#12334: Support for Python 3.13 (beta1 at the time of writing).
Bug Fixes
#12120: Fix [PermissionError]{.title-ref} crashes arising from directories which are not selected on the command-line.
#12191: Keyboard interrupts and system exits are now properly handled during the test collection.
#12300: Fixed handling of 'Function not implemented' error under squashfuse_ll, which is a different way to say that the mountpoint is read-only.
#12308: Fix a regression in pytest 8.2.0 where the permissions of automatically-created .pytest_cache directories became rwx------ instead of the expected rwxr-xr-x.
Trivial/Internal Changes
#12333: pytest releases are now attested using the recent Artifact Attestation support from GitHub, allowing users to verify the provenance of pytest's sdist and wheel artifacts.
Add support for using GPUs with map_groups (#45305)
Add support for using actors with map_groups (#45310)
💫 Enhancements:
Refine exception handling from arrow data conversion (#45294)
🔨 Fixes:
Fix Ray databricks UC reader with dynamic Databricks notebook scope token (#45153)
Fix bug where you can't return objects and array from UDF (#45287 )
Fix bug where map_groups triggers execution during input validation (#45314)
Ray Tune
🔨 Fixes:
[tune] Fix PB2 scheduler error resulting from trying to sort by Trial objects (#45161)
Ray Serve
🔨 Fixes:
Log application unhealthy errors at error level instead of warning level (#45211)
RLLib
💫 Enhancements:
Examples and tuned_examples learning test for new API stack are now “self-executable” (don’t require a third-party script anymore to run them). + WandB support. (#45023)
🔨 Fixes:
Fix result dict “spam” (duplicate, deprecated keys, e.g. “sampler_results” dumped into top level). (#45330)
📖 Documentation:
Add example for training with fractional GPUs on new API stack. (#45379)
Cleanup examples folder and remove deprecated sub directories. (#45327)
Ray Core
💫 Enhancements:
[Logs] Add runtime env started logs to job driver (#45255)
ray.util.collective support torch.bfloat16 (#39845)
[Core] Better propagate node death information (#45128)
🔨 Fixes:
[Core] Fix worker process leaks after job finishes (#44214)
MLflow 2.13.1 is a patch release that includes several bug fixes and integration improvements to existing features. New features that are introduced in this patch release are intended to provide a foundation to further major features that will be released in the next release.
Features:
[MLflow] Add mlflow[langchain] extra that installs recommended versions of langchain with MLflow (#12182, @sunishsheth2009)
[Tracking] Adding the ability to override the model_config in langchain flavor if loaded as pyfunc (#12085, @sunishsheth2009)
[Model Registry] Automatically detect if Presigned URLs are required for Unity Catalog (#12177, @artjen)
Bug fixes:
[Tracking] Use getUserLocalTempDir and getUserNFSTempDir to replace getReplLocalTempDir and getReplNFSTempDir in databricks runtime (#12105, @WeichenXu123)
[Model] Updating chat model to take default input_example and predict to accept json during inference (#12115, @sunishsheth2009)
[Tracking] Automatically call load_context when inferring signature in pyfunc (#12099, @sunishsheth2009)
v2.13.0
MLflow 2.13.0 includes several major features and improvements
With this release, we're happy to introduce several features that enhance the usability of MLflow broadly across a range of use cases.
Major Features and Improvements:
Streamable Python Models: The newly introduced predict_stream API for Python Models allows for custom model implementations that support the return of a generator object, permitting full customization for GenAI applications.
Enhanced Code Dependency Inference: A new feature for automatically inferrring code dependencies based on detected dependencies within a model's implementation. As a supplement to the code_paths parameter, the introduced infer_model_code_paths option when logging a model will determine which additional code modules are needed in order to ensure that your models can be loaded in isolation, deployed, and reliably stored.
Standardization of MLflow Deployment Server: Outputs from the Deployment Server's endpoints now conform to OpenAI's interfaces to provide a simpler integration with commonly used services.
Features:
[Deployments] Update the MLflow Deployment Server interfaces to be OpenAI compatible (#12003, @harupy)
[Deployments] Add Togetherai as a supported provider for the MLflow Deployments Server (#11557, @FotiosBistas)
[Models] Add predict_stream API support for Python Models (#11791, @WeichenXu123)
[Models] Enhance the capabilities of logging code dependencies for MLflow models (#11806, @WeichenXu123)
[Models] Add support for RunnableBinding models in LangChain (#11980, @serena-ruan)
[Model Registry / Databricks] Add support for renaming models registered to Unity Catalog (#11988, @artjen)
[Model Registry / Databricks] Improve the handling of searching for invalid components from Unity Catalog registered models (#11961, @artjen)
[Model Registry] Enhance retry logic and credential refresh to mitigate cloud provider token expiration failures when uploading or downloading artifacts (#11614, @artjen)
[Artifacts / Databricks] Add enhanced lineage tracking for models loaded from Unity Catalog (#11305, @shichengzhou-db)
[Tracking] Add resourcing metadata to Pyfunc models to aid in model serving environment configuration (#11832, @sunishsheth2009)
[Tracking] Enhance LangChain signature inference for models as code (#11855, @sunishsheth2009)
Bug fixes:
[Artifacts] Prohibit invalid configuration options for multi-part upload on AWS (#11975, @ian-ack-db)
[Model Registry] Enforce registered model metadata equality (#12013, @artjen)
[Models] Correct an issue with hasattr references in AttrDict usages (#11999, @BenWilson2)
Documentation updates:
[Docs] Simplify the main documentation landing page (#12017, @BenWilson2)
MLflow 2.13.1 is a patch release that includes several bug fixes and integration improvements to existing features. New features that are introduced in this patch release are intended to provide a foundation to further major features that will be released in the next release.
Features:
[MLflow] Add mlflow[langchain] extra that installs recommended versions of langchain with MLflow (#12182, @sunishsheth2009)
[Tracking] Adding the ability to override the model_config in langchain flavor if loaded as pyfunc (#12085, @sunishsheth2009)
[Model Registry] Automatically detect if Presigned URLs are required for Unity Catalog (#12177, @artjen)
Bug fixes:
[Tracking] Use getUserLocalTempDir and getUserNFSTempDir to replace getReplLocalTempDir and getReplNFSTempDir in databricks runtime (#12105, @WeichenXu123)
[Model] Updating chat model to take default input_example and predict to accept json during inference (#12115, @sunishsheth2009)
[Tracking] Automatically call load_context when inferring signature in pyfunc (#12099, @sunishsheth2009)
MLflow 2.13.0 includes several major features and improvements
With this release, we're happy to introduce several features that enhance the usability of MLflow broadly across a range of use cases.
Major Features and Improvements:
Streamable Python Models: The newly introduced predict_stream API for Python Models allows for custom model implementations that support the return of a generator object, permitting full customization for GenAI applications.
Enhanced Code Dependency Inference: A new feature for automatically inferrring code dependencies based on detected dependencies within a model's implementation. As a supplement to the code_paths parameter, the introduced infer_model_code_paths option when logging a model will determine which additional code modules are needed in order to ensure that your models can be loaded in isolation, deployed, and reliably stored.
Standardization of MLflow Deployment Server: Outputs from the Deployment Server's endpoints now conform to OpenAI's interfaces to provide a simpler integration with commonly used services.
Features:
[Deployments] Update the MLflow Deployment Server interfaces to be OpenAI compatible (#12003, @harupy)
[Deployments] Add Togetherai as a supported provider for the MLflow Deployments Server (#11557, @FotiosBistas)
[Models] Add predict_stream API support for Python Models (#11791, @WeichenXu123)
[Models] Enhance the capabilities of logging code dependencies for MLflow models (#11806, @WeichenXu123)
[Models] Add support for RunnableBinding models in LangChain (#11980, @serena-ruan)
[Model Registry / Databricks] Add support for renaming models registered to Unity Catalog (#11988, @artjen)
[Model Registry / Databricks] Improve the handling of searching for invalid components from Unity Catalog registered models (#11961, @artjen)
[Model Registry] Enhance retry logic and credential refresh to mitigate cloud provider token expiration failures when uploading or downloading artifacts (#11614, @artjen)
[Artifacts / Databricks] Add enhanced lineage tracking for models loaded from Unity Catalog (#11305, @shichengzhou-db)
[Tracking] Add resourcing metadata to Pyfunc models to aid in model serving environment configuration (#11832, @sunishsheth2009)
[Tracking] Enhance LangChain signature inference for models as code (#11855, @sunishsheth2009)
Bug fixes:
[Artifacts] Prohibit invalid configuration options for multi-part upload on AWS (#11975, @ian-ack-db)
... (truncated)
Commits
0c2975c Run python3 dev/update_mlflow_versions.py pre-release ... (#12184)
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Bumps the all-minor-and-patch-dependency-updates group with 5 updates:
8.2.08.2.10.4.20.4.72.20.02.23.00.9.30.9.42.12.12.13.1Updates
pytestfrom 8.2.0 to 8.2.1Release notes
Sourced from pytest's releases.
Commits
66ff8dfPrepare release version 8.2.13ffcfd1Merge pull request #12340 from pytest-dev/backport-12334-to-8.2.x0b28313[8.2.x] Add Python 3.13 (beta) supportf3dd93a[8.2.x] Attest package provenance (#12335)bb5a125[8.2.x] Spelling (#12331)f179bf2Merge pull request #12327 from pytest-dev/backport-12325-to-8.2.x2b671b5[8.2.x] cacheprovider: fix.pytest_cachenot being world-readable65ab7cbMerge pull request #12324 from pytest-dev/backport-12320-to-8.2.x4d5fb7dMerge pull request #12319 from pytest-dev/backport-12311-to-8.2.xcbe5996[8.2.x] changelog: document unittest 8.2 change as breakingUpdates
rufffrom 0.4.2 to 0.4.7Release notes
Sourced from ruff's releases.
... (truncated)
Changelog
Sourced from ruff's changelog.
... (truncated)
Commits
1ad5f9cBump version to v0.4.7 (#11646)e914bc3F401 sort bindings before adding to all (#11648)27f6f04[red-knot] initial (very incomplete) flow graph (#11624)d62a617red-knot: Don't refer toModuleinstances as IDs (#11649)16a926d[red-knot] infer int literal types (#11623)05566c6UpdateWho's Using Ruff?section to includeGodot(#11647)7ce17b7Add Vim and Kate setup guide forruff server(#11615)f9a6450Use char index rather than position for indent slice (#11645)8a25531red-knot: improve internal documentation inmodule.rs(#11638)9b6d2ceFix incorect placement of trailing stub function comments (#11632)Updates
ray[default]from 2.20.0 to 2.23.0Release notes
Sourced from ray[default]'s releases.
... (truncated)
Commits
a0947ea[ray release] version change for 2.23.0 (#45437)6157e3b[ci] remove old worker container test (#45422)566dd5e[ci][microcheck] manually add tests to microcheck (#45400)90fa289[release] mark chaos_torch_batch_inference_16_gpu_300gb_raw as non-stable (#4...3acce1cadded typehint overloads to accurately infer the return type for ray.… (#45033)e0ac723[ci][microcheck] include step id from all step job flavors (#45403)eed322a[RLlib] Fix test_actor_manager CI test. (#45411)86ae5e8[RLlib] Cleanup examples folder 11: Example for using fractional GPUs on the ...faab1ac[RLlib] Issue 44686: Upgrade allrllib_contribrequirements to tf2.11.1. (#...467b092[ci][microcheck] improve determine new tests (#45377)Updates
nbsphinxfrom 0.9.3 to 0.9.4Release notes
Sourced from nbsphinx's releases.
Changelog
Sourced from nbsphinx's changelog.
... (truncated)
Commits
e9cb489Release 0.9.4ae98e6bDOC: show the matplotlib widget backenddbbc0e5CI: stop ignoring PendingDeprecationWarning about Node.traverse()d30467fconstrain docutils >= 0.18.1260b6d2don't use deprecated/ignored rawsource for docutils.nodes.Text init5d0f4b8switch Node.traverse() to findall() to avoid deprecation warnings311c53bCI: Add "trusted publishing" workflowc5fbe13CI: check multiple Python and Sphinx versions34062ecCircleCI: exclude Sphinx versions with regression in LaTeX modefdc25d8include all items doc/ in sdistsUpdates
mlflowfrom 2.12.1 to 2.13.1Release notes
Sourced from mlflow's releases.
... (truncated)
Changelog
Sourced from mlflow's changelog.
... (truncated)
Commits
0c2975cRunpython3 dev/update_mlflow_versions.py pre-release ...(#12184)c710be3fix lint1f52d7b[MLflow] Adding recommended versions of langchain in langchain extra for MLfl...c5e3b03Add ENABLE_MLFLOW_TRACING to control tracing in serving (#12180)debd95eAvoid showing repeated warnings for tracing default experiment usage (#12175)bc819e6doc: Fix "Unrecognized content type parameters: ." (#12142)6bdf3e7Add more examples for fluent tracing APIs (#12171)18ff44aAllow Relative Paths in Mlflow Log Model (#12160)fe1e47dAutomatically detect if Presigned URLs are required for Unity Catalog (#12177)cacaefeAdded a UC uri check to flavor_backend_registry (#11990)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
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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