Training and Inference using Docker

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.

Steps

• 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=$(id -u) and then export GID=$(id -g).

• Run docker-compose up --build which will build the image, run a container and launch a Jupyter server on port 4242.

• 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 or test.py located in the src/ 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.