, this will raise an error telling you to switch to one of the recommended alternatives: cloud storage or NFS. Please see ray-project/ray#37177 if you have questions.
• Data: We are introducing a new streaming integration of Ray Data and Ray Train. This allows streaming data ingestion for model training, and enables per-epoch data preprocessing. The DatasetPipeline API is also being deprecated in favor of Dataset with streaming execution.
• RLlib: Public alpha release for the new multi-gpu Learner API that is less complex and more powerful compared to our previous solution (blogpost). This is used under PPO algorithm by default.

Ray Libraries

Ray AIR

🎉 New Features:

• Added support for restoring Results from local trial directories. (#35406)

💫 Enhancements:

• [Train/Tune] Disable Train/Tune syncing to head node (#37142)
• [Train/Tune] Introduce new console output progress reporter for Train and Tune (#35389, #36154, #36072, #35770, #36764, #36765, #36156, #35977)
• [Train/Data] New Train<>Data streaming integration (#35236, #37215, #37383)

🔨 Fixes:

• Pass on KMS-related kwargs for s3fs (#35938)
• Fix infinite recursion in log redirection (#36644)
• Remove temporary checkpoint directories after restore (#37173)
• Removed actors that haven't been started shouldn't be tracked (#36020)
• Fix bug in execution for actor re-use (#36951)
• Cancel pg.ready() task for pending trials that end up reusing an actor (#35748)
• Add case for Dict[str, np.array] batches in DummyTrainer read bytes calculation (#36484)

📖 Documentation:

• Remove experimental features page, add github issue instead (#36950)
• Fix batch format in dreambooth example (#37102)
• Fix Checkpoint.from_checkpoint docstring (#35793)

🏗 Architecture refactoring:

• Remove deprecated mlflow and wandb integrations (#36860, #36899)
• Move constants from tune/results.py to air/constants.py (#35404)
• Clean up a few checkpoint related things. (#35321)

Ray Data

🎉 New Features:

• New streaming integration of Ray Data and Ray Train. This allows streaming data ingestion for model training, and enables per-epoch data preprocessing. (#35236)
• Enable execution optimizer by default (#36294, #35648, #35621, #35952)
• Deprecate DatasetPipeline (#35753)
• Add Dataset.unique() (#36655, #36802)
• Add option for parallelizing post-collation data batch operations in DataIterator.iter_batches() (#36842) (#37260)
• Enforce strict mode batch format for DataIterator.iter_batches() (#36686)
• Remove ray.data.range_arrow() (#35756)

... (truncated)

Commits

• 0db82e3 [air] Fix behavior of multi-node checkpointing without an external `storage_p...
• d2299ee [data] Fix "ImportError: sys.meta_path is None, Python is likely shutting dow...
• 4ebe568 [Logging] Switch worker_setup_hook to worker_process_setup_hook (#37247) (#37...
• b6a1a6f [Data] Cherry-pick #37359 (#37428)
• 6209bb4 Support both pydantic 1.x and 2.x (#37055) (#37468)
• 170e61a Cherry pick 37449 (#37459)
• ab942b8 Revert "Revert "[Core] Fix the race condition where grpc requests are handled...
• d340728 [core][autoscaler] Cherry picks change to autoscaler intereface (#37407)
• 5cc575c [Core] Fix test_get_wheel_filename (#37433)
• 941a905 [Core] Fix test_get_master_wheel_url (#37424)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #15.