Bump ray[default,rllib] from 2.5.0 to 2.6.1

[dependabot]

Bumps ray[default,rllib] from 2.5.0 to 2.6.1.

Release notes

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

Ray-2.6.1

The Ray 2.6.1 patch release contains a critical fix for cluster launcher, and compatibility update for Ray Serve protobuf definition with python 3.11, as well doc improvements.

⚠️ Cluster launcher in Ray 2.6.0 fails to start multi-node clusters. Please update to 2.6.1 if you plan to use 2.6.0 cluster launcher.

Ray Core

🔨 Fixes:

• [core][autoscaler] Fix env variable overwrite not able to be used if the command itself uses the env #37675

Ray Serve

🔨 Fixes:

• [serve] Cherry-pick Serve enum to_proto fixes for Python 3.11 #37660

Ray Air

📖Documentation:

• [air][doc] Update docs to reflect head node syncing deprecation #37475

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)

... (truncated)

Commits

• d68bf04 Update version to 2.6.1 (#37679)
• cdbee21 [core][autoscaler] Fix env variable overwrite not able to be used if command ...
• 62b4a0a [serve] Cherry-pick Serve enum to_proto fixes for Python 3.11 (#37660)
• f7572b5 [DOC] Added in new CSAT widget (#37351) (#37487)
• e9f1dc4 [air][doc] Update docs to reflect head node syncing deprecation (#37475) (#37...
• 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)
• Additional commits viewable in compare view

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