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MLflow 2.3.2

· 2 min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

MLflow 2.3.2 is a patch release containing the following features, bug fixes and changes:

Features:

  • [Models] Add GPU support for transformers models pyfunc inference and serving (#8375, @ankit-db)
  • [Models] Disable autologging functionality for non-relevant models when training a transformers model (#8405, @BenWilson2)
  • [Models] Add support for preserving and overriding torch_dtype values in transformers pipelines (#8421, @BenWilson2)
  • [Models] Add support for Feature Extraction pipelines in the transformers flavor (#8423, @BenWilson2)
  • [Tracking] Add basic HTTP auth support for users, registered models, and experiments permissions (#8286, @gabrielfu)

Bug Fixes:

  • [Models] Fix inferred schema issue with Text2TextGeneration pipelines in the transformers flavor (#8391, @BenWilson2)
  • [Models] Change MLflow dependency pinning in logged models from a range value to an exact major and minor version (#8422, @harupy)

Documentation updates:

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.3.1

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

MLflow 2.3.1 is a patch release containing bug fixes and a security patch for GHSA-83fm-w79m-64r5. If you are using mlflow server or mlflow ui, we recommend upgrading to MLflow 2.3.1 as soon as possible.

Security patches:

  • [Security] Fix critical LFI attack vulnerability by disabling the ability to provide relative paths in registered model sources (#8281, @BenWilson2)

Bug fixes:

  • [Tracking] Fix an issue causing file and model uploads to hang on Databricks (#8348, @harupy)
  • [Tracking / Model Registry] Fix an issue causing file and model downloads to hang on Databricks (#8350, @dbczumar)
  • [Scoring] Fix regression in schema enforcement for model serving when using the inputs format for inference (#8326, @BenWilson2)
  • [Model Registry] Fix regression in model naming parsing where special characters were not accepted in model names (#8322, @arpitjasa-db)
  • [Recipes] Fix card rendering with the pandas profiler to handle columns containing all null values (#8263, @sunishsheth2009)

MLflow 2.3.0

· 3 min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 2.3.0!

MLflow 2.3.0 includes several major features and improvements

Features:

Bug fixes:

  • [Models] Fix a bug with tensorflow autologging for models with multiple inputs (#8097, @jaume-ferrarons)
  • [Recipes] Fix a bug with Pandas 2.0 updates for profiler rendering of datetime types (#7925, @sunishsheth2009)
  • [Tracking] Prevent exceptions from being raised if a parameter is logged with an existing key whose value is identical to the logged parameter (#8038, @AdamStelmaszczyk)
  • [Tracking] Fix an issue with deleting experiments in the FileStore backend (#8178, @mariusschlegel)
  • [Tracking] Fix a UI bug where the "Source Run" field in the Model Version page points to an incorrect set of artifacts (#8156, @WeichenXu123)
  • [Tracking] Fix a bug wherein renaming a run reverts its current lifecycle status to UNFINISHED (#8154, @WeichenXu123)
  • [Tracking] Fix a bug where a file URI could be used as a model version source (#8126, @harupy)
  • [Projects] Fix an issue with MLflow projects that have submodules contained within a project (#8050, @kota-iizuka)
  • [Examples] Fix lightning hyperparameter tuning examples (#8039, @BenWilson2)
  • [Server-infra] Fix bug with Cache-Control headers for static server files (#8016, @jmahlik)

Documentation updates:

  • [Examples] Add a new and thorough example for the creation of custom model flavors (#7867, @benjaminbluhm)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.2.2

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 2.2.2!

MLflow 2.2.2 is a patch release containing the following bug fixes:

  • [Model Registry] Allow source to be a local path within a run's artifact directory if a run_id is specified (#7993, @harupy)
  • [Model Registry] Fix a bug where a windows UNC path is considered a local path (#7988, @WeichenXu123)
  • [Model Registry] Disallow name to be a file path in FileStore.get_registered_model (#7965, @harupy)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.2.1

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 2.2.1!

MLflow 2.2.1 is a patch release containing the following bug fixes:

  • [Model Registry] Fix a bug that caused too many results to be requested by default when calling MlflowClient.search_model_versions() (#7935, @dbczumar)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.2.0

· 4 min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 2.2.0!

MLflow 2.2.0 includes several major features and improvements

Features:

Bug fixes:

  • [Recipes] Fix dataset format validation in the ingest step for custom dataset sources (#7638, @sunishsheth2009)
  • [Recipes] Fix bug in identification of worst performing examples during training (#7658, @sunishsheth2009)
  • [Recipes] Ensure consistent rendering of the recipe graph when inspect() is called (#7852, @sunishsheth2009)
  • [Recipes] Correctly respect positive_class configuration in the transform step (#7626, @sunishsheth2009)
  • [Recipes] Make logged metric names consistent with mlflow.evaluate() (#7613, @sunishsheth2009)
  • [Recipes] Add run_id and artifact_path keys to logged MLmodel files (#7651, @sunishsheth2009)
  • [UI] Fix bugs in UI validation of experiment names, model names, and tag keys (#7818, @subramaniam02)
  • [Tracking] Resolve artifact locations to absolute paths when creating experiments (#7670, @bali0019)
  • [Tracking] Exclude Delta checkpoints from Spark datasource autologging (#7902, @harupy)
  • [Tracking] Consistently return an empty list from GetMetricHistory when a metric does not exist (#7589, @bali0019; #7659, @harupy)
  • [Artifacts] Fix support for artifact operations on Windows paths in UNC format (#7750, @bali0019)
  • [Artifacts] Fix bug in HDFS artifact listing (#7581, @pwnywiz)
  • [Model Registry] Disallow creation of model versions with local filesystem sources in mlflow server (#7908, @harupy)
  • [Model Registry] Fix handling of deleted model versions in FileStore (#7716, @harupy)
  • [Model Registry] Correctly initialize Model Registry SQL tables independently of MLflow Tracking (#7704, @harupy)
  • [Models] Correctly move PyTorch model outputs from GPUs to CPUs during inference with pyfunc (#7885, @ankit-db)
  • [Build] Fix compatiblility issues with Python installations compiled using PYTHONOPTIMIZE=2 (#7791, @dbczumar)
  • [Build] Fix compatibility issues with the upcoming pandas 2.0 release (#7899, @harupy; #7910, @dbczumar)

Documentation updates:

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.1.1

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 2.1.1!

MLflow 2.1.1 is a patch release containing the following bug fixes:

  • [Scoring] Fix mlflow.pyfunc.spark_udf() type casting error on model with ColSpec input schema and make PyFuncModel.predict support dataframe with elements of numpy.ndarray type (#7592 @WeichenXu123)
  • [Scoring] Make mlflow.pyfunc.scoring_server.client.ScoringServerClient support input dataframe with elements of numpy.ndarray type (#7594 @WeichenXu123)
  • [Tracking] Ensure mlflow imports ML packages lazily (#7597, @harupy)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.1.0

· 3 min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 2.1.0!

MLflow 2.1.0 includes several major features and improvements

Features:

  • [Recipes] Introduce support for multi-class classification (#7458, @mshtelma)
  • [Recipes] Extend the pyfunc representation of classification models to output scores in addition to labels (#7474, @sunishsheth2009)
  • [UI] Add user ID and lifecycle stage quick search links to the Runs page (#7462, @jaeday)
  • [Tracking] Paginate the GetMetricHistory API (#7523, #7415, @BenWilson2)
  • [Tracking] Add Runs search aliases for Run name and start time that correspond to UI column names (#7492, @apurva-koti)
  • [Tracking] Add a /version endpoint to mlflow server for querying the server's MLflow version (#7273, @joncarter1)
  • [Model Registry] Add FileStore support for the Model Registry (#6605, @serena-ruan)
  • [Model Registry] Introduce an mlflow.search_registered_models() fluent API (#7428, @TSienki)
  • [Model Registry / Java] Add a getRegisteredModel() method to the Java client (#6602) (#7511, @drod331)
  • [Model Registry / R] Add an mlflow_set_model_version_tag() method to the R client (#7401, @leeweijie)
  • [Models] Introduce a metadata field to the MLmodel specification and log_model() methods (#7237, @jdonzallaz)
  • [Models] Extend Model.load() to support loading MLmodel specifications from remote locations (#7517, @dbczumar)
  • [Models] Pin the major version of MLflow in Models' requirements.txt and conda.yaml files (#7364, @BenWilson2)
  • [Scoring] Extend mlflow.pyfunc.spark_udf() to support StructType results (#7527, @WeichenXu123)
  • [Scoring] Extend TensorFlow and Keras Models to support multi-dimensional inputs with mlflow.pyfunc.spark_udf()(#7531, #7291, @WeichenXu123)
  • [Scoring] Support specifying deployment environment variables and tags when deploying models to SageMaker (#7433, @jhallard)

Bug fixes:

  • [Recipes] Fix a bug that prevented use of custom early_stop functions during model tuning (#7538, @sunishsheth2009)
  • [Recipes] Fix a bug in the logic used to create a Spark session during data ingestion (#7307, @WeichenXu123)
  • [Tracking] Make the metric names produced by mlflow.autolog() consistent with mlflow.evaluate() (#7418, @wenfeiy-db)
  • [Tracking] Fix an autologging bug that caused nested, redundant information to be logged for XGBoost and LightGBM models (#7404, @WeichenXu123)
  • [Tracking] Correctly classify SQLAlchemy OperationalErrors as retryable HTTP errors (#7240, @barrywhart)
  • [Artifacts] Correctly handle special characters in credentials when using FTP artifact storage (#7479, @HCTsai)
  • [Models] Address an issue that prevented MLeap models from being saved on Windows (#6966, @dbczumar)
  • [Scoring] Fix a permissions issue encountered when using NFS during model scoring with mlflow.pyfunc.spark_udf() (#7427, @WeichenXu123)

Documentation updates:

  • [Docs] Add more examples to the Runs search documentation page (#7487, @apurva-koti)
  • [Docs] Add documentation for Model flavors developed by the community (#7425, @mmerce)
  • [Docs] Add an example for logging and scoring ONNX Models (#7398, @Rusteam)
  • [Docs] Fix a typo in the model scoring REST API example for inputs with the dataframe_split format (#7540, @zhouyangyu)
  • [Docs] Fix a typo in the model scoring REST API example for inputs with the dataframe_records format (#7361, @dbczumar)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 2.0.1

· 5 min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of 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. Check out the MLflow 2.0 blog post for an in-depth walk through!

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)

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)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

MLflow 1.30.0

· 3 min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 1.30.0!

MLflow 1.30.0 includes several major features and improvements

Features:

  • [Pipelines] Introduce hyperparameter tuning support to MLflow Pipelines (#6859, @prithvikannan)
  • [Pipelines] Introduce support for prediction outlier comparison to training data set (#6991, @jinzhang21)
  • [Pipelines] Introduce support for recording all training parameters for reproducibility (#7026, #7094, @prithvikannan)
  • [Pipelines] Add support for Delta tables as a datasource in the ingest step (#7010, @sunishsheth2009)
  • [Pipelines] Add expanded support for data profiling up to 10,000 columns (#7035, @prithvikanna)
  • [Pipelines] Add support for AutoML in MLflow Pipelines using FLAML (#6959, @mshtelma)
  • [Pipelines] Add support for simplified transform step execution by allowing for unspecified configuration (#6909, @apurva-koti)
  • [Pipelines] Introduce a data preview tab to the transform step card (#7033, @prithvikannan)
  • [Tracking] Introduce run_name attribute for create_run, get_run and update_run APIs (#6782, #6798 @apurva-koti)
  • [Tracking] Add support for searching by creation_time and last_update_time for the search_experiments API (#6979, @harupy)
  • [Tracking] Add support for search terms run_id IN and run ID NOT IN for the search_runs API (#6945, @harupy)
  • [Tracking] Add support for searching by user_id and end_time for the search_runs API (#6881, #6880 @subramaniam02)
  • [Tracking] Add support for searching by run_name and run_id for the search_runs API (#6899, @harupy; #6952, @alexacole)
  • [Tracking] Add support for synchronizing run name attribute and mlflow.runName tag (#6971, @BenWilson2)
  • [Tracking] Add support for signed tracking server requests using AWSSigv4 and AWS IAM (#7044, @pdifranc)
  • [Tracking] Introduce the update_run() API for modifying the status and name attributes of existing runs (#7013, @gabrielfu)
  • [Tracking] Add support for experiment deletion in the mlflow gc cli API (#6977, @shaikmoeed)
  • [Models] Add support for environment restoration in the evaluate() API (#6728, @jerrylian-db)
  • [Models] Remove restrictions on binary classification labels in the evaluate() API (#7077, @dbczumar)
  • [Scoring] Add support for BooleanType to mlflow.pyfunc.spark_udf() (#6913, @BenWilson2)
  • [SQLAlchemy] Add support for configurable Pool class options for SqlAlchemyStore (#6883, @mingyu89)

Bug fixes:

  • [Pipelines] Enable Pipeline subprocess commands to create a new SparkSession if one does not exist (#6846, @prithvikannan)
  • [Pipelines] Fix a rendering issue with bool column types in Step Card data profiles (#6907, @sunishsheth2009)
  • [Pipelines] Add validation and an exception if required step files are missing (#7067, @mingyu89)
  • [Pipelines] Change step configuration validation to only be performed during runtime execution of a step (#6967, @prithvikannan)
  • [Tracking] Fix infinite recursion bug when inferring the model schema in mlflow.pyspark.ml.autolog() (#6831, @harupy)
  • [UI] Remove the browser error notification when failing to fetch artifacts (#7001, @kevingreer)
  • [Models] Allow mlflow-skinny package to serve as base requirement in MLmodel requirements (#6974, @BenWilson2)
  • [Models] Fix an issue with code path resolution for loading SparkML models (#6968, @dbczumar)
  • [Models] Fix an issue with dependency inference in logging SparkML models (#6912, @BenWilson2)
  • [Models] Fix an issue involving potential duplicate downloads for SparkML models (#6903, @serena-ruan)
  • [Models] Add missing pos_label to sklearn.metrics.precision_recall_curve in mlflow.evaluate() (#6854, @dbczumar)
  • [SQLAlchemy] Fix a bug in SqlAlchemyStore where set_tag() updates the incorrect tags (#7027, @gabrielfu)

Documentation updates:

  • [Models] Update details regarding the default Keras serialization format (#7022, @balvisio)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.