How to set up Python on Compute Engine

How to set up Python on Compute Engine – Kristina Chodorow's BlogSkip to content

This is a followup to my previous post on setting up big files on GCP. I ran into similar problems with Python as I did with static files, but my solution was a bit different.

The right wayTM of running Python on GCP seems to be via a docker container. However, adding a virtual environment to a docker container is painful: for anything more than a small number of dependencies and the docker image becomes too unwieldy to upload. Thus, I decided to keep my virtual environment on a separate disk in GCP and mount it as a volume on container startup. This keeps the Python image svelte and the virtual environment static, both good things! It does mean that they can get out of sync: technically I should probably be setting up some sort of continuous deployment. However, I don’t want spend the rest of my life setting up ops stuff, so let’s go with this for now.

To create a separate disk, follow the instructions in the last post for creating and attaching a disk to your GCP instance. Make sure you mark the disk read/write, since we’re going to install a bunch of packages.

Start up the instance and mount your disk (I’m calling mine vqgan_models, because sharing is caring).

On your development environment, scp your requirements.txt file over to GCP:

gcloud compute scp requirements.txt vqgan-clip:/mnt/disks/vqgan_models/python_env/requirements.txt

Here’s where things get a little tricky, so here’s a high-level view of what we’re doing:

1. Create a “scratch” Docker instance.
2. Add our persistent disk to the container in such a way that it mimics what out prod app will look like.
3. Install Python dependencies.

Virtual environments are not relocatable, so we need to make the virtual environment directory match what prod will look like. For instance, I’ll be running my python app in /app with a virtual environment /app/.venv. Thus, I am going to mount my persistent disk to /app in the scratch docker container:

docker run -v /mnt/disks/vqgan_models/python_env:/app -it python:3.10-slim bash

This will put you in a bash shell in a python environment container. Everything your create in /app will be saved to the persistent disk.

Note: when you want to leave, exit by hitting Ctrl-D! Typing “exit” seemed to cause changes in the volume not to actually be written to the persistent disk.

Now you can create a virtual environment that will match your production environment:

# Shell starts in /
$ cd /app
$ python3 -m venv .venv
$ . .venv/bin/activate
$ pip install -r requirements.txt

Hit Ctrl-D to exit the scratch docker instance. Shut down your instance so you can change your docker volumes. Go to Container -> Change -> Volume mounts and set the Mount path to /app/.venv and the Host path to /mnt/disks/vqgan_models/python_env/.venv.

On you development machine, set up a Dockerfile that copies your source code and then activates your virtual environment before starting your service:

FROM python:3.10-slim
WORKDIR /app
COPY mypkg ./mypkg
CMD . .venv/bin/activate && python -m mypkg.my_service

Build and push your image:

$ export BACKEND_IMAGE="${REGION}"-docker.pkg.dev/"${PROJECT_ID}"/"${BACKEND_ARTIFACT}"/my-python-app
$ docker build --platform linux/amd64 --tag "${BACKEND_IMAGE}" .
$ docker push "${BACKEND_IMAGE}"

Now start up your GCP instance and make sure it’s running by checking the docker logs.

$ export CID=$(docker container ls | tail -n 1 | cut -f 1 -d' ')
$ docker logs $CID
...
I0917 01:24:51.654180 139988588971840 my_service.py:119] Ready to serve

Now you can quickly upload new versions of code without hassling with giant Docker containers.

Note: I am a newbie at these technologies, so please let me know in the comments if there are better ways of doing this!