Bump mlflow from 1.24.0 to 1.25.1

[dependabot[bot]]

Bumps mlflow from 1.24.0 to 1.25.1.

Release notes

Sourced from mlflow's releases.

MLflow 1.25.1 is a patch release containing the following bug fixes:

• [Models] Fix a pyfunc artifact overwrite bug when multiple artifacts are saved in sub-directories (#5657, @​kyle-jarvis)
• [Scoring] Fix permissions issue for Spark workers accessing model artifacts from a temp directory created by the driver (#5684, @​WeichenXu123)

Note: Version 1.25.1 of the MLflow R package has not yet been released. It will be available on CRAN within the next week.

MLflow 1.25.0 includes several major features and improvements:

Features:

• [Tracking] Introduce a new fluent API mlflow.last_active_run() that provides the most recent fluent active run (#5584, @​MarkYHZhang)
• [Tracking] Add experiment_names argument to the mlflow.search_runs() API to support searching runs by experiment names (#5564, @​r3stl355)
• [Tracking] Add a description parameter to mlflow.start_run() (#5534, @​dogeplusplus)
• [Tracking] Add log_every_n_step parameter to mlflow.pytorch.autolog() to control metric logging frequency (#5516, @​adamreeve)
• [Tracking] Log pyspark.ml.param.Params values as MLflow parameters during PySpark autologging (#5481, @​serena-ruan)
• [Tracking] Add support for pyspark.ml.Transformers to PySpark autologging (#5466, @​serena-ruan)
• [Tracking] Add input example and signature autologging for Keras models (#5461, @​bali0019)
• [Models] Introduce mlflow.diviner flavor for large-scale time series forecasting (#5553, @​BenWilson2)
• [Models] Add pyfunc.get_model_dependencies() API to retrieve reproducible environment specifications for MLflow Models with the pyfunc flavor (#5503, @​WeichenXu123)
• [Models] Add code_paths argument to all model flavors to support packaging custom module code with MLflow Models (#5448, @​stevenchen-db)
• [Models] Support creating custom artifacts when evaluating models with mlflow.evaluate() (#5405, #5476 @​MarkYHZhang)
• [Models] Add mlflow_version field to MLModel specification (#5515, #5576, @​r3stl355)
• [Models] Add support for logging models to preexisting destination directories (#5572, @​akshaya-a)
• [Scoring / Projects] Introduce --env-manager configuration for specifying environment restoration tools (e.g. conda) and deprecate --no-conda (#5567, @​harupy)
• [Scoring] Support restoring model dependencies in mlflow.pyfunc.spark_udf() to ensure accurate predictions (#5487, #5561, @​WeichenXu123)
• [Scoring] Add support for numpy.ndarray type inputs to the TensorFlow pyfunc predict() function (#5545, @​WeichenXu123)
• [Scoring] Support deployment of MLflow Models to Sagemaker Serverless (#5610, @​matthewmayo)
• [UI] Add MLflow version to header beneath logo (#5504, @​adamreeve)
• [Artifacts] Introduce a mlflow.artifacts.download_artifacts() API mirroring the functionality of the mlflow artifacts download CLI (#5585, @​dbczumar)
• [Artifacts] Introduce environment variables for controlling GCS artifact upload/download chunk size and timeouts (#5438, #5483, @​mokrueger)

Bug fixes and documentation updates:

• [Tracking/SQLAlchemy] Create an index on run_uuid for PostgreSQL to improve query performance (#5446, @​harupy)
• [Tracking] Remove client-side validation of metric, param, tag, and experiment fields (#5593, @​BenWilson2)
• [Projects] Support setting the name of the MLflow Run when executing an MLflow Project (#5187, @​bramrodenburg)
• [Scoring] Use pandas split orientation for DataFrame inputs to SageMaker deployment predict() API to preserve column ordering (#5522, @​dbczumar)
• [Server-Infra] Fix runs search compatibility bugs with PostgreSQL, MySQL, and MSSQL (#5540, @​harupy)
• [CLI] Fix a bug in the mlflow-skinny client that caused mlflow --version to fail (#5573, @​BenWilson2)
• [Docs] Update guidance and examples for model deployment to AzureML to recommend using the mlflow-azureml package (#5491, @​santiagxf)

Small bug fixes and doc updates (#5591, #5629, #5597, #5592, #5562, #5477, @​BenWilson2; #5554, @​juntai-zheng; #5570, @​tahesse; #5605, @​guelate; #5633, #5632, #5625, #5623, #5615, #5608, #5600, #5603, #5602, #5596, #5587, #5586, #5580, #5577, #5568, #5290, #5556, #5560, #5557, #5548, #5547, #5538, #5513, #5505, #5464, #5495, #5488, #5485, #5468, #5455, #5453, #5454, #5452, #5445, #5431, @​harupy; #5640, @​nchittela; #5520, #5422, @​Ark-kun; #5639, #5604, @​nishipy; #5543, #5532, #5447, #5435, @​WeichenXu123; #5502, @​singankit; #5500, @​Sohamkayal4103; #5449, #5442, @​apurva-koti; #5552, @​vinijaiswal; #5511, @​adamreeve; #5428, @​jinzhang21; #5309, @​sunishsheth2009; #5581, #5559, @​Kr4is; #5626, #5618, #5529, @​sisp; #5652, #5624, #5622, #5613, #5509, #5459, #5437, @​dbczumar; #5616, @​liangz1)

Changelog

Sourced from mlflow's changelog.

1.25.1 (2022-04-13)

MLflow 1.25.1 is a patch release containing the following bug fixes:

• [Models] Fix a pyfunc artifact overwrite bug for when multiple artifacts are saved in sub-directories (#5657, @​kyle-jarvis)
• [Scoring] Fix permissions issue for Spark workers accessing model artifacts from a temp directory created by the driver (#5684, @​WeichenXu123)

1.25.0 (2022-04-11)

MLflow 1.25.0 includes several major features and improvements:

Features:

• [Tracking] Introduce a new fluent API mlflow.last_active_run() that provides the most recent fluent active run (#5584, @​MarkYHZhang)
• [Tracking] Add experiment_names argument to the mlflow.search_runs() API to support searching runs by experiment names (#5564, @​r3stl355)
• [Tracking] Add a description parameter to mlflow.start_run() (#5534, @​dogeplusplus)
• [Tracking] Add log_every_n_step parameter to mlflow.pytorch.autolog() to control metric logging frequency (#5516, @​adamreeve)
• [Tracking] Log pyspark.ml.param.Params values as MLflow parameters during PySpark autologging (#5481, @​serena-ruan)
• [Tracking] Add support for pyspark.ml.Transformers to PySpark autologging (#5466, @​serena-ruan)
• [Tracking] Add input example and signature autologging for Keras models (#5461, @​bali0019)
• [Models] Introduce mlflow.diviner flavor for large-scale time series forecasting (#5553, @​BenWilson2)
• [Models] Add pyfunc.get_model_dependencies() API to retrieve reproducible environment specifications for MLflow Models with the pyfunc flavor (#5503, @​WeichenXu123)
• [Models] Add code_paths argument to all model flavors to support packaging custom module code with MLflow Models (#5448, @​stevenchen-db)
• [Models] Support creating custom artifacts when evaluating models with mlflow.evaluate() (#5405, #5476 @​MarkYHZhang)
• [Models] Add mlflow_version field to MLModel specification (#5515, #5576, @​r3stl355)
• [Models] Add support for logging models to preexisting destination directories (#5572, @​akshaya-a)
• [Scoring / Projects] Introduce --env-manager configuration for specifying environment restoration tools (e.g. conda) and deprecate --no-conda (#5567, @​harupy)
• [Scoring] Support restoring model dependencies in mlflow.pyfunc.spark_udf() to ensure accurate predictions (#5487, #5561, @​WeichenXu123)
• [Scoring] Add support for numpy.ndarray type inputs to the TensorFlow pyfunc predict() function (#5545, @​WeichenXu123)
• [Scoring] Support deployment of MLflow Models to Sagemaker Serverless (#5610, @​matthewmayo)
• [UI] Add MLflow version to header beneath logo (#5504, @​adamreeve)
• [Artifacts] Introduce a mlflow.artifacts.download_artifacts() API mirroring the functionality of the mlflow artifacts download CLI (#5585, @​dbczumar)
• [Artifacts] Introduce environment variables for controlling GCS artifact upload/download chunk size and timeouts (#5438, #5483, @​mokrueger)

Bug fixes and documentation updates:

• [Tracking/SQLAlchemy] Create an index on run_uuid for PostgreSQL to improve query performance (#5446, @​harupy)
• [Tracking] Remove client-side validation of metric, param, tag, and experiment fields (#5593, @​BenWilson2)
• [Projects] Support setting the name of the MLflow Run when executing an MLflow Project (#5187, @​bramrodenburg)
• [Scoring] Use pandas split orientation for DataFrame inputs to SageMaker deployment predict() API to preserve column ordering (#5522, @​dbczumar)
• [Server-Infra] Fix runs search compatibility bugs with PostgreSQL, MySQL, and MSSQL (#5540, @​harupy)
• [CLI] Fix a bug in the mlflow-skinny client that caused mlflow --version to fail (#5573, @​BenWilson2)
• [Docs] Update guidance and examples for model deployment to AzureML to recommend using the mlflow-azureml package (#5491, @​santiagxf)

Small bug fixes and doc updates (#5591, #5629, #5597, #5592, #5562, #5477, @​BenWilson2; #5554, @​juntai-zheng; #5570, @​tahesse; #5605, @​guelate; #5633, #5632, #5625, #5623, #5615, #5608, #5600, #5603, #5602, #5596, #5587, #5586, #5580, #5577, #5568, #5290, #5556, #5560, #5557, #5548, #5547, #5538, #5513, #5505, #5464, #5495, #5488, #5485, #5468, #5455, #5453, #5454, #5452, #5445, #5431, @​harupy; #5640, @​nchittela; #5520, #5422, @​Ark-kun; #5639, #5604, @​nishipy; #5543, #5532, #5447, #5435, @​WeichenXu123; #5502, @​singankit; #5500, @​Sohamkayal4103; #5449, #5442, @​apurva-koti; #5552, @​vinijaiswal; #5511, @​adamreeve; #5428, @​jinzhang21; #5309, @​sunishsheth2009; #5581, #5559, @​Kr4is; #5626, #5618, #5529, @​sisp; #5652, #5624, #5622, #5613, #5509, #5459, #5437, @​dbczumar; #5616, @​liangz1)

Commits

• b4ac5e8 Fixes #5582 (#5657)
• 0bfd7da Update MLflow version to 1.25.1 (#5691)
• 7200641 python dev/update_pypi_package_index.py (#5690)
• 9e30917 Fix temp directory permission issue on worker side (#5684) (#5688)
• 6822c55 Set log_model_signatures=False by default for mlflow.tensorflow.autolog()...
• eaba6f9 Revert #5571 (branch-1.25) (#5648)
• 9b62aee Update MLflow version to 1.25.0 (#5644)
• c7454bf python dev/update_pypi_package_index.py (#5643)
• e9b2170 python dev/update_ml_package_versions.py (#5642)
• f4fafb8 Add problem matchers for pylint (#5639)
• Additional commits viewable in compare view

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