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Developing locally in a Docker container

Using Docker containers for local development gets rid of many of the problems of conflicting versions of Python and libraries on development machines, which can be the cause of irritating snags when trying to set up your development environment. In the Dockerized environment, all of the code is running in an isolated virtual environment (a little like a virtual machine, but more efficient in terms of resources) and so there are no version conflicts. The Docker container closely resembles our deployment environment, so it also helps to prevent "Well It Works On My Machine"-type deployment difficulties.

Running the API server locally in Docker

git clone the server repository, if you haven't already, to a suitable place on your local machine.

git clone https://github.com/rcpch/digital-growth-charts-server.git

Scripts

The s/ folder contains some simple scripts to help with development. To run them, ensure they are made executable in your filesystem (they may not be by default depending on your OS).

You can do that in whatever File > Permissions > Make Executable menu your desktop provides, or for *nix environments or the WSL you can type chmod +x <filename> to add executable permissions.

Run all scripts from the root of the project, or they won't work.

Build the Docker image with all required dependencies

run the s/build-docker script which will build the Docker image with all the required dependencies

This is useful for rapidly getting a development environment set up. It pulls the python Docker base image, deletes any existing identically-named images, and builds the new image with the server code linked into it.

Start the Docker container

run the s/start-docker script, which will run the image in a Docker container.

The dGC server will then be running in development mode in the container, and will be available at https://localhost:5000