[tune](deps): Bump mlflow from 1.21.0 to 2.2.1 in /python/requirements/ml

[dependabot]

Bumps mlflow from 1.21.0 to 2.2.1.

Release notes

Sourced from mlflow's releases.

MLflow 2.2.1 is a patch release containing the following bug fixes:

• [Model Registry] Fix a bug that caused too many results to be requested by default when calling MlflowClient.search_model_versions() (#7935, @​dbczumar)

MLflow 2.2.0 includes several major features and improvements

Features:

• [Recipes] Add support for score calibration to the classification recipe (#7744, @​sunishsheth2009)
• [Recipes] Add automatic label encoding to the classification recipe (#7711, @​sunishsheth2009)
• [Recipes] Support custom data splitting logic in the classification and regression recipes (#7815, #7588, @​sunishsheth2009)
• [Recipes] Introduce customizable MLflow Run name prefixes to the classification and regression recipes (#7746, @​kamalesh0406; #7763, @​sunishsheth2009)
• [UI] Add a new Chart View to the MLflow Experiment Page for model performance insights (#7864, @​hubertzub-db, @​apurva-koti, @​prithvikannan, @​ridhimag11, @​sunishseth2009, @​dbczumar)
• [UI] Modernize and improve parallel coordinates chart for model tuning (#7864, @​hubertzub-db, @​apurva-koti, @​prithvikannan, @​ridhimag11, @​sunishseth2009, @​dbczumar)
• [UI] Add typeahead suggestions to the MLflow Experiment Page search bar (#7864, @​hubertzub-db, @​apurva-koti, @​prithvikannan, @​ridhimag11, @​sunishseth2009, @​dbczumar)
• [UI] Improve performance of Experiments Sidebar for large numbers of experiments (#7804, @​jmahlik)
• [Tracking] Introduce autologging support for native PyTorch models (#7627, @​temporaer)
• [Tracking] Allow specifying model_format when autologging XGBoost models (#7781, @​guyrosin)
• [Tracking] Add MLFLOW_ARTIFACT_UPLOAD_DOWNLOAD_TIMEOUT environment variable to configure artifact operation timeouts (#7783, @​wamartin-aml)
• [Artifacts] Include Content-Type response headers for artifacts downloaded from mlflow server (#7827, @​bali0019)
• [Model Registry] Introduce the searchModelVersions() API to the Java client (#7880, @​gabrielfu)
• [Model Registry] Introduce max_results, order_by and page_token arguments to MlflowClient.search_model_versions() (#7623, @​serena-ruan)
• [Models] Support logging large ONNX models by using external data (#7808, @​dogeplusplus)
• [Models] Add support for logging Diviner models fit in Spark (#7800, @​BenWilson2)
• [Models] Introduce MLFLOW_DEFAULT_PREDICTION_DEVICE environment variable to set the device for pyfunc model inference (#7922, @​ankit-db)
• [Scoring] Publish official Docker images for the MLflow Model scoring server at github.com/mlflow/mlflow/pkgs (#7759, @​dbczumar)

Bug fixes:

• [Recipes] Fix dataset format validation in the ingest step for custom dataset sources (#7638, @​sunishsheth2009)
• [Recipes] Fix bug in identification of worst performing examples during training (#7658, @​sunishsheth2009)
• [Recipes] Ensure consistent rendering of the recipe graph when inspect() is called (#7852, @​sunishsheth2009)
• [Recipes] Correctly respect positive_class configuration in the transform step (#7626, @​sunishsheth2009)
• [Recipes] Make logged metric names consistent with mlflow.evaluate() (#7613, @​sunishsheth2009)
• [Recipes] Add run_id and artifact_path keys to logged MLmodel files (#7651, @​sunishsheth2009)
• [UI] Fix bugs in UI validation of experiment names, model names, and tag keys (#7818, @​subramaniam02)
• [Tracking] Resolve artifact locations to absolute paths when creating experiments (#7670, @​bali0019)
• [Tracking] Exclude Delta checkpoints from Spark datasource autologging (#7902, @​harupy)
• [Tracking] Consistently return an empty list from GetMetricHistory when a metric does not exist (#7589, @​bali0019; #7659, @​harupy)
• [Artifacts] Fix support for artifact operations on Windows paths in UNC format (#7750, @​bali0019)
• [Artifacts] Fix bug in HDFS artifact listing (#7581, @​pwnywiz)
• [Model Registry] Disallow creation of model versions with local filesystem sources in mlflow server (#7908, @​harupy)
• [Model Registry] Fix handling of deleted model versions in FileStore (#7716, @​harupy)
• [Model Registry] Correctly initialize Model Registry SQL tables independently of MLflow Tracking (#7704, @​harupy)
• [Models] Correctly move PyTorch model outputs from GPUs to CPUs during inference with pyfunc (#7885, @​ankit-db)
• [Build] Fix compatiblility issues with Python installations compiled using PYTHONOPTIMIZE=2 (#7791, @​dbczumar)
• [Build] Fix compatibility issues with the upcoming pandas 2.0 release (#7899, @​harupy; #7910, @​dbczumar)

Documentation updates:

... (truncated)

Changelog

Sourced from mlflow's changelog.

2.2.1 (2023-03-02)

MLflow 2.2.1 is a patch release containing the following bug fixes:

• [Model Registry] Fix a bug that caused too many results to be requested by default when calling MlflowClient.search_model_versions() (#7935, @​dbczumar)

2.2.0 (2023-02-28)

MLflow 2.2.0 includes several major features and improvements

Features:

• [Recipes] Add support for score calibration to the classification recipe (#7744, @​sunishsheth2009)
• [Recipes] Add automatic label encoding to the classification recipe (#7711, @​sunishsheth2009)
• [Recipes] Support custom data splitting logic in the classification and regression recipes (#7815, #7588, @​sunishsheth2009)
• [Recipes] Introduce customizable MLflow Run name prefixes to the classification and regression recipes (#7746, @​kamalesh0406; #7763, @​sunishsheth2009)
• [UI] Add a new Chart View to the MLflow Experiment Page for model performance insights (#7864, @​hubertzub-db, @​apurva-koti, @​prithvikannan, @​ridhimag11, @​sunishseth2009, @​dbczumar)
• [UI] Modernize and improve parallel coordinates chart for model tuning (#7864, @​hubertzub-db, @​apurva-koti, @​prithvikannan, @​ridhimag11, @​sunishseth2009, @​dbczumar)
• [UI] Add typeahead suggestions to the MLflow Experiment Page search bar (#7864, @​hubertzub-db, @​apurva-koti, @​prithvikannan, @​ridhimag11, @​sunishseth2009, @​dbczumar)
• [UI] Improve performance of Experiments Sidebar for large numbers of experiments (#7804, @​jmahlik)
• [Tracking] Introduce autologging support for native PyTorch models (#7627, @​temporaer)
• [Tracking] Allow specifying model_format when autologging XGBoost models (#7781, @​guyrosin)
• [Tracking] Add MLFLOW_ARTIFACT_UPLOAD_DOWNLOAD_TIMEOUT environment variable to configure artifact operation timeouts (#7783, @​wamartin-aml)
• [Artifacts] Include Content-Type response headers for artifacts downloaded from mlflow server (#7827, @​bali0019)
• [Model Registry] Introduce the searchModelVersions() API to the Java client (#7880, @​gabrielfu)
• [Model Registry] Introduce max_results, order_by and page_token arguments to MlflowClient.search_model_versions() (#7623, @​serena-ruan)
• [Models] Support logging large ONNX models by using external data (#7808, @​dogeplusplus)
• [Models] Add support for logging Diviner models fit in Spark (#7800, @​BenWilson2)
• [Models] Introduce MLFLOW_DEFAULT_PREDICTION_DEVICE environment variable to set the device for pyfunc model inference (#7922, @​ankit-db)
• [Scoring] Publish official Docker images for the MLflow Model scoring server at github.com/mlflow/mlflow/pkgs (#7759, @​dbczumar)

Bug fixes:

• [Recipes] Fix dataset format validation in the ingest step for custom dataset sources (#7638, @​sunishsheth2009)
• [Recipes] Fix bug in identification of worst performing examples during training (#7658, @​sunishsheth2009)
• [Recipes] Ensure consistent rendering of the recipe graph when inspect() is called (#7852, @​sunishsheth2009)
• [Recipes] Correctly respect positive_class configuration in the transform step (#7626, @​sunishsheth2009)
• [Recipes] Make logged metric names consistent with mlflow.evaluate() (#7613, @​sunishsheth2009)
• [Recipes] Add run_id and artifact_path keys to logged MLmodel files (#7651, @​sunishsheth2009)
• [UI] Fix bugs in UI validation of experiment names, model names, and tag keys (#7818, @​subramaniam02)
• [Tracking] Resolve artifact locations to absolute paths when creating experiments (#7670, @​bali0019)
• [Tracking] Exclude Delta checkpoints from Spark datasource autologging (#7902, @​harupy)
• [Tracking] Consistently return an empty list from GetMetricHistory when a metric does not exist (#7589, @​bali0019; #7659, @​harupy)
• [Artifacts] Fix support for artifact operations on Windows paths in UNC format (#7750, @​bali0019)
• [Artifacts] Fix bug in HDFS artifact listing (#7581, @​pwnywiz)
• [Model Registry] Disallow creation of model versions with local filesystem sources in mlflow server (#7908, @​harupy)
• [Model Registry] Fix handling of deleted model versions in FileStore (#7716, @​harupy)
• [Model Registry] Correctly initialize Model Registry SQL tables independently of MLflow Tracking (#7704, @​harupy)
• [Models] Correctly move PyTorch model outputs from GPUs to CPUs during inference with pyfunc (#7885, @​ankit-db)
• [Build] Fix compatiblility issues with Python installations compiled using PYTHONOPTIMIZE=2 (#7791, @​dbczumar)

... (truncated)

Commits

• ffe005c Run python3 dev/update_mlflow_versions.py pre-release --new-version 2.2.1 (#7...
• 355e148 Revert "Run python3 dev/update_ml_package_versions.py (#7937)"
• 3e09e42 Run python3 dev/update_ml_package_versions.py (#7937)
• 7e4de89 Use default max results of 10000 for model registry search_model_versions() A...
• 8ea83b7 Improve error handling when wheel download fails (#7920)
• f64ebc5 #7921 Modify pytorch predict to use the GPU if it's available (#7922)
• dd0709c Experiment list virtual (#7804)
• e103f97 Ignore delta checkpoint files in spark autologging (#7902)
• 8fd7ddc Bump pandas (#7910)
• 978d68c Run python3 dev/update_ml_package_versions.py (#7905)
• Additional commits viewable in compare view

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