Bump mlflow from 1.19.0 to 2.2.1

Bumps mlflow from 1.19.0 to 2.2.1.

Release notes

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

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

[Model Registry] Patch for https://github.com/mlflow/mlflow/security/advisories/GHSA-xg73-94fp-g449 (#7908, @harupy)

[Model Registry] Patch for https://github.com/mlflow/mlflow/security/advisories/GHSA-wp72-7hj9-5265 (#7965, @harupy)

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)

... (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)

[Model Registry] Patch for GHSA-xg73-94fp-g449 (#7908, @harupy)

[Model Registry] Patch for GHSA-wp72-7hj9-5265 (#7965, @harupy)

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)

... (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)
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AICoE / prometheus-anomaly-detector

Bump mlflow from 1.19.0 to 2.2.1 #199

2.2.1 (2023-03-02)

2.2.0 (2023-02-28)