Have a containerized development environment for projects that use some of the most common libraries and utilities for AI, ML, etc. This makes possible to have an independent and upgradable environment that can be shared between projects or server.
This is an initial point that may be adapted to specific needs. Check the
Dockerfile to see what will be installed. By default installs GPU support for most libraries as it’s based on GPU enabled Tensorflow image. Packages for R are commented but you can uncomment them.
New software installed in the container will be lost as it runs as a ephemeral container. If you will use it, add to the
Dockerfile and rebuild.
- Build the docker image
docker build -t ai:latest .
- Create or move to you project directory. You may want to create any directory used by the container. It will create them but will be owned by root.
- Run the environment:
./denv.shto run a shell in the environment
./denv_jupyter.shto run a jupyter notebook server within the environment