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combinator-ml / terraform-k8s-mlflow

MLflow terraform module
Apache License 2.0
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chore(deps): bump mlflow[extras] from 1.23.1 to 1.24.0 in /docker #24

Closed dependabot[bot] closed 2 years ago

dependabot[bot] commented 2 years ago

Bumps mlflow[extras] from 1.23.1 to 1.24.0.

Release notes

Sourced from mlflow[extras]'s releases.

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)
  • [UI] Improve and restructure the Compare Runs page. Additions include "show diff only" toggles and scrollable tables (#5306, @​WeichenXu123)
  • [Models] Introduce mlflow.pmdarima flavor for pmdarima models (#5373, @​BenWilson2)
  • [Models] When loading an MLflow Model, print a warning if a mismatch is detected between the current environment and the Model's dependencies (#5368, @​WeichenXu123)
  • [Models] Support computing custom scalar metrics during model evaluation with mlflow.evaluate() (#5389, @​MarkYHZhang)
  • [Scoring] Add support for deploying and evaluating SageMaker models via the MLflow Deployments API (#4971, #5396, @​jamestran201)

Bug fixes and documentation updates:

  • [Tracking / UI] Fix artifact listing and download failures that occurred when operating the MLflow server in --serve-artifacts mode (#5409, @​dbczumar)
  • [Tracking] Support environment-variable-based authentication when making artifact requests to the MLflow server in --serve-artifacts mode (#5370, @​TimNooren)
  • [Tracking] Fix bugs in hostname and path resolution when making artifacts requests to the MLflow server in --serve-artifacts mode (#5384, #5385, @​mert-kirpici)
  • [Tracking] Fix an import error that occurred when mlflow.log_figure() was used without matplotlib.figure imported (#5406, @​WeichenXu123)
  • [Tracking] Correctly log XGBoost metrics containing the @ symbol during autologging (#5403, @​maxfriedrich)
  • [Tracking] Fix a SQL Server database error that occurred during Runs search (#5382, @​dianacarvalho1)
  • [Tracking] When downloading artifacts from HDFS, store them in the user-specified destination directory (#5210, @​DimaClaudiu)
  • [Tracking / Model Registry] Improve performance of large artifact and model downloads (#5359, @​mehtayogita)
  • [Models] Fix fast.ai PyFunc inference behavior for models with 2D outputs (#5411, @​santiagxf)
  • [Models] Record Spark model information to the active run when mlflow.spark.log_model() is called (#5355, @​szczeles)
  • [Models] Restore onnxruntime execution providers when loading ONNX models with mlflow.pyfunc.load_model() (#5317, @​ecm200)
  • [Projects] Increase Docker image push timeout when using Projects with Docker (#5363, @​zanitete)
  • [Python] Fix a bug that prevented users from enabling DEBUG-level Python log outputs (#5362, @​dbczumar)
  • [Docs] Add a developer guide explaining how to build custom plugins for mlflow.evaluate() (#5333, @​WeichenXu123)

Note: Version 1.24.0 of the MLflow R package has not yet been released. It will be available on CRAN within the next week.

Changelog

Sourced from mlflow[extras]'s changelog.

1.24.0 (2022-02-27)

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)
  • [UI] Improve and restructure the Compare Runs page. Additions include "show diff only" toggles and scrollable tables (#5306, @​WeichenXu123)
  • [Models] Introduce mlflow.pmdarima flavor for pmdarima models (#5373, @​BenWilson2)
  • [Models] When loading an MLflow Model, print a warning if a mismatch is detected between the current environment and the Model's dependencies (#5368, @​WeichenXu123)
  • [Models] Support computing custom scalar metrics during model evaluation with mlflow.evaluate() (#5389, @​MarkYHZhang)
  • [Scoring] Add support for deploying and evaluating SageMaker models via the MLflow Deployments API <https://mlflow.org/docs/latest/models.html#deployment-to-custom-targets>_ (#4971, #5396, @​jamestran201)

Bug fixes and documentation updates:

  • [Tracking / UI] Fix artifact listing and download failures that occurred when operating the MLflow server in --serve-artifacts mode (#5409, @​dbczumar)
  • [Tracking] Support environment-variable-based authentication when making artifact requests to the MLflow server in --serve-artifacts mode (#5370, @​TimNooren)
  • [Tracking] Fix bugs in hostname and path resolution when making artifacts requests to the MLflow server in --serve-artifacts mode (#5384, #5385, @​mert-kirpici)
  • [Tracking] Fix an import error that occurred when mlflow.log_figure() was used without matplotlib.figure imported (#5406, @​WeichenXu123)
  • [Tracking] Correctly log XGBoost metrics containing the @ symbol during autologging (#5403, @​maxfriedrich)
  • [Tracking] Fix a SQL Server database error that occurred during Runs search (#5382, @​dianacarvalho1)
  • [Tracking] When downloading artifacts from HDFS, store them in the user-specified destination directory (#5210, @​DimaClaudiu)
  • [Tracking / Model Registry] Improve performance of large artifact and model downloads (#5359, @​mehtayogita)
  • [Models] Fix fast.ai PyFunc inference behavior for models with 2D outputs (#5411, @​santiagxf)
  • [Models] Record Spark model information to the active run when mlflow.spark.log_model() is called (#5355, @​szczeles)
  • [Models] Restore onnxruntime execution providers when loading ONNX models with mlflow.pyfunc.load_model() (#5317, @​ecm200)
  • [Projects] Increase Docker image push timeout when using Projects with Docker (#5363, @​zanitete)
  • [Python] Fix a bug that prevented users from enabling DEBUG-level Python log outputs (#5362, @​dbczumar)
  • [Docs] Add a developer guide explaining how to build custom plugins for mlflow.evaluate() (#5333, @​WeichenXu123)

Small bug fixes and doc updates (#5298, @​wamartin-aml; #5399, #5321, #5313, #5307, #5305, #5268, #5284, @​harupy; #5329, @​Ark-kun; #5375, #5346, #5304, @​dbczumar; #5401, #5366, #5345, @​BenWilson2; #5326, #5315, @​WeichenXu123; #5236, @​singankit; #5302, @​timvink; #5357, @​maitre-matt; #5347, #5344, @​mehtayogita; #5367, @​apurva-koti; #5348, #5328, #5310, @​liangz1; #5267, @​sunishsheth2009)

Commits
  • 3816b31 python dev/update_ml_package_versions.py (#5426)
  • ac35fa4 python dev/update_pypi_package_index.py (#5427)
  • e897100 Update MLflow version to 1.24.0 (#5417)
  • e926be9 Implement the predict() function for SageMaker deployment plugin (#5396)
  • 43596ea Adding support for multiclass models or models returning multiple probabiliti...
  • cb7b361 [Custom Metrics] Enabled support for logging of numerical metrics (#5389)
  • a274c5b Fix ListArtifacts and get-artifact compatibility errors with proxied artifact...
  • 2e8a3c5 Add model registration argument for autolog (#5395)
  • 3973283 Pmdarima flavor (#5373)
  • e8db119 init (#5406)
  • Additional commits viewable in compare view


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dependabot[bot] commented 2 years ago

Superseded by #25.