Easily Docker-ize your build/development environment and seamlessly run commands inside it.
dockerized is a tool for seamlessly executing commands in a container. It takes care of the details so you can run a command in a container as if it was running on your machine - just prepend any command with dockerized exec to have it run in the container.
See https://benzaita.github.io/dockerized-cli/index.html
Install dockerized:
$ pip install dockerized
Initialize your environment:
$ dockerized init
$ echo FROM python:3.9 > .dockerized/Dockerfile.dockerized
or use an example:
$ dockerized init --from https://github.com/benzaita/dockerized-example-python.git
Then run a command inside that environment:
$ dockerized exec python --version
...
Python 3.9.0
Or drop into an interactive shell inside the environment:
$ dockerized shell
# python --version
Python 3.9.0
See the examples wiki page.
docker run or docker exec?Fair question! After all dockerized is just a wrapper for Docker. You can definitely use docker run or docker exec but there are a few details you'd have to take care of:
Rebuilding the Docker image: after changing the Dockerfile, you need to run docker build before running docker run again. When iterating on the Dockerfile this can become a pain.
With dockerized you just do dockerized exec.
Volumes and working directory: to allow the developer to run commands from arbitrary locations within the project, you probably want to mount the project root into the container. Manually running docker run -v $PWD:... one time and docker run -v $PWD/..:... another time, or adding some script to do this for you.
With dockerized you just do dockerized exec.
Running Docker Compose: almost every project has integration tests. Running them locally usually means running Docker Compose. Now you need to run docker-compose up before running docker run. Besides being annoying, see also "Port contamination" below.
With dockerized you just do dockerized exec.
"Port contamination": many people run their tests on the host, against dependencies (think PostgreSQL for example) running in containers. Since the tests need to access the PostgreSQL port, they expose this port to the host. When you are working on multiple projects these exposed ports start conflicting and you have to docker-compose stop one project before docker-compose start the other.
With dockerized you just do dockerized exec.
$ claude mcp add dockerized-cli \
-- python -m otcore.mcp_server <graph>