Bump ray[default] from 2.9.3 to 2.10.0

[dependabot]

Bumps ray[default] from 2.9.3 to 2.10.0.

Release notes

Sourced from ray[default]'s releases.

Ray-2.10.0

Release Highlights

Ray 2.10 release brings important stability improvements and enhancements to Ray Data, with Ray Data becoming generally available (GA).

• [Data] Ray Data becomes generally available with stability improvements in streaming execution, reading and writing data, better tasks concurrency control, and debuggability improvement with dashboard, logging and metrics visualization.
• [RLlib] “New API Stack” officially announced as alpha for PPO and SAC.
• [Serve] Added a default autoscaling policy set via num_replicas=”auto” (#42613).
• [Serve] Added support for active load shedding via max_queued_requests (#42950).
• [Serve] Added replica queue length caching to the DeploymentHandle scheduler (#42943).
  • This should improve overhead in the Serve proxy and handles.
  • max_ongoing_requests (max_concurrent_queries) is also now strictly enforced (#42947).
  • If you see any issues, please report them on GitHub and you can disable this behavior by setting: RAY_SERVE_ENABLE_QUEUE_LENGTH_CACHE=0.
• [Serve] Renamed the following parameters. Each of the old names will be supported for another release before removal.
  • max_concurrent_queries -> max_ongoing_requests
  • target_num_ongoing_requests_per_replica -> target_ongoing_requests
  • downscale_smoothing_factor -> downscaling_factor
  • upscale_smoothing_factor -> upscaling_factor
• [Serve] WARNING: the following default values will change in Ray 2.11:
  • Default for max_ongoing_requests will change from 100 to 5.
  • Default for target_ongoing_requests will change from 1 to 2.
• [Core] Autoscaler v2 is in alpha and can be tried out with Kuberay. It has improved observability and stability compared to v1.
• [Train] Added support for accelerator types via ScalingConfig(accelerator_type).
• [Train] Revamped the XGBoostTrainer and LightGBMTrainer to no longer depend on xgboost_ray and lightgbm_ray. A new, more flexible API will be released in a future release.
• [Train/Tune] Refactored local staging directory to remove the need for local_dir and RAY_AIR_LOCAL_CACHE_DIR.

Ray Libraries

Ray Data

🎉 New Features:

• Streaming execution stability improvement to avoid memory issue, including per-operator resource reservation, streaming generator output buffer management, and better runtime resource estimation (#43026, #43171, #43298, #43299, #42930, #42504)
• Metadata read stability improvement to avoid AWS transient error, including retry on application-level exception, spread tasks across multiple nodes, and configure retry interval (#42044, #43216, #42922, #42759).
• Allow tasks concurrency control for read, map, and write APIs (#42849, #43113, #43177, #42637)
• Data dashboard and statistics improvement with more runtime metrics for each components (#43790, #43628, #43241, #43477, #43110, #43112)
• Allow to specify application-level error to retry for actor task (#42492)
• Add num_rows_per_file parameter to file-based writes (#42694)
• Add DataIterator.materialize (#43210)
• Skip schema call in DataIterator.to_tf if tf.TypeSpec is provided (#42917)
• Add option to append for Dataset.write_bigquery (#42584)
• Deprecate legacy components and classes (#43575, #43178, #43347, #43349, #43342, #43341, #42936, #43144, #43022, #43023)

💫 Enhancements:

• Restructure stdout logging for better readability (#43360)
• Add a more performant way to read large TFRecord datasets (#42277)
• Modify ImageDatasource to use Image.BILINEAR as the default image resampling filter (#43484)
• Reduce internal stack trace output by default (#43251)
• Perform incremental writes to Parquet files (#43563)
• Warn on excessive driver memory usage during shuffle ops (#42574)
• Distributed reads for ray.data.from_huggingface (#42599)

... (truncated)

Commits

• 09abba2 [data] turn of actor locality by default (#44124)
• 6e85651 [doc][serve] document auto num replicas (#43926) (#44050)
• 26295a5 [Data] [Docs] Prose and formatting updates (Working with Tensors / Working wi...
• 27b68d1 Cherry pick #44008 and #44022 (#44095)
• e032d7c [core] Streaming generator task waits for all object report acks before finis...
• f40a62d [serve] Drop stale handle metrics (#44045) (#44053)
• ad6d26b [serve] Remove warnings for upcoming default change (#44051) (#44057)
• 4176911 [ci] remove ray linkcheck (#44048) (#44054)
• 4a7c71c [Data] Remove 'Using Preprocessors' (#44006) (#44042)
• 9e2ebea [Data] Revise batch inference user guide (#44007) (#44041)
• Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot merge will merge this PR after your CI passes on it
• @dependabot squash and merge will squash and merge this PR after your CI passes on it
• @dependabot cancel merge will cancel a previously requested merge and block automerging
• @dependabot reopen will reopen this PR if it is closed
• @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
• @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency
• @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

[amitschang]

I'll look into how we can upgrade

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 100.00%. Comparing base (c09e53b) to head (69c25ac).
Report is 2 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff            @@
##              main       #69   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
=========================================
  Files           11        11           
  Lines          576       576           
=========================================
  Hits           576       576           

Flag	Coverage Δ
unittests	100.00% <ø> (ø)

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[jamienoss]

@amitschang I took a quick look around as it seemed odd that nothing was stated in the change log (at least that looked obvious). I did find this ray-project/ray#40802 and seems like there's instability around it given the multiple reverts.

Anyhoo, this PR looks good to me, I guess the only double-check is to ask whether similar try-excepts need altering that aren't directly tested - are there any?

[amitschang]

@jamienoss, cool thanks. I don't there there are anymore gotchas here - in the end I think we will deprecate the ray.data version of executor anyhow as it is really meant for another kind of workflow