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 https://mlflow.org/docs/latest/index.html.
To discuss MLflow or get help, please subscribe to our mailing list ([email protected]) or join us on Slack at https://tinyurl.com/mlflow-slack.
To report bugs, please use GitHub issues.
The programs in examples use the MLflow Tracking API. For instance, run:
python examples/quickstart/mlflow_tracking.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
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.
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 https://github.com/mlflow/mlflow-example.git -P alpha=0.4
See examples/sklearn_elasticnet_wine 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
examples/sklearn_logisitic_regression/train.py that you can run as follows:
$ python examples/sklearn_logisitic_regression/train.py
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.