Example template to use Conda + Docker for reproducible, easy to deploy models.

Blog post goes into more detail - find it here:

https://binal.pub/2018/10/data-science-with-docker-and-conda/

How to Use This All

As an example - here's my normal development process. Using it I can get from development to production with little friction, knowing that my code will work as expected, and that it won't negatively affect other processes on the production server.

Developing and Packaging

1. Clone the template down. Update the environment.yml as needed with packages I know I'll need, and run docker-compose build. This will build the development image with all the packages I defined installed within it.
2. Create a .env_dev file with development environment variables
3. Run docker-compose up and navigate to JupyterLab, which will be running on http://localhost:8888. We can access it by entering in the token local_dev.
4. From there prototype and develop a model/process using Jupyter Notebooks, saving any notebooks I create along the way into /notebooks as a development diary. Any final artifacts/models I plan on using in production I save within /code.
5. Once I have a final version of my code, save it (and any models it relies on) into /code.
6. Update the docker-compose.prod.yml file's command section to point to the my scripts' name, and the image section to point to my docker registry (something like my_registry/my_project:0.1).
7. Run docker-compose -f docker-compose.prod.yml build - this builds the production version of the image, packaging everything in the /code and /notebooks directories directly onto the image.
8. Run docker-compose -f docker-compose.prod.yml push which pushes that packaged image into my organizations docker registry.

At this point I now have an image that contains all my code, models, and other artifacts I need, that's preinstalled with exact versions of the Python packages and dependencies I require. It's stored in a central location where I can easily pull it down onto other servers.