MLflow 1.12.1 is a patch release containing bug fixes and small changes:

MLflow 1.12.1 is a patch release containing bug fixes and small changes:

• Fix run_link for cross-workspace model versions (#3681, @sueann)
• Remove hard dependency on matplotlib for sklearn autologging (#3703, @dbczumar)
• Do not disable existing loggers when initializing alembic (#3653, @arthury1n)

We are happy to announce the availability of MLflow 1.12.0!

In addition to bug and documentation fixes, MLflow 1.12.0 includes several major features and improvements, in particular a number of improvements to MLflow's Pytorch integrations and autologging:

PyTorch

• mlflow.pytorch.log_model, mlflow.pytorch.load_model now support logging/loading TorchScript models (#3557, @shrinath-suresh)
• mlflow.pytorch.log_model supports passing requirements_file & extra_files arguments to log additional artifacts along with a model (#3436, @shrinath-suresh)

Autologging

• Add universal mlflow.autolog which enables autologging for all supported integrations (#3561, #3590, @andrewnitu)
• Add mlflow.pytorch.autolog API for automatic logging of metrics, params, and models from Pytorch Lightning training (#3601, @shrinath-suresh, #3636, @karthik-77). This API is also enabled by mlflow.autolog.
• Scikit-learn, XGBoost, and LightGBM autologging now support logging model signatures and input examples (#3386, #3403, #3449, @andrewnitu)
• mlflow.sklearn.autolog now supports logging metrics (e.g. accuracy) and plots (e.g. confusion matrix heat map) (#3423, #3327, @willzhan-db, @harupy)

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

We are happy to announce the availability of MLflow 1.11.0!

In addition to bug and documentation fixes, MLflow 1.11.0 includes the following features and improvements:

• New mlflow.sklearn.autolog() API for automatic logging of metrics, params, and models from scikit-learn model training (#3287, @harupy; #3323, #3358 @dbczumar)
• Registered model & model version creation APIs now support specifying an initial description (#3271, @sueann)
• The R mlflow_log_model and mlflow_load_model APIs now support XGBoost models (#3085, @lorenzwalthert)
• New mlflow.list_run_infos fluent API for listing run metadata (#3183, @trangevi)
• Added section for visualizing and comparing model schemas to model version and model-version-comparison UIs (#3209, @zhidongqu-db)
• Enhanced support for using the model registry across Databricks workspaces: support for registering models to a Databricks workspace from outside the workspace (#3119, @sueann), tracking run-lineage of these models (#3128, #3164, @ankitmathur-db; #3187, @harupy), and calling mlflow.<flavor>.load_model against remote Databricks model registries (#3330, @sueann)
• UI support for setting/deleting registered model and model version tags (#3187, @harupy)
• UI support for archiving existing staging/production versions of a model when transitioning a new model version to staging/production (#3134, @harupy)

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

We are happy to announce the availability of MLflow 1.10.0!

In addition to bug and documentation fixes, MLflow 1.10.0 includes the following features and improvements:

• MlflowClient.transition_model_version_stage now supports an archive_existing_versions argument for archiving existing staging or production model versions when transitioning a new model version to staging or production (#3095, @harupy)
• Added set_registry_uri, get_registry_uri APIs. Setting the model registry URI causes fluent APIs like mlflow.register_model to communicate with the model registry at the specified URI (#3072, @sueann)
• Added paginated MlflowClient.search_registered_models API (#2939, #3023, #3027 @ankitmathur-db; #2966, @mparkhe)
• Added syntax highlighting when viewing text files (YAML etc) in the MLflow runs UI (#3041, @harupy)

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

MLflow 1.9.1 is a patch release containing a number of bug-fixes and improvements:

• Fixes AttributeError when pickling an instance of the Python MlflowClient class (#2955, @Polyphenolx)
• Fixes bug that prevented updating model-version descriptions in the model registry UI (#2969, @AnastasiaKol)
• Fixes bug where credentials were not properly propagated to artifact CLI commands when logging artifacts from Java to the DatabricksArtifactRepository (#3001, @dbczumar)
• Removes use of new Pandas API in new MLflow model-schema functionality, so that it can be used with older Pandas versions (#2988, @aarondav)

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

We are happy to announce the availability of MLflow 1.9.0!

In addition to bug and documentation fixes, MLflow 1.9.0 includes the following major features and improvements:

• log_model and save_model APIs now support saving model signatures (the model's input and output schema) and example input along with the model itself (#2698, #2775, @tomasatdatabricks). Model signatures are used to reorder and validate input fields when scoring/serving models using the pyfunc flavor, mlflow models CLI commands, or mlflow.pyfunc.spark_udf (#2920, @tomasatdatabricks and @aarondav)
• Introduce fastai model persistence and autologging APIs under mlflow.fastai (#2619, #2689 @antoniomdk)
• Add pluggable mlflow.deployments API and CLI for deploying models to custom serving tools, e.g. RedisAI (#2327, @hhsecond)
• Add plugin interface for executing MLflow projects against custom backends (#2566, @jdlesage)
• Enable viewing PDFs logged as artifacts from the runs UI (#2859, @ankmathur96)
• Significant performance and scalability improvements to metric comparison and scatter plots in the UI (#2447, @mjlbach)

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

We are happy to announce the availability of MLflow 1.8.0!

In addition to bug and documentation fixes, MLflow 1.8.0 includes the following major features and improvements:

• Added mlflow.azureml.deploy API for deploying MLflow models to AzureML (#2375 @csteegz, #2711, @akshaya-a)
• Added mlflow.spacy module with support for logging and loading spaCy models (#2242, @arocketman)
• Added ability to compare source runs associated with model versions from the registered model UI (#2537, @juntai-zheng)
• MLflow metrics UI plots now scale to rendering thousands of points using scattergl (#2447, @mjlbach)

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

MLflow 1.7.2 is a patch release containing a minor change:

• Pin alembic version to 1.4.1 or below to prevent pep517-related installation errors (#2612, @smurching)

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

MLflow 1.7.1 is a patch release containing bug fixes and small changes:

• Remove usage of Nonnull annotations and findbugs dependency in Java package.
• Add version upper bound (<=1.3.13) to sqlalchemy dependency in Python package
• Documentation fixes

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

We are happy to announce the availability of MLflow 1.7.0!

In addition to bug and documentation fixes, MLflow 1.7.0 includes the following major changes:

• Support for Python 2 is deprecated and will be dropped in a future release. At that point, existing Python 2 workflows that use MLflow will continue to work without modification, but Python 2 users will no longer get access to the latest MLflow features and bugfixes.

• Breaking changes to Model Registry REST APIs

  • Model Registry REST APIs have been updated to be more consistent with the other MLflow APIs and are intended to be stable until the next major version.
  • Python and Java client APIs for Model Registry are backward compatible and have been updated to use the new REST APIs.

In addition several UI and and backend features were added in version 1.7.0. For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.