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mparkhe Revert "Propagate query params in Databricks notebook/job links" (#1007)
* Revert "Propagate query params in notebook/job links (#968)"
This reverts commit 3ed00f8.

#1007 should be a direct revert of #968
Latest commit 834eba5 Mar 18, 2019
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docs Clarify API docs for LogBatch REST endpoint (#1001) Mar 14, 2019
examples Improve docs and example README for MLflow Docker projects (#970) Mar 12, 2019
tests File Store: When reading run data, return maximum metric value at max… Mar 16, 2019
.dockerignore Add missing files (.dockerignore, .travis.yml) (#12) Jun 7, 2018
CONTRIBUTING.rst Style edit and fix some formatting bugs. (#862) Feb 24, 2019
Dockerfile Recommend a simpler command to get version (#107) Jul 4, 2018
README.rst [Default envs] Add docker/sagemaker integration tests with default co… Nov 14, 2018
dev-requirements.txt Run MLProjects on docker containers (#555) Jan 18, 2019 Java SDK for MLflow (#380) Aug 28, 2018 Log the run page URL as a tag when running on Databricks (#388) Aug 29, 2018
pylintrc Updates to Projects API (#82) Jul 18, 2018 Include protobuf dependencies and use C implementation. (#74) Jun 27, 2018
test-requirements.txt Pin test requirements (#952) Mar 5, 2019


MLflow Beta Release

Note: The current version of MLflow is a beta release. This means that APIs and data formats are subject to change!

Note 2: We do not currently support running MLflow on Windows. Despite this, we would appreciate any contributions to make MLflow work better on Windows.


Install MLflow from PyPi via pip install mlflow

MLflow requires conda to be on the PATH for the projects feature.

Nightly snapshots of MLflow master are also available here.


Official documentation for MLflow can be found at


To discuss MLflow or get help, please subscribe to our mailing list ( or join us on Slack at

To report bugs, please use GitHub issues.

Running a Sample App With the Tracking API

The programs in examples use the MLflow Tracking API. For instance, run:

python examples/quickstart/

This program will use MLflow Tracking API, which logs tracking data in ./mlruns. This can then be viewed with the Tracking UI.

Launching the Tracking UI

The MLflow Tracking UI will show runs logged in ./mlruns at http://localhost:5000. Start it with:

mlflow ui

Note: Running mlflow ui from within a clone of MLflow is not recommended - doing so will run the dev UI from source. We recommend running the UI from a different working directory, using the --file-store option to specify which log directory to run against. Alternatively, see instructions for running the dev UI in the contributor guide.

Running a Project from a URI

The mlflow run command lets you run a project packaged with a MLproject file from a local path or a Git URI:

mlflow run examples/sklearn_elasticnet_wine -P alpha=0.4

mlflow run -P alpha=0.4

See examples/sklearn_elasticnet_wine for a sample project with an MLproject file.

Saving and Serving Models

To illustrate managing models, the mlflow.sklearn package can log scikit-learn models as MLflow artifacts and then load them again for serving. There is an example training application in examples/sklearn_logisitic_regression/ that you can run as follows:

$ python examples/sklearn_logisitic_regression/
Score: 0.666
Model saved in run <run-id>

$ mlflow sklearn serve -r <run-id> -m model

$ curl -d '[{"x": 1}, {"x": -1}]' -H 'Content-Type: application/json' -X POST localhost:5000/invocations


We happily welcome contributions to MLflow. Please see our contribution guide for details.

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