The following project creates a docker container running mlflow server. The current version is version 1.19.0 of Mlflow. An integration for postgresql has been foreseen to store models inside mlflow server.
- docker engine
- docker compose
If you are new to docker you will first need to install docker on your machine. If the installation is finished, you will need to clone this repo to your local machine. Once it has been cloned you can build the docker with the following command:
docker build -t mlflowserver -f Dockerfile . --no-cache
If the build was successful you can start the container with the following command:
docker run -p 5000:5000 --env MLFLOW_BACKEND_STORE_URI=<backendstore> -it mlflowserver:latestIn this case the backend store would be a valid connection string towards the database you are running.
If you are unsure how to achieve this, there is also a docker-compose.yml file that spins up both postgress db and
mlflow server, giving you a full working example.
If you want to run the docker compose example run the following command:
docker-compose -f docker-compose.yml up --build -dOne of the options you can configure is the artifact root.
By default this will be set to a path inside the docker container, in a location ./mlruns.
This also means that if the container dies, that your artifacts will be gone.
There are a couple of options for remote artifact root:
- AWS S3 => eg: s3://
- Azure Blob store => eg: "wasbs://poc@mflowpoc2.blob.core.windows.net"
- Google Cloud Storage
- ....
If you run the docker local the website will be available from http://localhost:5000
All documentation with regards to mlflow can be found here: https://mlflow.org/docs/latest/index.html
If you want to contribute, please abide by the following rules:
Create a feature branch and add your changes Create a pull request to merge into master Have a reviewer and merge