This guide assumes you have Docker
and docker-compose
installed
and setup to run as non-root user following the instructions
here,
here and
here.
- Clone the repository.
- Download the data and place it in a
data/
directory at the root of the repository. - Navigate to the
docker/
directory. - Run
export UID=$(id -u)
and thenexport GID=$(id -g)
. - Run
docker-compose up --build
which will build the image, run a container and launch a Jupyter server on port4242
. - Use the link in the Jupyter command output to access any of the several notebooks for EDA, Training, Inference and Error Analysis.
- If you would like to run the CLI interface, use
docker-compose run ml-fuel bash
to launch an interactive terminal. - You can now run
pre-processing.py
,train.py
ortest.py
located in thesrc/
directory. Check the docs for more details.
The steps above mount the local code repository and data directory to a volume on the container, setting up the correct permissions so that you can keep any pretrained models or inference files even after the container is shut down.