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· One min read
MLflow maintainers

We are happy to announce the availability of MLflow 1.19.0!

In addition to bug and documentation fixes, MLflow 1.19.0 includes the following features and improvements:

  • Add support for plotting per-class feature importance computed on linear boosters in XGBoost autologging (#4523, @dbczumar)
  • Add mlflow_create_registered_model and mlflow_delete_registered_model for R to create/delete registered models.
  • Add support for setting tags while resuming a run (#4497, @dbczumar)
  • MLflow UI updates (#4490, @sunishsheth2009)
    • Add framework for internationalization support.
    • Move metric columns before parameter and tag columns in the runs table.
    • Change the display format of run start time to elapsed time (e.g. 3 minutes ago) from timestamp (e.g. 2021-07-14 14:02:10) in the runs table.

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

· One min read
MLflow maintainers

We are happy to announce the availability of MLflow 1.18.0!

In addition to bug and documentation fixes, MLflow 1.18.0 includes the following features and improvements:

  • Autologging performance improvements for XGBoost, LightGBM, and scikit-learn (#4416, #4473, @dbczumar)
  • Add new PaddlePaddle flavor to MLflow Models (#4406, #4439, @jinminhao)
  • Introduce paginated ListExperiments API (#3881, @wamartin-aml)
  • Include Runtime version for MLflow Models logged on Databricks (#4421, @stevenchen-db)
  • MLflow Models now log dependencies in pip requirements.txt format, in addition to existing conda format (#4409, #4422, @stevenchen-db)
  • Add support for limiting the number child runs created by autologging for scikit-learn hyperparameter search models (#4382, @mohamad-arabi)
  • Improve artifact upload / download performance on Databricks (#4260, @dbczumar)
  • Migrate all model dependencies from conda to "pip" section (#4393, @WeichenXu123)

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

· One min read
MLflow maintainers

We are happy to announce the availability of MLflow 1.17.0!

In addition to bug and documentation fixes, MLflow 1.17.0 includes the following features and improvements:

  • Add support for hyperparameter-tuning models to mlflow.pyspark.ml.autolog() (#4270, @WeichenXu123)

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

· One min read
MLflow maintainers

We are happy to announce the availability of MLflow 1.16.0!

In addition to bug and documentation fixes, MLflow 1.16.0 includes the following features and improvements:

  • Add mlflow.pyspark.ml.autolog() API for autologging of pyspark.ml estimators (#4228, @WeichenXu123)
  • Add mlflow.catboost.log_model, mlflow.catboost.save_model, mlflow.catboost.load_model APIs for CatBoost model persistence (#2417, @harupy)
  • Enable mlflow.pyfunc.spark_udf to use column names from model signature by default (#4236, @Loquats)
  • Add datetime data type for model signatures (#4241, @vperiyasamy)
  • Add mlflow.sklearn.eval_and_log_metrics API that computes and logs metrics for the given scikit-learn model and labeled dataset. (#4218, @alkispoly-db)

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

· One min read
MLflow maintainers

We are happy to announce the availability of MLflow 1.15.0!

In addition to bug and documentation fixes, MLflow 1.15.0 includes the following features and improvements:

  • Add silent=False option to all autologging APIs, to allow suppressing MLflow warnings and logging statements during autologging setup and training (#4173, @dbczumar)
  • Add disable_for_unsupported_versions=False option to all autologging APIs, to disable autologging for versions of ML frameworks that have not been explicitly tested against the current version of the MLflow client (#4119, @WeichenXu123)

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

· 2 min read
MLflow maintainers

We are happy to announce the availability of MLflow 1.14.0!

In addition to bug and documentation fixes, MLflow 1.14.0 includes the following features and improvements:

  • MLflow's model inference APIs (mlflow.pyfunc.predict), built-in model serving tools (mlflow models serve), and model signatures now support tensor inputs. In particular, MLflow now provides built-in support for scoring PyTorch, TensorFlow, Keras, ONNX, and Gluon models with tensor inputs. For more information, see https://mlflow.org/docs/latest/models.html#deploy-mlflow-models (#3808, #3894, #4084, #4068 @wentinghu; #4041 @tomasatdatabricks, #4099, @arjundc-db)
  • Add new mlflow.shap.log_explainer, mlflow.shap.load_explainer APIs for logging and loading shap.Explainer instances (#3989, @vivekchettiar)
  • The MLflow Python client is now available with a reduced dependency set via the mlflow-skinny PyPI package (#4049, @eedeleon)
  • Add new RequestHeaderProvider plugin interface for passing custom request headers with REST API requests made by the MLflow Python client (#4042, @jimmyxu-db)
  • mlflow.keras.log_model now saves models in the TensorFlow SavedModel format by default instead of the older Keras H5 format (#4043, @harupy)
  • mlflow_log_model now supports logging MLeap models in R (#3819, @yitao-li)
  • Add mlflow.pytorch.log_state_dict, mlflow.pytorch.load_state_dict for logging and loading PyTorch state dicts (#3705, @shrinath-suresh)
  • mlflow gc can now garbage-collect artifacts stored in S3 (#3958, @sklingel)

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

· One min read
MLflow maintainers

We are happy to announce the availability of MLflow 1.13.1!

MLflow 1.13.1 is a patch release containing bug fixes and small changes:

  • Fix bug causing Spark autologging to ignore configuration options specified by mlflow.autolog() (#3917, @dbczumar)
  • Fix bugs causing metrics to be dropped during TensorFlow autologging (#3913, #3914, @dbczumar)
  • Fix incorrect value of optimizer name parameter in autologging PyTorch Lightning (#3901, @harupy)
  • Fix model registry database allow_null_for_run_id migration failure affecting MySQL databases (#3836, @t-henri)
  • Fix failure in transition_model_version_stage when uncanonical stage name is passed (#3929, @harupy)
  • Fix an undefined variable error causing AzureML model deployment to fail (#3922, @eedeleon)
  • Reclassify scikit-learn as a pip dependency in MLflow Model conda environments (#3896, @harupy)
  • Fix experiment view crash and artifact view inconsistency caused by artifact URIs with redundant slashes (#3928, @dbczumar)

· 2 min read
MLflow maintainers

We are happy to announce the availability of MLflow 1.13.0!

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

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

(#3867, @smurching)

Autologging enhancements:​

More features and improvements:​

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

· One min read
MLflow maintainers

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)