[tune](deps): Bump mlflow from 1.19.0 to 1.20.2 in /python/requirements/tune

[dependabot]

Bumps mlflow from 1.19.0 to 1.20.2.

Release notes

Sourced from mlflow's releases.

MLflow 1.20.2 is a patch release containing the following features and bug fixes:

Features:

• Enabled auto dependency inference in spark flavor in autologging (#4759, @​harupy)

Bug fixes and documentation updates:

• Increased MLflow client HTTP request timeout from 10s to 120s (#4764, @​jinzhang21)
• Fixed autologging compatibility bugs with TensorFlow and Keras version 2.6.0 (#4766, @​dbczumar)

Small bug fixes and doc updates (#4770, @​WeichenXu123)

MLflow 1.20.1

Note: The MLflow R package for 1.20.1 is not yet available but will be in a week because CRAN's submission system will be offline until September 1st.

MLflow 1.20.1 is a patch release for the MLflow Python and R packages containing the following bug fixes:

• Avoid calling importlib_metadata.packages_distributions upon mlflow.utils.requirements_utils import (#4741, @​dbczumar)
• Avoid depending on importlib_metadata==4.7.0 (#4740, @​dbczumar)

MLflow 1.20.0

Note: The MLflow R package for 1.20.0 is not yet available but will be in a week because CRAN's submission system will be offline until September 1st.

MLflow 1.20.0 includes several major features and improvements:

Features:

• Autologging for scikit-learn now records post training metrics when scikit-learn evaluation APIs, such as sklearn.metrics.mean_squared_error, are called (#4491, #4628 #4638, @​WeichenXu123)
• Autologging for PySpark ML now records post training metrics when model evaluation APIs, such as Evaluator.evaluate(), are called (#4686, @​WeichenXu123)
• Add pip_requirements and extra_pip_requirements to mlflow.*.log_model and mlflow.*.save_model for directly specifying the pip requirements of the model to log / save (#4519, #4577, #4602, @​harupy)
• Added stdMetrics entries to the training metrics recorded during PySpark CrossValidator autologging (#4672, @​WeichenXu123)
• MLflow UI updates:
  1. Improved scalability of the parallel coordinates plot for run performance comparison,
  2. Added support for filtering runs based on their start time on the experiment page,
  3. Added a dropdown for runs table column sorting on the experiment page,
  4. Upgraded the AG Grid plugin, which is used for runs table loading on the experiment page, to version 25.0.0,
  5. Fixed a bug on the experiment page that caused the metrics section of the runs table to collapse when selecting columns from other table sections (#4712, @​dbczumar)
• Added support for distributed execution to autologging for PyTorch Lightning (#4717, @​dbczumar)
• Expanded R support for Model Registry functionality (#4527, @​bramrodenburg)
• Added model scoring server support for defining custom prediction response wrappers (#4611, @​Ark-kun)
• mlflow.*.log_model and mlflow.*.save_model now automatically infer the pip requirements of the model to log / save based on the current software environment (#4518, @​harupy)
• Introduced support for running Sagemaker Batch Transform jobs with MLflow Models (#4410, #4589, @​YQ-Wang)

Bug fixes and documentation updates:

• Deprecate requirements_file argument for mlflow.*.save_model and mlflow.*.log_model (#4620, @​harupy)
• set nextPageToken to null (#4729, @​harupy)
• Fix a bug in MLflow UI where the pagination token for run search is not refreshed when switching experiments (#4709, @​harupy)
• Fix a bug in the model scoring server that rejected requests specifying a valid Content-Type header with the charset parameter (#4609, @​Ark-kun)

... (truncated)

Changelog

Sourced from mlflow's changelog.

1.20.2 (2021-09-03)

MLflow 1.20.2 is a patch release containing the following features and bug fixes:

Features:

• Enabled auto dependency inference in spark flavor in autologging (#4759, @​harupy)

Bug fixes and documentation updates:

• Increased MLflow client HTTP request timeout from 10s to 120s (#4764, @​jinzhang21)
• Fixed autologging compatibility bugs with TensorFlow and Keras version 2.6.0 (#4766, @​dbczumar)

Small bug fixes and doc updates (#4770, @​WeichenXu123)

1.20.1 (2021-08-26)

MLflow 1.20.1 is a patch release containing the following bug fixes:

• Avoid calling importlib_metadata.packages_distributions upon mlflow.utils.requirements_utils import (#4741, @​dbczumar)
• Avoid depending on importlib_metadata==4.7.0 (#4740, @​dbczumar)

1.20.0 (2021-08-25)

MLflow 1.20.0 includes several major features and improvements:

Features:

• Autologging for scikit-learn now records post training metrics when scikit-learn evaluation APIs, such as sklearn.metrics.mean_squared_error, are called (#4491, #4628 #4638, @​WeichenXu123)
• Autologging for PySpark ML now records post training metrics when model evaluation APIs, such as Evaluator.evaluate(), are called (#4686, @​WeichenXu123)
• Add pip_requirements and extra_pip_requirements to mlflow.*.log_model and mlflow.*.save_model for directly specifying the pip requirements of the model to log / save (#4519, #4577, #4602, @​harupy)
• Added stdMetrics entries to the training metrics recorded during PySpark CrossValidator autologging (#4672, @​WeichenXu123)
• MLflow UI updates:
  1. Improved scalability of the parallel coordinates plot for run performance comparison,
  2. Added support for filtering runs based on their start time on the experiment page,
  3. Added a dropdown for runs table column sorting on the experiment page,
  4. Upgraded the AG Grid plugin, which is used for runs table loading on the experiment page, to version 25.0.0,
  5. Fixed a bug on the experiment page that caused the metrics section of the runs table to collapse when selecting columns from other table sections (#4712, @​dbczumar)
• Added support for distributed execution to autologging for PyTorch Lightning (#4717, @​dbczumar)
• Expanded R support for Model Registry functionality (#4527, @​bramrodenburg)
• Added model scoring server support for defining custom prediction response wrappers (#4611, @​Ark-kun)
• mlflow.*.log_model and mlflow.*.save_model now automatically infer the pip requirements of the model to log / save based on the current software environment (#4518, @​harupy)
• Introduced support for running Sagemaker Batch Transform jobs with MLflow Models (#4410, #4589, @​YQ-Wang)

Bug fixes and documentation updates:

• Deprecate requirements_file argument for mlflow.*.save_model and mlflow.*.log_model (#4620, @​harupy)
• set nextPageToken to null (#4729, @​harupy)
• Fix a bug in MLflow UI where the pagination token for run search is not refreshed when switching experiments (#4709, @​harupy)
• Fix a bug in the model scoring server that rejected requests specifying a valid Content-Type header with the charset parameter (#4609, @​Ark-kun)

... (truncated)

Commits

• 36869c0 Update MLflow version to 1.20.2 (#4775)
• 7ad4c22 [ALL TESTS] Update (#4774)
• f47e3ec Fix autologging compatibility with Keras >= 2.6.0 (#4766)
• 287b16b Update spark support version to be 3.2 (#4770)
• cbe4b39 Enable auto dependency inference in spark flavor (#4759)
• 940f966 Bump java version to 1.20.1 (#4765)
• 0787378 Increase HTTP timeout to 90s. Disabled cloud storage HTTP timeout. (#4764)
• 97b5bfe [ALL TESTS] Update (#4742)
• 627b7e7 Avoid calling packages_distributions upon requirements_utils import (#4741)
• 27d2f98 Specify != 4.7.0 (#4740)
• Additional commits viewable in compare view

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