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MLflow v1.13 Release Notes

Release Date: 2020-12-22 // over 3 years ago

  • MLflow 1.13 includes several major 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 (#3867, @smurching):

    • ➕ 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

    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)