chore(deps): bump mlflow[extras] from 1.27.0 to 2.1.0 in /docker

Bumps mlflow[extras] from 1.27.0 to 2.1.0.

Release notes

Sourced from mlflow[extras]'s releases.

MLflow 2.1.0 includes several major features and improvements

Features:

[Recipes] Introduce support for multi-class classification (#7458, @mshtelma)

[Recipes] Extend the pyfunc representation of classification models to output scores in addition to labels (#7474, @sunishsheth2009)

[UI] Add user ID and lifecycle stage quick search links to the Runs page (#7462, @jaeday)

[Tracking] Paginate the GetMetricHistory API (#7523, #7415, @BenWilson2)

[Tracking] Add Runs search aliases for Run name and start time that correspond to UI column names (#7492, @apurva-koti)

[Tracking] Add a /version endpoint to mlflow server for querying the server's MLflow version (#7273, @joncarter1)

[Model Registry] Add FileStore support for the Model Registry (#6605, @serena-ruan)

[Model Registry] Introduce an mlflow.search_registered_models() fluent API (#7428, @TSienki)

[Model Registry / Java] Add a getRegisteredModel() method to the Java client (#6602) (#7511, @drod331)

[Model Registry / R] Add an mlflow_set_model_version_tag() method to the R client (#7401, @leeweijie)

[Models] Introduce a metadata field to the MLmodel specification and log_model() methods (#7237, @jdonzallaz)

[Models] Extend Model.load() to support loading MLmodel specifications from remote locations (#7517, @dbczumar)

[Models] Pin the major version of MLflow in Models' requirements.txt and conda.yaml files (#7364, @BenWilson2)

[Scoring] Extend mlflow.pyfunc.spark_udf() to support StructType results (#7527, @WeichenXu123)

[Scoring] Extend TensorFlow and Keras Models to support multi-dimensional inputs with mlflow.pyfunc.spark_udf()(#7531, #7291, @WeichenXu123)

[Scoring] Support specifying deployment environment variables and tags when deploying models to SageMaker (#7433, @jhallard)

Bug fixes:

[Recipes] Fix a bug that prevented use of custom early_stop functions during model tuning (#7538, @sunishsheth2009)

[Recipes] Fix a bug in the logic used to create a Spark session during data ingestion (#7307, @WeichenXu123)

[Tracking] Make the metric names produced by mlflow.autolog() consistent with mlflow.evaluate() (#7418, @wenfeiy-db)

[Tracking] Fix an autologging bug that caused nested, redundant information to be logged for XGBoost and LightGBM models (#7404, @WeichenXu123)

[Tracking] Correctly classify SQLAlchemy OperationalErrors as retryable HTTP errors (#7240, @barrywhart)

[Artifacts] Correctly handle special characters in credentials when using FTP artifact storage (#7479, @HCTsai)

[Models] Address an issue that prevented MLeap models from being saved on Windows (#6966, @dbczumar)

[Scoring] Fix a permissions issue encountered when using NFS during model scoring with mlflow.pyfunc.spark_udf() (#7427, @WeichenXu123)

Documentation updates:

[Docs] Add more examples to the Runs search documentation page (#7487, @apurva-koti)

[Docs] Add documentation for Model flavors developed by the community (#7425, @mmerce)

[Docs] Add an example for logging and scoring ONNX Models (#7398, @Rusteam)

[Docs] Fix a typo in the model scoring REST API example for inputs with the dataframe_split format (#7540, @zhouyangyu)

[Docs] Fix a typo in the model scoring REST API example for inputs with the dataframe_records format (#7361, @dbczumar)

Small bug fixes and documentation updates:

#7571, #7543, #7529, #7435, #7399, @WeichenXu123; #7568, @xiaoye-hua; #7549, #7557, #7509, #7498, #7499, #7485, #7486, #7484, #7391, #7388, #7390, #7381, #7366, #7348, #7346, #7334, #7340, #7323, @BenWilson2; #7561, #7562, #7560, #7553, #7546, #7539, #7544, #7542, #7541, #7533, #7507, #7470, #7469, #7467, #7466, #7464, #7453, #7449, #7450, #7440, #7430, #7436, #7429, #7426, #7410, #7406, #7409, #7407, #7405, #7396, #7393, #7395, #7384, #7376, #7379, #7375, #7354, #7353, #7351, #7352, #7350, #7345, #6493, #7343, #7344, @harupy; #7494, @dependabot[bot]; #7526, @tobycheese; #7489, @liangz1; #7534, @Jingnan-Jia; #7496, @danielhstahl; #7504, #7503, #7459, #7454, #7447, @tsugumi-sys; #7461, @wkrt7; #7451, #7414, #7372, #7289, @sunishsheth2009; #7441, @ikrizanic; #7432, @Pochingto; #7386, @jhallard; #7370, #7373, #7371, #7336, #7341, #7342, @dbczumar; #7335, @prithvikannan

MLflow 2.0.1

The 2.0.1 version of MLflow is a major milestone release that focuses on simplifying the management of end-to-end MLOps workflows, providing new feature-rich functionality, and expanding upon the production-ready MLOps capabilities offered by MLflow. This release contains several important breaking changes from the 1.x API, additional major features and improvements.

Features:

[Recipes] MLflow Pipelines is now MLflow Recipes - a framework that enables data scientists to quickly develop high-quality models and deploy them to production

... (truncated)

Changelog

Sourced from mlflow[extras]'s changelog.

2.1.0 (2022-12-21)

MLflow 2.1.0 includes several major features and improvements

Features:

[Recipes] Introduce support for multi-class classification (#7458, @mshtelma)

[Recipes] Extend the pyfunc representation of classification models to output scores in addition to labels (#7474, @sunishsheth2009)

[UI] Add user ID and lifecycle stage quick search links to the Runs page (#7462, @jaeday)

[Tracking] Paginate the GetMetricHistory API (#7523, #7415, @BenWilson2)

[Tracking] Add Runs search aliases for Run name and start time that correspond to UI column names (#7492, @apurva-koti)

[Tracking] Add a /version endpoint to mlflow server for querying the server's MLflow version (#7273, @joncarter1)

[Model Registry] Add FileStore support for the Model Registry (#6605, @serena-ruan)

[Model Registry] Introduce an mlflow.search_registered_models() fluent API (#7428, @TSienki)

[Model Registry / Java] Add a getRegisteredModel() method to the Java client (#6602) (#7511, @drod331)

[Model Registry / R] Add an mlflow_set_model_version_tag() method to the R client (#7401, @leeweijie)

[Models] Introduce a metadata field to the MLmodel specification and log_model() methods (#7237, @jdonzallaz)

[Models] Extend Model.load() to support loading MLmodel specifications from remote locations (#7517, @dbczumar)

[Models] Pin the major version of MLflow in Models' requirements.txt and conda.yaml files (#7364, @BenWilson2)

[Scoring] Extend mlflow.pyfunc.spark_udf() to support StructType results (#7527, @WeichenXu123)

[Scoring] Extend TensorFlow and Keras Models to support multi-dimensional inputs with mlflow.pyfunc.spark_udf()(#7531, #7291, @WeichenXu123)

[Scoring] Support specifying deployment environment variables and tags when deploying models to SageMaker (#7433, @jhallard)

Bug fixes:

[Recipes] Fix a bug that prevented use of custom early_stop functions during model tuning (#7538, @sunishsheth2009)

[Recipes] Fix a bug in the logic used to create a Spark session during data ingestion (#7307, @WeichenXu123)

[Tracking] Make the metric names produced by mlflow.autolog() consistent with mlflow.evaluate() (#7418, @wenfeiy-db)

[Tracking] Fix an autologging bug that caused nested, redundant information to be logged for XGBoost and LightGBM models (#7404, @WeichenXu123)

[Tracking] Correctly classify SQLAlchemy OperationalErrors as retryable HTTP errors (#7240, @barrywhart)

[Artifacts] Correctly handle special characters in credentials when using FTP artifact storage (#7479, @HCTsai)

[Models] Address an issue that prevented MLeap models from being saved on Windows (#6966, @dbczumar)

[Scoring] Fix a permissions issue encountered when using NFS during model scoring with mlflow.pyfunc.spark_udf() (#7427, @WeichenXu123)

Documentation updates:

[Docs] Add more examples to the Runs search documentation page (#7487, @apurva-koti)

[Docs] Add documentation for Model flavors developed by the community (#7425, @mmerce)

[Docs] Add an example for logging and scoring ONNX Models (#7398, @Rusteam)

[Docs] Fix a typo in the model scoring REST API example for inputs with the dataframe_split format (#7540, @zhouyangyu)

[Docs] Fix a typo in the model scoring REST API example for inputs with the dataframe_records format (#7361, @dbczumar)

Small bug fixes and documentation updates:

#7571, #7543, #7529, #7435, #7399, @WeichenXu123; #7568, @xiaoye-hua; #7549, #7557, #7509, #7498, #7499, #7485, #7486, #7484, #7391, #7388, #7390, #7381, #7366, #7348, #7346, #7334, #7340, #7323, @BenWilson2; #7561, #7562, #7560, #7553, #7546, #7539, #7544, #7542, #7541, #7533, #7507, #7470, #7469, #7467, #7466, #7464, #7453, #7449, #7450, #7440, #7430, #7436, #7429, #7426, #7410, #7406, #7409, #7407, #7405, #7396, #7393, #7395, #7384, #7376, #7379, #7375, #7354, #7353, #7351, #7352, #7350, #7345, #6493, #7343, #7344, @harupy; #7494, @dependabot[bot]; #7526, @tobycheese; #7489, @liangz1; #7534, @Jingnan-Jia; #7496, @danielhstahl; #7504, #7503, #7459, #7454, #7447, @tsugumi-sys; #7461, @wkrt7; #7451, #7414, #7372, #7289, @sunishsheth2009; #7441, @ikrizanic; #7432, @Pochingto; #7386, @jhallard; #7370, #7373, #7371, #7336, #7341, #7342, @dbczumar; #7335, @prithvikannan

2.0.1 (2022-11-14)

The 2.0.1 version of MLflow is a major milestone release that focuses on simplifying the management of end-to-end MLOps workflows, providing new feature-rich functionality, and expanding upon the production-ready MLOps capabilities offered by MLflow. This release contains several important breaking changes from the 1.x API, additional major features and improvements.

... (truncated)

Commits

a94ab5d Run python3 dev/update_mlflow_versions.py pre-release --new-version 2.1.0 (#7...
3210b37 [ALL TESTS] Update (#7572)
0886c16 Update requirements for Mlflow 2.1.0 releasing (#7573)
9afde60 Upgrade PySpark maximum supported version to 3.4.0 (for supporting databricks...
e53b93a Run python3 dev/update_ml_package_versions.py (#7570)
046d3e8 Run python3 dev/update_pypi_package_index.py (#7569)
8a2c80c convert recipies/cards to pytest (#7568)
145bda8 Adding support for predict and predict_proba in mlflow recipe model (#7474)
d9bda69 Introduce stability annotation indicator for contributors (#7549)
37f4bbe Avoid using GitHub API (#7561)
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combinator-ml / terraform-k8s-mlflow

chore(deps): bump mlflow[extras] from 1.27.0 to 2.1.0 in /docker #36

MLflow 2.0.1

2.1.0 (2022-12-21)

2.0.1 (2022-11-14)