MLflow 1.13.0
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 wheninitial_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)