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MLflow 0.8.2

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 0.8.2!

MLflow 0.8.2 is a patch release on top of 0.8.1 containing bug fixes and documentation updates. Please visit the change log to read more about the fixes and updates introduced in this release.

MLflow 0.8.1

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 0.8.1! MLflow 0.8.1 introduces several significant improvements:

  • Improved UI responsiveness and load time, especially when displaying experiments containing hundreds to thousands of runs.

  • Improved visualizations, including interactive scatter plots for MLflow run comparisons.

  • Expanded support for scoring Python models as Spark UDFs. For more information, see the updated documentation for this feature.

  • By default, saved models will now include a Conda environment specifying all of the dependencies necessary for loading them in a new environment.

  • MLflow projects can now be run from ZIP files.

The release includes additional bugfixes and improvements across the Python client, tracking UI, and documentation. Visit the change log to read more about the new features.

MLflow 0.8.0

· 2 min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 0.8.0! MLflow 0.8.0 introduces several major features:

  • Dramatically improved UI for comparing experiment run results:

    • Metrics and parameters are by default grouped into a single column, to avoid an explosion of mostly-empty columns. Individual metrics and parameters can be moved into their own column to help compare across rows.
    • Runs that are "nested" inside other runs (e.g., as part of a hyperparameter search or multistep workflow) now show up grouped by their parent run, and can be expanded or collapsed altogether. Runs can be nested by calling mlflow.start_run or mlflow.run while already within a run.
    • Run names (as opposed to automatically generated run UUIDs) now show up instead of the run ID, making comparing runs in graphs easier.
    • The state of the run results table, including filters, sorting, and expanded rows, is persisted in browser local storage, making it easier to go back and forth between an individual run view and the table.

  • Support for deploying models as Docker containers directly to Azure Machine Learning Service Workspace (as opposed to the previously-recommended solution of Azure ML Workbench).

The release also includes bugfixes and improvements across the Python and Java clients, tracking UI, and documentation. Visit the change log to read more about the new features.

MLflow 0.7.0

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 0.7.0!

MLflow 0.7.0 introduces several major features:

The release also includes bugfixes and improvements across the Python and Java clients, tracking UI, and documentation. Visit the change log to read more about the new features.

MLflow 0.6.0

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 0.6.0!

MLflow 0.6.0 introduces several major features:

  • A Java client API (to be published on Maven within the next day or two)
  • Support for saving and serving SparkML models as MLeap for low-latency serving
  • Support for tagging runs with metadata, during and after the run completion
  • Support for deleting (and restoring deleted) experiments

In addition to these features, there are a host of improvements and bugfixes to the REST API, Python API, tracking UI, and documentation. Visit the change log to read more about the new features.

MLflow 0.5.2

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 0.5.2!

MLflow 0.5.2 is a patch release on top of 0.5.1 containing only bug fixes and no breaking changes or features.

Visit the change log to read about the new features.

MLflow 0.5.1

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 0.5.1!

MLflow 0.5.1 is a patch release on top of 0.5.0 containing only bug fixes and no breaking changes or features.

Visit the change log to read about the new features.

MLflow 0.5.0

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 0.5.0!

MLflow 0.5.0 offers some major improvements:

  • Keras and PyTorch first-class support as models
  • SFTP support as an artifactory
  • A new scatterplot visualization to compare runs
  • A more complete Python SDK for experiment and run management

Visit the change log to read about the new features.

MLflow 0.4.2

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 0.4.2!

MLflow 0.4.2 offers some improvements and minor bug fixes:

  • MLflow experiments REST API and mlflow experiments create now support providing --artifact-location
  • [UI] Runs can now be sorted by columns, and added a Select All button
  • Databricks File System (DBFS) artifactory support added
  • databricks-cli version upgraded to >= 0.8.0 to support new DatabricksConfigProvider interface

Visit the change log to read about the new features.

MLflow 0.4.1

· One min read
MLflow maintainers
MLflow maintainers
MLflow maintainers

We are happy to announce the availability of MLflow 0.4.1!

MLflow 0.4.1 offers some improvements and minor bug fixes:

  • [Projects] MLflow will use the conda installation directory given by the $MLFLOW_CONDA_HOME if specified (e.g. running conda commands by invoking "$MLFLOW_CONDA_HOME/bin/conda"), defaulting to running "conda" otherwise.
  • [UI] Show GitHub links in the UI for projects run from http(s):// GitHub URLs (#235, @smurching)

Visit the change log to read about the new features.