mlflow

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 in that can 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.

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

For help or questions about MLflow usage (e.g. “how do I do X?”) see the docs or Stack 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 on Slack at https://tinyurl.com/mlflow-slack.

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, specifying a backend store via the --backend-store-uri option. 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 https://github.com/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_logistic_regression/train.py that 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/invocations

Contributing

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

Release files for mlflow 1.4.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mlflow 1.4.0
File	Size	Uploaded
mlflow-1.4.0.tar.gz	14.6 MB	Oct 31, 2019	Details

Built distribution (wheel)

Table of built distributions (wheels) for mlflow 1.4.0
File	Interpreter	ABI	Platform	Reset
mlflow-1.4.0-py2-none-any.whl 14.8 MB Oct 31, 2019	Python 2	none	any	Details

Total release size: 29.3 MB

Release files / mlflow-1.4.0.tar.gz

Download URL	mlflow-1.4.0.tar.gz
Size	14.6 MB
Tags	Source
SHA-256 checksum How to use checksums	`9116d82be380c32fa465049d14b217c4c200ad11614f4c6674e6b524b2935206`
BLAKE2b-256 checksum How to use checksums	`4fb5675b32531c43f0f1445d727b63bc88b3f30467af9e27d0e7d4a243758dbb`
Upload date	Oct 31, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`Python-urllib/2.7`

Release files / mlflow-1.4.0-py2-none-any.whl

Download URL	mlflow-1.4.0-py2-none-any.whl
Size	14.8 MB
Tags	Python 2
SHA-256 checksum How to use checksums	`87ac6cc9087af58b6bca7f002832c2f10de83a50c5f8d5aeaa50ad379ac7eb37`
BLAKE2b-256 checksum How to use checksums	`1f2ec9b90797a6558ae5718866fbc5ae1e7c313d574195515ac34931ec53611e`
Upload date	Oct 31, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`Python-urllib/2.7`