Installing and running Loud ML with Dockeredit

The official Docker image for Loud ML can be downloaded from our DockerHub repo. It can be used to install Loud ML on any operating system that supports Docker containers.

To install Loud ML Community version, run the command below:

docker pull loudml/community

To run the image, and in particular the loudmld process giving access to the HTTP API you can run:

docker run -ti -p 8077:8077 -v $VOLUME/etc/loudml:/etc/loudml  -v $VOLUME/var/lib/loudml:/var/lib/loudml:rw loudml/community

loudmld stores all training information to /var/lib/loudml directory. This directory is local to the container and you can define $VOLUME in the above shell command to keep these files on your host.


Docker users can use more advanced settings thanks to docker-compose

An example for your reference is available in the loudml Github repository, directory docker-compose/. You can fork the example and create your own as required.

Directory layout of Docker containeredit

The Docker image places config files, logs, and the data directory in the appropriate locations for a Debian-based system:

Type Description Default Location Setting


Loud ML home directory or $LM_HOME



Configuration files including config.yml




Environment variables including heap size, file descriptors.



The location of the data files of each model defined on the node.



Next stepsedit

You now have a test Loud ML environment set up. Before you start serious engineering go into production with Loud ML, you will need to do some additional setup: