Bump mlflow[extras] from 2.0.1 to 2.2.0

Bumps mlflow[extras] from 2.0.1 to 2.2.0.

Release notes

Sourced from mlflow[extras]'s releases.

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:

[Docs] Add an example of saving and loading Spark MLlib models with MLflow (#7706, @dipanjank)

[Docs] Add usage examples for mlflow.lightgbm APIs (#7565, @canerturkseven)

[Docs] Add an example of custom model flavor creation with sktime (#7624, @benjaminbluhm)

[Docs] Clarify precision_recall_auc metric calculation in mlflow.evaluate() (#7701, @BenWilson2)

... (truncated)

Changelog

Sourced from mlflow[extras]'s changelog.

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)

[Build] Fix compatibility issues with the upcoming pandas 2.0 release (#7899, @harupy; #7910, @dbczumar)

Documentation updates:

[Docs] Add an example of saving and loading Spark MLlib models with MLflow (#7706, @dipanjank)

[Docs] Add usage examples for mlflow.lightgbm APIs (#7565, @canerturkseven)

... (truncated)

Commits

977f794 changelog updates (#7924)
182f04c Use a shorter timeout for _await_server_up_or_die (#7917)
dde55af #7921 Modify pytorch predict to use the GPU if it's available (#7922)
c187443 MLflow UI changes (#7923)
545b10e Improve error handling when wheel download fails (#7920)
1f310fa Fix tests/dev/test_update_ml_package_versions.py (#7918)
3b1819a Add support for writing UC model version files at creation time (#7844)
887104c Fix flaky test_search_experiments_filter_by_time_attribute (#7916)
7f6f4c1 Handle empty PR description in advice.js (#7914)
d4754e6 Merge compile_ml_package_versions.py and update_ml_package_versions.py (#...
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neuro-inc / neuro-base-environment

Bump mlflow[extras] from 2.0.1 to 2.2.0 #576

2.2.0 (2023-02-28)