GitHub - mlflow/mlflow: Open source platform for the machine learning lifecycle
source link: https://github.com/mlflow/mlflow
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README.rst
MLflow Alpha Release
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.
Installing
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.
Documentation
Official documentation for MLflow can be found at https://mlflow.org/docs/latest/index.html.
Community
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.
Running a Sample App With the Tracking API
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.
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 [email protected]:mlflow/mlflow-example.git -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/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> model $ curl -d '[{"x": 1}, {"x": -1}]' -H 'Content-Type: application/json' -X POST localhost:5000/invocations
Contributing
We happily welcome contributions to MLflow. Please see our contribution guide for details.
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