Bump mlflow from 2.12.1 to 2.13.0

[dependabot]

Bumps mlflow from 2.12.1 to 2.13.0.

Release notes

Sourced from mlflow's releases.

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)
• [Docs] Add documentation for the expanded code path inference feature (#11997, @​BenWilson2)
• [Docs] Add documentation guidelines for the predict_stream API (#11976, @​BenWilson2)
• [Docs] Add support for enhanced Documentation with the JFrog MLflow Plugin (#11426, @​yonarbel)

Small bug fixes and documentation updates:

#12052, #12053, #12022, #12029, #12024, #11992, #12004, #11958, #11957, #11850, #11938, #11924, #11922, #11920, #11820, #11822, #11798, @​serena-ruan; #12054, #12051, #12045, #12043, #11987, #11888, #11876, #11913, #11868, @​sunishsheth2009; #12049, #12046, #12037, #11831, @​dbczumar; #12047, #12038, #12020, #12021, #11970, #11968, #11967, #11965, #11963, #11941, #11956, #11953, #11934, #11921, #11454, #11836, #11826, #11793, #11790, #11776, #11765, #11763, #11746, #11748, #11740, #11735, @​harupy; #12025, #12034, #12027, #11914, #11899, #11866, @​BenWilson2; #12026, #11991, #11979, #11964, #11939, #11894, @​daniellok-db; #11951, #11974, #11916, @​annzhang-db; #12015, #11931, #11627, @​jessechancy; #12014, #11917, @​prithvikannan; #12012, @​AveshCSingh; #12001, @​yunpark93; #11984, #11983, #11977, #11977, #11949, @​edwardfeng-db; #11973, @​bbqiu; #11902, #11835, #11775, @​B-Step62; #11845, @​lababidi

MLflow 2.12.2 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 2 minor releases.

Features:

• [Models] Add an environment configuration flag to enable raising an exception instead of a warning for failures in model dependency inference (#11903, @​BenWilson2)
• [Models] Add support for the llm/v1/embeddings task in the Transformers flavor to unify the input and output structures for embedding models (#11795, @​B-Step62)

... (truncated)

Changelog

Sourced from mlflow's changelog.

2.13.0 (2024-05-20)

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)
• [Docs] Add documentation for the expanded code path inference feature (#11997, @​BenWilson2)
• [Docs] Add documentation guidelines for the predict_stream API (#11976, @​BenWilson2)
• [Docs] Add support for enhanced Documentation with the JFrog MLflow Plugin (#11426, @​yonarbel)

Small bug fixes and documentation updates:

#12052, #12053, #12022, #12029, #12024, #11992, #12004, #11958, #11957, #11850, #11938, #11924, #11922, #11920, #11820, #11822, #11798, @​serena-ruan; #12054, #12051, #12045, #12043, #11987, #11888, #11876, #11913, #11868, @​sunishsheth2009; #12049, #12046, #12037, #11831, @​dbczumar; #12047, #12038, #12020, #12021, #11970, #11968, #11967, #11965, #11963, #11941, #11956, #11953, #11934, #11921, #11454, #11836, #11826, #11793, #11790, #11776, #11765, #11763, #11746, #11748, #11740, #11735, @​harupy; #12025, #12034, #12027, #11914, #11899, #11866, @​BenWilson2; #12026, #11991, #11979, #11964, #11939, #11894, @​daniellok-db; #11951, #11974, #11916, @​annzhang-db; #12015, #11931, #11627, @​jessechancy; #12014, #11917, @​prithvikannan; #12012, @​AveshCSingh; #12001, @​yunpark93; #11984, #11983, #11977, #11977, #11949, @​edwardfeng-db; #11973, @​bbqiu; #11902, #11835, #11775, @​B-Step62; #11845, @​lababidi

2.12.2 (2024-05-08)

MLflow 2.12.2 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 2 minor releases.

Features:

... (truncated)

Commits

• 1b604e4 Run python3 dev/update_mlflow_versions.py pre-release ... (#12056)
• 43bddf7 [test] run test_langchain_tracer in cross_version tests (#12052)
• 22a5b53 [MLflow] Fix langchain tests (#12054)
• 9b36854 Rename function and fix iter (#12053)
• 3779805 [MLflow] Renaming vector search index to retriever (#12051)
• f7c420f LangChain tracing: only end spans if the trace is still active (#12049)
• b53cd2e [MLflow] Update mlflow langchain pyfunc.load_model to correctly write tags to...
• 2c7906a Bug fix: TraceStatus hydration from proto (#12044)
• fa6e185 [MLflow] Update mlflow langchain metadata to write dependencies_schemas (#12045)
• e18ba3f Run protos workflow if we change dependencies (#12047)
• Additional commits viewable in compare view

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[dependabot]

A newer version of mlflow exists, but since this PR has been edited by someone other than Dependabot I haven't updated it. You'll get a PR for the updated version as normal once this PR is merged.