Bump ray[default,rllib] from 2.5.0 to 2.9.1

[dependabot[bot]]

Bumps ray[default,rllib] from 2.5.0 to 2.9.1.

Release notes

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

Ray-2.9.1

This patch release contains fixes for Ray Core, Ray Data, and Ray Serve.

Ray Core

🔨 Fixes:

• Adding debupgy as the ray debugger (#42311)
• Fix task events profile events per task leak (#42248)
• Make sure redis sync context and async context connect to the same redis instance (#42040)

Ray Data

🔨 Fixes:

• [Data] Retry write if error during file clean up (#42326)

Ray Serve

🔨 Fixes:

• Improve handling the websocket server disconnect scenario (#42130)
• Fix pydantic config documentation (#42216)
• Address issues under high network delays:
  • Enable setting queue length response deadline via environment variable (#42001)
  • Add exponential backoff for queue_len_response_deadline_s (#42041)

Ray-2.9.0

Release Highlights

• This release contains fixes for the Ray Dashboard. Additional context can be found here: https://www.anyscale.com/blog/update-on-ray-cves-cve-2023-6019-cve-2023-6020-cve-2023-6021-cve-2023-48022-cve-2023-48023 
• Ray Train has now upgraded support for spot node preemption -- allowing Ray Train to handle preemption node failures differently than application errors.
• Ray is now compatible with Pydantic versions =2.5.0, addressing a piece of user feedback we’ve consistently received.
• The Ray Dashboard now has a page for Ray Data to monitor real-time execution metrics.
• Streaming generator is now officially a public API (#41436, #38784). Streaming generator allows writing streaming applications easily on top of Ray via Python generator API and has been used for Ray Serve and Ray data for several releases. See the documentation for details. 
• We’ve added experimental support for new accelerators: Intel GPU (#38553), Intel Gaudi Accelerators (#40561), and Huawei Ascend NPU (#41256).

Ray Libraries

Ray Data

🎉 New Features:

• Add the dashboard for Ray Data to monitor real-time execution metrics and log file for debugging (https://docs.ray.io/en/master/data/monitoring-your-workload.html).
• Introduce concurrency argument to replace ComputeStrategy in map-like APIs (#41461)
• Allow task failures during execution (#41226)
• Support PyArrow 14.0.1 (#41036)
• Add new API for reading and writing Datasource (ray-project/ray#40296)
• Enable group-by over multiple keys in datasets (#37832)
• Add support for multiple group keys in map_groups (#40778)

💫 Enhancements:

• Optimize OpState.outqueue_num_blocks (#41748)
• Improve stall detection for StreamingOutputsBackpressurePolicy (#41637)
• Enable read-only Datasets to be executed on new execution backend (#41466, #41597)
• Inherit block size from downstream ops (#41019)
• Use runtime object memory for scheduling (#41383)

... (truncated)

Commits

• cfbf98c [Debugger] Adding debupgy as the ray debugger (#42311) (#42361)
• dc0c031 [Data] Retry write if error during file clean up (#42326) (#42327)
• b2d50b8 [core][gcs] Fix task events profile events per task leak (#42248) (#42268)
• ada0a92 [Serve] Improve handling the websocket server disconnect scenario (#42130) (#...
• 84a66ae [Serve][Doc] Fix pydantic config documentation (#42216) (#42241)
• 0842d27 Pick of 42226 (#42236)
• 3bb96d2 [serve] Cherry-pick queue length deadline configuration changes (#42176)
• e7a883d [ci] pick of 42185 (#42207)
• 0f221ee pick of 41540 (#42179)
• 90b9e45 pick of 42175 (#42177)
• Additional commits viewable in compare view

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