Note: The current version of MLflow is an alpha 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.
Official documentation for MLflow can be found at https://mlflow.org/docs/latest/index.html.
The programs in example
use the MLflow Tracking API. For instance, run:
python example/quickstart/test.py
This program will use MLflow Tracking API,
which logs tracking data in ./mlruns
. This can then be viewed with the Tracking UI.
The MLflow Tracking UI will show runs logged in ./mlruns
at http://localhost:5000.
Start it with:
mlflow ui
The mlflow run
command lets you run a project packaged with a MLproject file from a local path
or a Git URI:
mlflow run example/tutorial -P alpha=0.4 mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.4
See example/tutorial
for a sample project with an MLproject file.
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
example/quickstart/test_sklearn.py
that you can run as follows:
$ python example/quickstart/test_sklearn.py Score: 0.666 Model saved in run <run-id> $ mlflow sklearn serve -r <run-id> 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.