[tune](deps): Bump mlflow from 1.21.0 to 1.25.1 in /python/requirements/ml #61

dependabot · 2022-04-16T07:07:39Z

Bumps mlflow from 1.21.0 to 1.25.1.

Release notes

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)

MLflow 1.24.0 includes several major features and improvements:

Features:

[Tracking] Support uploading, downloading, and listing artifacts through the MLflow server via mlflow server --serve-artifacts (#5320, @BenWilson2, @harupy)

[Tracking] Add the registered_model_name argument to mlflow.autolog() for automatic model registration during autologging (#5395, @WeichenXu123)

... (truncated)

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)

1.24.0 (2022-02-27)

MLflow 1.24.0 includes several major features and improvements:

... (truncated)

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)
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Bumps [mlflow](https://github.com/mlflow/mlflow) from 1.21.0 to 1.25.1. - [Release notes](https://github.com/mlflow/mlflow/releases) - [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md) - [Commits](mlflow/mlflow@v1.21.0...v1.25.1) --- updated-dependencies: - dependency-name: mlflow dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <[email protected]>

dependabot · 2022-05-21T07:12:54Z

Superseded by #70.

dependabot bot added the dependencies Pull requests that update a dependency file label Apr 16, 2022

dependabot bot mentioned this pull request Apr 16, 2022

[tune](deps): Bump mlflow from 1.21.0 to 1.24.0 in /python/requirements/ml #54

Closed

dependabot bot closed this May 21, 2022

dependabot bot deleted the dependabot/pip/python/requirements/ml/mlflow-1.25.1 branch May 21, 2022 07:12

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[tune](deps): Bump mlflow from 1.21.0 to 1.25.1 in /python/requirements/ml #61

[tune](deps): Bump mlflow from 1.21.0 to 1.25.1 in /python/requirements/ml #61

dependabot bot commented on behalf of github Apr 16, 2022

dependabot bot commented on behalf of github May 21, 2022

[tune](deps): Bump mlflow from 1.21.0 to 1.25.1 in /python/requirements/ml #61

[tune](deps): Bump mlflow from 1.21.0 to 1.25.1 in /python/requirements/ml #61

Conversation

dependabot bot commented on behalf of github Apr 16, 2022

1.25.1 (2022-04-13)

1.25.0 (2022-04-11)

1.24.0 (2022-02-27)

dependabot bot commented on behalf of github May 21, 2022