I need help setting up continuous integration for my Python machine learning project. I’m working with TensorFlow and OpenAI Gym libraries and want to create a proper CI/CD pipeline on GitLab.
I’ve used GitLab’s default CI templates for other programming languages before, but I can’t find any specific examples for Python projects that use machine learning frameworks like TensorFlow. The regular Python templates seem too basic for ML projects.
What would be the best approach to configure the .gitlab-ci.yml file? I’m particularly concerned about handling the dependencies and making sure TensorFlow installs correctly in the CI environment. Should I use Docker images or virtual environments? Any specific considerations for Gym environments during testing?
I’ve been running TensorFlow on GitLab CI for two years - definitely go with Docker images instead of virtual environments. Way more predictable and faster builds since you can use pre-built images with TensorFlow already there. I use tensorflow/tensorflow:latest-py3 as my base and add other dependencies in a separate stage. This completely solved the CUDA compatibility nightmares I had with custom environments. For Gym testing, you’ll need a headless display with xvfb-run for visual environments. I add apt-get install -y xvfb to before_script and wrap tests with xvfb-run -a python -m pytest. Handles most rendering issues you’ll hit. Watch out for memory usage during model training in tests - that caught me early on. Use smaller datasets or mock the heavy stuff, or your CI jobs will timeout.
Docker’s definitely the right choice, but skip the official TensorFlow images - they’re bloated and slow everything down. I build my own slim image with just Python 3.9, TensorFlow CPU, and Gym dependencies. Cuts build time in half.
For CI, I use three stages: build dependencies, run tests, deploy artifacts. With Gym, you’ve got to nail the headless setup - not just xvfb, but set DISPLAY properly and make sure tests don’t try rendering anything.
Cache your pip dependencies between runs if you can. TensorFlow takes forever to install otherwise. And here’s something that bit me hard - always pin your exact TensorFlow version, not just major. Minor updates will randomly break your models in CI.