mlflow 1.13

MLflow 1.13.0

on Python PyPI

We are happy to announce the availability of MLflow 1.13.0!

Note: The MLflow R package for 1.13.0 is not yet available on CRAN because CRAN's submission system will be offline until January 4.

In addition to bug and documentation fixes, MLflow 1.13.0 includes the following features and improvements:

Features:

New fluent APIs for logging in-memory objects as artifacts:

• Add mlflow.log_text which logs text as an artifact (#3678, @harupy)
• Add mlflow.log_dict which logs a dictionary as an artifact (#3685, @harupy)
• Add mlflow.log_figure which logs a figure object as an artifact (#3707, @harupy)
• Add mlflow.log_image which logs an image object as an artifact (#3728, @harupy)

UI updates / fixes:

• Add model version link in compact experiment table view
• Add logged/registered model links in experiment runs page view
• Enhance artifact viewer for MLflow models
• Model registry UI settings are now persisted across browser sessions
• Add model version description field to model version table

(all merged in #3867, @smurching)

Autologging enhancements:

• Improve robustness of autologging integrations to exceptions (#3682, #3815, dbczumar; #3860, @mohamad-arabi; #3854, #3855, #3861, @harupy)
• Add disable configuration option for autologging (#3682, #3815, dbczumar; #3838, @mohamad-arabi; #3854, #3855, #3861, @harupy)
• Add exclusive configuration option for autologging (#3851, @apurva-koti; #3869, @dbczumar)
• Add log_models configuration option for autologging (#3663, @mohamad-arabi)
• Set tags on autologged runs for easy identification (and add tags to start_run) (#3847, @dbczumar)

More features and improvements:

• Allow Keras models to be saved with SavedModel format (#3552, @skylarbpayne)
• Add support for statsmodels flavor (#3304, @olbapjose)
• Add support for nested-run in mlflow R client (#3765, @yitao-li)
• Deploying a model using mlflow.azureml.deploy now integrates better with the AzureML tracking/registry. (#3419, @trangevi)
• Update schema enforcement to handle integers with missing values (#3798, @tomasatdatabricks)

Bug fixes and documentation updates:

• When running an MLflow Project on Databricks, the version of MLflow installed on the Databricks cluster will now match the version used to run the Project (#3880, @FlorisHoogenboom)
• Fix bug where metrics are not logged for single-epoch tf.keras training sessions (#3853, @dbczumar)
• Reject boolean types when logging MLflow metrics (#3822, @HCoban)
• Fix alignment of Keras / tf.Keras metric history entries when initial_epoch is different from zero. (#3575, @garciparedes)
• Fix bugs in autologging integrations for newer versions of TensorFlow and Keras (#3735, @dbczumar)
• Drop global filterwwarnings module at import time (#3621, @jogo)
• Fix bug that caused preexisting Python loggers to be disabled when using MLflow with the SQLAlchemyStore (#3653, @arthury1n)
• Fix h5py library incompatibility for exported Keras models (#3667, @tomasatdatabricks)

Small changes, bug fixes and doc updates (#3887, #3882, #3845, #3833, #3830, #3828, #3826, #3825, #3800, #3809, #3807, #3786, #3794, #3731, #3776, #3760, #3771, #3754, #3750, #3749, #3747, #3736, #3701, #3699, #3698, #3658, #3675, @harupy; #3723, @mohamad-arabi; #3650, #3655, @shrinath-suresh; #3850, #3753, #3725, @dmatrix; ##3867, #3670, #3664, @smurching; #3681, @sueann; #3619, @andrewnitu; #3837, @javierluraschi; #3721, @szczeles; #3653, @arthury1n; #3883, #3874, #3870, #3877, #3878, #3815, #3859, #3844, #3703, @dbczumar; #3768, @wentinghu; #3784, @HCoban; #3643, #3649, @arjundc-db; #3864, @AveshCSingh, #3756, @yitao-li)