Autogluon-cloud aims to provide user tools to train, fine-tune and deploy AutoGluon backed models on the cloud. With just a few lines of codes, users could train a model and perform inference on the cloud without worrying about MLOps details such as resource management
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Update ray[default] requirement from <2.11,>=2.10.0 to >=2.10.0,<2.40 #159
Basic HTTP deployments will now return โInternal Server Errorโ instead of a traceback to match FastAPI behavior (ray-project/ray#48491)
Fixed an issue where high values of max_ongoing_requests couldnโt be reached due to an interaction with coreโs max_concurrency (ray-project/ray#48274)
Fixed an edge case where pending requests were not canceled properly (ray-project/ray#47873)
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Updates the requirements on ray[default] to permit the latest version.
Release notes
Sourced from ray[default]'s releases.
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Commits
5a6c335
cherrypick #48563 and #48527 (#48574)06e23b0
2.39 version change (#48573)62d7f4b
[RLlib] New API stack on by default for PPO. (#48284)c7263e4
Make it possible to use Ray distributed debugger without setting RAY_DEBUG (#...f6a5423
Revert "[core] Add source location to status" (#48517)e162165
[RLlib] Cleanupevaluation
folder. (#48493)9b92557
[Data] FixOutputBlockBuffer
to avoid repeatedly copying remainder block (#...0a779ad
[Data] Bump min version of PyArrow from 6.0 to 9.0 (#47040)cf85018
Revert "[Observability] Added object refs Task is dependent on to `TaskInfoEn...056d596
[serve] move handle options to its own file (#48454)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
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