[tune](deps): Bump mlflow from 1.13.1 to 1.14.0 in /python/requirements

[dependabot]

Bumps mlflow from 1.13.1 to 1.14.0.

Release notes

Sourced from mlflow's releases.

MLflow 1.14.0

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:

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)

Bug fixes and documentation updates:

• Enable autologging for TensorFlow estimators that extend tensorflow.compat.v1.estimator.Estimator (#4097, @mohamad-arabi)
• Fix for universal autolog configs overriding integration-specific configs (#4093, @dbczumar)
• Allow mlflow.models.infer_signature to handle dataframes containing pandas.api.extensions.ExtensionDtype (#4069, @caleboverman)
• Fix bug where mlflow_restore_run doesn't propagate the client parameter to mlflow_get_run (#4003, @yitao-li)
• Fix bug where scoring on served model fails when request data contains a string that looks like URL and pandas version is later than 1.1.0 (#3921, @Secbone)
• Fix bug causing mlflow_list_experiments to fail listing experiments with tags (#3942, @lorenzwalthert)
• Fix bug where metrics plots are computed from incorrect target values in scikit-learn autologging (#3993, @mtrencseni)
• Remove redundant / verbose Python event logging message in autologging (#3978, @dbczumar)
• Fix bug where mlflow_load_model doesn't load metadata associated to MLflow model flavor in R (#3872, @yitao-li)
• Fix mlflow.spark.log_model, mlflow.spark.load_model APIs on passthrough-enabled environments against ACL'd artifact locations (#3443, @smurching)

Small bug fixes and doc updates:

(#4102, #4101, #4096, #4091, #4067, #4059, #4016, #4054, #4052, #4051, #4038, #3992, #3990, #3981, #3949, #3948, #3937, #3834, #3906, #3774, #3916, #3907, #3938, #3929, #3900, #3902, #3899, #3901, #3891, #3889, @harupy; #4014, #4001, @dmatrix; #4028, #3957, @dbczumar; #3816, @lorenzwalthert; #3939, @pauldj54; #3740, @jkthompson; #4070, #3946, @jimmyxu-db; #3836, @t-henri; #3982, @neo-anderson; #3972, #3687, #3922, @eedeleon; #4044, @WeichenXu123; #4063, @yitao-li; #3976, @whiteh; #4110, @tomasatdatabricks; #4050, @apurva-koti; #4100, #4084, @wentinghu; #3947, @vperiyasamy; #4021, @trangevi; #3773, @ankan94; #4090, @jinzhang21; #3918, @danielfrg)

Changelog

Sourced from mlflow's changelog.

1.14.0 (2021-02-18)

MLflow 1.14.0 includes several major 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)

Bug fixes and documentation updates:

• Enable autologging for TensorFlow estimators that extend tensorflow.compat.v1.estimator.Estimator (#4097, @mohamad-arabi)
• Fix for universal autolog configs overriding integration-specific configs (#4093, @dbczumar)
• Allow mlflow.models.infer_signature to handle dataframes containing pandas.api.extensions.ExtensionDtype (#4069, @caleboverman)
• Fix bug where mlflow_restore_run doesn't propagate the client parameter to mlflow_get_run (#4003, @yitao-li)
• Fix bug where scoring on served model fails when request data contains a string that looks like URL and pandas version is later than 1.1.0 (#3921, @Secbone)
• Fix bug causing mlflow_list_experiments to fail listing experiments with tags (#3942, @lorenzwalthert)
• Fix bug where metrics plots are computed from incorrect target values in scikit-learn autologging (#3993, @mtrencseni)
• Remove redundant / verbose Python event logging message in autologging (#3978, @dbczumar)
• Fix bug where mlflow_load_model doesn't load metadata associated to MLflow model flavor in R (#3872, @yitao-li)
• Fix mlflow.spark.log_model, mlflow.spark.load_model APIs on passthrough-enabled environments against ACL'd artifact locations (#3443, @smurching)

Small bug fixes and doc updates (#4102, #4101, #4096, #4091, #4067, #4059, #4016, #4054, #4052, #4051, #4038, #3992, #3990, #3981, #3949, #3948, #3937, #3834, #3906, #3774, #3916, #3907, #3938, #3929, #3900, #3902, #3899, #3901, #3891, #3889, @harupy; #4014, #4001, @dmatrix; #4028, #3957, @dbczumar; #3816, @lorenzwalthert; #3939, @pauldj54; #3740, @jkthompson; #4070, #3946, @jimmyxu-db; #3836, @t-henri; #3982, @neo-anderson; #3972, #3687, #3922, @eedeleon; #4044, @WeichenXu123; #4063, @yitao-li; #3976, @whiteh; #4110, @tomasatdatabricks; #4050, @apurva-koti; #4100, #4084, @wentinghu; #3947, @vperiyasamy; #4021, @trangevi; #3773, @ankan94; #4090, @jinzhang21; #3918, @danielfrg)

Commits

• 9395672 Check DBFS FUSE availability before using it in mlflow.spark APIs (#4108) (#4...
• 772a110 Update MLflow version to 1.14.0 (#4111)
• 22b9826 SHAP Explainer Logging Integration (#3989)
• 7beb91d [Follow-up for #4101] Fix broken autologging example tests (#4102)
• f5825ac Support parsing df input with tensor schema and parsing tensor input with df ...
• c573aaa Add monkey patching for old and new tf.Estimator classes (#4097)
• 4ba469e Fix enable_test_mode_by_default_for_autologging_integrations (#4101)
• f7fbaa4 Added tensor input support for ONNX and gluon flavors. (#4041)
• b1e4023 Improve method to parse tf-serving json (#4099)
• 1a6c7c1 Don't allow schema creation for multiple unnamed TensorSpecs (#4100)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #7.