Package: blind
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code
into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be
used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you
currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow’s current components are:MLflow Tracking: An API to log parameters, code, and
results in machine learning experiments and compare them using an interactive UI.MLflow Projects: A code packaging format for reproducible
runs using Conda and Docker, so you can share your ML code with others.MLflow Models: A model packaging format and tools that let
you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as
Docker, Apache Spark, Azure ML and AWS SageMaker.MLflow Model Registry: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.InstallingInstall MLflow from PyPI viapip install mlflowMLflow requirescondato be on thePATHfor the projects feature.Nightly snapshots of MLflow master are also availablehere.DocumentationOfficial documentation for MLflow can be found athttps://mlflow.org/docs/latest/index.html.CommunityFor help or questions about MLflow usage (e.g. “how do I do X?”) see thedocsorStack Overflow.To report a bug, file a documentation issue, or submit a feature request, please open a GitHub issue.For release announcements and other discussions, please subscribe to our mailing list (mlflow-users@googlegroups.com)
or join us onSlack.Running a Sample App With the Tracking APIThe programs inexamplesuse the MLflow Tracking API. For instance, run:python examples/quickstart/mlflow_tracking.pyThis program will useMLflow Tracking API,
which logs tracking data in./mlruns. This can then be viewed with the Tracking UI.Launching the Tracking UIThe MLflow Tracking UI will show runs logged in./mlrunsathttp://localhost:5000.
Start it with:mlflow uiNote:Runningmlflow uifrom 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,
specifying a backend store via the--backend-store-urioption. Alternatively, see
instructions for running the dev UI in thecontributor guide.Running a Project from a URIThemlflow runcommand 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=0.4Seeexamples/sklearn_elasticnet_winefor a sample project with an MLproject file.Saving and Serving ModelsTo illustrate managing models, themlflow.sklearnpackage can log scikit-learn models as
MLflow artifacts and then load them again for serving. There is an example training application inexamples/sklearn_logistic_regression/train.pythat you can run as follows:$ python examples/sklearn_logistic_regression/train.py
Score: 0.666
Model saved in run <run-id>

$ mlflow models serve --model-uri runs:/<run-id>/model

$ curl -d '{"columns":[0],"index":[0,1],"data":[[1],[-1]]}' -H 'Content-Type: application/json'  localhost:5000/invocationsContributingWe happily welcome contributions to MLflow. Please see ourcontribution guidefor details.