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MLflow v2.0.1 Release Notes

Release Date: 2022-11-14 // over 3 years ago

  • 🚀 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)
    • 0️⃣ [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)

    🐛 Bug fixes:

    • [Recipes] Fix rendering issue with profile cards polyfill (#7154, @hubertzub-db)
    • [Tracking] Set the MLflow Run name correctly when specified as part of the tags argument to mlflow.start_run() (#7228, @Cokral)
    • [Tracking] Fix an issue with conflicting MLflow Run name assignment if the mlflow.runName tag is set (#7138, @harupy)
    • 🚀 [Scoring] Fix incorrect payload constructor error in SageMaker deployment client predict() API (#7193, @dbczumar)
    • 🚀 [Scoring] Fix an issue where DataCaptureConfig information was not preserved when updating a Sagemaker deployment (#7281, @harupy)

    📚 Small bug fixes and documentation updates:

    7309, #7314, #7288, #7276, #7244, #7207, #7175, #7107, @sunishsheth2009; #7261, #7313, #7311, #7249, #7278, #7260, #7284, #7283, #7263, #7266, #7264, #7267, #7265, #7250, #7259, #7247, #7242, #7143, #7214, #7226, #7230, #7227, #7229, #7225, #7224, #7223, #7210, #7192, #7197, #7196, #7204, #7198, #7191, #7189, #7184, #7182, #7170, #7183, #7131, #7165, #7151, #7164, #7168, #7150, #7128, #7028, #7118, #7117, #7102, #7072, #7103, #7101, #7100, #7099, #7098, #7041, #7040, #6978, #6768, #6719, #6669, #6658, #6656, #6655, #6538, #6507, #6504 @harupy; #7310, #7308, #7300, #7290, #7239, #7220, #7127, #7091, #6713 @BenWilson2; #7332, #7299, #7271, #7209, #7180, #7179, #7158, #7147, #7114, @prithvikannan; #7275, #7245, #7134, #7059, @jinzhang21; #7306, #7298, #7287, #7272, #7258, #7236, @ayushthe1; #7279, @tk1012; #7219, @rddefauw; #7333, #7218, #7208, #7188, #7190, #7176, #7137, #7136, #7130, #7124, #7079, #7052, #6541 @dbczumar; #6640, @WeichenXu123; #7200, @hubertzub-db; #7121, @Gonmeso; #6988, @alonisser; #7141, @pdifranc; #7086, @jerrylian-db; #7286, @shogohida