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MLflow 2.13.0

· 3 min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

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)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.