search

DougTrajano / mlflow-server

MLflow Tracking Server with basic auth deployed in AWS App Runner.
https://gallery.ecr.aws/t9j8s4z8/mlflow
Apache License 2.0
34 stars 17 forks source link

Bump mlflow from 1.30.0 to 2.0.1 #202

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Bumps mlflow from 1.30.0 to 2.0.1.

Release notes

Sourced from mlflow's releases.

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
  • [Recipes] Add support for classification models to MLflow Recipes (#7082, @​bbarnes52)
  • [UI] Introduce support for pinning runs within the experiments UI (#7177, @​harupy)
  • [UI] Simplify the layout and provide customized displays of metrics, parameters, and tags within the experiments UI (#7177, @​harupy)
  • [UI] Simplify run filtering and ordering of runs within the experiments UI (#7177, @​harupy)
  • [Tracking] Update mlflow.pyfunc.get_model_dependencies() to download all referenced requirements files for specified models (#6733, @​harupy)
  • [Tracking] Add support for selecting the Keras model save_format used by mlflow.tensorflow.autolog() (#7123, @​balvisio)
  • [Models] Set mlflow.evaluate() status to stable as it is now a production-ready API
  • [Models] Simplify APIs for specifying custom metrics and custom artifacts during model evaluation with mlflow.evaluate() (#7142, @​harupy)
  • [Models] Correctly infer the positive label for binary classification within mlflow.evaluate() (#7149, @​dbczumar)
  • [Models] Enable automated signature logging for tensorflow and keras models when mlflow.tensorflow.autolog() is enabled (#6678, @​BenWilson2)
  • [Models] Add support for native Keras and Tensorflow Core models within mlflow.tensorflow (#6530, @​WeichenXu123)
  • [Models] Add support for defining the model_format used by mlflow.xgboost.save/log_model() (#7068, @​AvikantSrivastava)
  • [Scoring] Overhaul the model scoring REST API to introduce format indicators for inputs and support multiple output fields (#6575, @​tomasatdatabricks; #7254, @​adriangonz)
  • [Scoring] Add support for ragged arrays in model signatures (#7135, @​trangevi)
  • [Java] Add getModelVersion API to the java client (#6955, @​wgottschalk)

Breaking Changes:

The following list of breaking changes are arranged by their order of significance within each category.

  • [Core] Support for Python 3.7 has been dropped. MLflow now requires Python >=3.8
  • [Recipes] mlflow.pipelines APIs have been replaced with mlflow.recipes
  • [Tracking / Registry] Remove /preview routes for Tracking and Model Registry REST APIs (#6667, @​harupy)
  • [Tracking] Remove deprecated list APIs for experiments, models, and runs from Python, Java, R, and REST APIs (#6785, #6786, #6787, #6788, #6800, #6868, @​dbczumar)
  • [Tracking] Remove deprecated runs response field from Get Experiment REST API response (#6541, #6524 @​dbczumar)
  • [Tracking] Remove deprecated MlflowClient.download_artifacts API (#6537, @​WeichenXu123)
  • [Tracking] Change the behavior of environment variable handling for MLFLOW_EXPERIMENT_NAME such that the value is always used when creating an experiment (#6674, @​BenWilson2)
  • [Tracking] Update mlflow server to run in --serve-artifacts mode by default (#6502, @​harupy)
  • [Tracking] Update Experiment ID generation for the Filestore backend to enable threadsafe concurrency (#7070, @​BenWilson2)
  • [Tracking] Remove dataset_name and on_data_{name | hash} suffixes from mlflow.evaluate() metric keys (#7042, @​harupy)
  • [Models / Scoring / Projects] Change default environment manager to virtualenv instead of conda for model inference and project execution (#6459, #6489 @​harupy)
  • [Models] Move Keras model logging APIs to the mlflow.tensorflow flavor and drop support for TensorFlow Estimators (#6530, @​WeichenXu123)
  • [Models] Remove deprecated mlflow.sklearn.eval_and_log_metrics() API in favor of mlflow.evaluate() API (#6520, @​dbczumar)
  • [Models] Require mlflow.evaluate() model inputs to be specified as URIs (#6670, @​harupy)
  • [Models] Drop support for returning custom metrics and artifacts from the same function when using mlflow.evaluate(), in favor of custom_artifacts (#7142, @​harupy)
  • [Models] Extend PyFuncModel spec to support conda and virtualenv subfields (#6684, @​harupy)
  • [Scoring] Remove support for defining input formats using the Content-Type header (#6575, @​tomasatdatabricks; #7254, @​adriangonz)
  • [Scoring] Replace the --no-conda CLI option argument for native serving with --env-manager='local' (#6501, @​harupy)
  • [Scoring] Remove public APIs for mlflow.sagemaker.deploy() and mlflow.sagemaker.delete() in favor of MLflow deployments APIs, such as mlflow deployments -t sagemaker (#6650, @​dbczumar)
  • [Scoring] Rename input argument df to inputs in mlflow.deployments.predict() method (#6681, @​BenWilson2)
  • [Projects] Replace the use_conda argument with the env_manager argument within the run CLI command for MLflow Projects (#6654, @​harupy)
  • [Projects] Modify the MLflow Projects docker image build options by renaming --skip-image-build to --build-image with a default of False (#7011, @​harupy)
  • [Integrations/Azure] Remove deprecated mlflow.azureml modules from MLflow in favor of the azure-mlflow deployment plugin (#6691, @​BenWilson2)
  • [R] Remove conda integration with the R client (#6638, @​harupy)

... (truncated)

Changelog

Sourced from mlflow's changelog.

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.

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
  • [Recipes] Add support for classification models to MLflow Recipes (#7082, @​bbarnes52)
  • [UI] Introduce support for pinning runs within the experiments UI (#7177, @​harupy)
  • [UI] Simplify the layout and provide customized displays of metrics, parameters, and tags within the experiments UI (#7177, @​harupy)
  • [UI] Simplify run filtering and ordering of runs within the experiments UI (#7177, @​harupy)
  • [Tracking] Update mlflow.pyfunc.get_model_dependencies() to download all referenced requirements files for specified models (#6733, @​harupy)
  • [Tracking] Add support for selecting the Keras model save_format used by mlflow.tensorflow.autolog() (#7123, @​balvisio)
  • [Models] Set mlflow.evaluate() status to stable as it is now a production-ready API
  • [Models] Simplify APIs for specifying custom metrics and custom artifacts during model evaluation with mlflow.evaluate() (#7142, @​harupy)
  • [Models] Correctly infer the positive label for binary classification within mlflow.evaluate() (#7149, @​dbczumar)
  • [Models] Enable automated signature logging for tensorflow and keras models when mlflow.tensorflow.autolog() is enabled (#6678, @​BenWilson2)
  • [Models] Add support for native Keras and Tensorflow Core models within mlflow.tensorflow (#6530, @​WeichenXu123)
  • [Models] Add support for defining the model_format used by mlflow.xgboost.save/log_model() (#7068, @​AvikantSrivastava)
  • [Scoring] Overhaul the model scoring REST API to introduce format indicators for inputs and support multiple output fields (#6575, @​tomasatdatabricks; #7254, @​adriangonz)
  • [Scoring] Add support for ragged arrays in model signatures (#7135, @​trangevi)
  • [Java] Add getModelVersion API to the java client (#6955, @​wgottschalk)

Breaking Changes:

The following list of breaking changes are arranged by their order of significance within each category.

  • [Core] Support for Python 3.7 has been dropped. MLflow now requires Python >=3.8
  • [Recipes] mlflow.pipelines APIs have been replaced with mlflow.recipes
  • [Tracking / Registry] Remove /preview routes for Tracking and Model Registry REST APIs (#6667, @​harupy)
  • [Tracking] Remove deprecated list APIs for experiments, models, and runs from Python, Java, R, and REST APIs (#6785, #6786, #6787, #6788, #6800, #6868, @​dbczumar)
  • [Tracking] Remove deprecated runs response field from Get Experiment REST API response (#6541, #6524 @​dbczumar)
  • [Tracking] Remove deprecated MlflowClient.download_artifacts API (#6537, @​WeichenXu123)
  • [Tracking] Change the behavior of environment variable handling for MLFLOW_EXPERIMENT_NAME such that the value is always used when creating an experiment (#6674, @​BenWilson2)
  • [Tracking] Update mlflow server to run in --serve-artifacts mode by default (#6502, @​harupy)
  • [Tracking] Update Experiment ID generation for the Filestore backend to enable threadsafe concurrency (#7070, @​BenWilson2)
  • [Tracking] Remove dataset_name and on_data_{name | hash} suffixes from mlflow.evaluate() metric keys (#7042, @​harupy)
  • [Models / Scoring / Projects] Change default environment manager to virtualenv instead of conda for model inference and project execution (#6459, #6489 @​harupy)
  • [Models] Move Keras model logging APIs to the mlflow.tensorflow flavor and drop support for TensorFlow Estimators (#6530, @​WeichenXu123)
  • [Models] Remove deprecated mlflow.sklearn.eval_and_log_metrics() API in favor of mlflow.evaluate() API (#6520, @​dbczumar)
  • [Models] Require mlflow.evaluate() model inputs to be specified as URIs (#6670, @​harupy)
  • [Models] Drop support for returning custom metrics and artifacts from the same function when using mlflow.evaluate(), in favor of custom_artifacts (#7142, @​harupy)
  • [Models] Extend PyFuncModel spec to support conda and virtualenv subfields (#6684, @​harupy)
  • [Scoring] Remove support for defining input formats using the Content-Type header (#6575, @​tomasatdatabricks; #7254, @​adriangonz)
  • [Scoring] Replace the --no-conda CLI option argument for native serving with --env-manager='local' (#6501, @​harupy)
  • [Scoring] Remove public APIs for mlflow.sagemaker.deploy() and mlflow.sagemaker.delete() in favor of MLflow deployments APIs, such as mlflow deployments -t sagemaker (#6650, @​dbczumar)
  • [Scoring] Rename input argument df to inputs in mlflow.deployments.predict() method (#6681, @​BenWilson2)
  • [Projects] Replace the use_conda argument with the env_manager argument within the run CLI command for MLflow Projects (#6654, @​harupy)
  • [Projects] Modify the MLflow Projects docker image build options by renaming --skip-image-build to --build-image with a default of False (#7011, @​harupy)

... (truncated)

Commits


Dependabot compatibility score

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
dependabot[bot] commented 1 year ago

Looks like mlflow is up-to-date now, so this is no longer needed.