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Using uv in GitLab CI/CD

Using the uv image

Astral provides Docker images with uv preinstalled. Select a variant that is suitable for your workflow.

gitlab-ci.yml
variables:
  UV_VERSION: 0.5
  PYTHON_VERSION: 3.12
  BASE_LAYER: bookworm-slim

stages:
  - analysis

uv:
  stage: analysis
  image: ghcr.io/astral-sh/uv:$UV_VERSION-python$PYTHON_VERSION-$BASE_LAYER
  script:
    # your `uv` commands

Note

If you are using a distroless image, you have to specify the entrypoint: 

uv:
  image:
    name: ghcr.io/astral-sh/uv:$UV_VERSION
    entrypoint: [""]
  # ...

Caching

Persisting the uv cache between workflow runs can improve performance.

uv-install:
  variables:
    UV_CACHE_DIR: .uv-cache
  cache:
    - key:
        files:
          - uv.lock
      paths:
        - $UV_CACHE_DIR
  script:
    # Your `uv` commands
    - uv cache prune --ci

See the GitLab caching documentation for more details on configuring caching.

Using uv cache prune --ci at the end of the job is recommended to reduce cache size. See the uv cache documentation for more details.

Using uv pip

If using the uv pip interface instead of the uv project interface, uv requires a virtual environment by default. To allow installing packages into the system environment, use the --system flag on all uv invocations or set the UV_SYSTEM_PYTHON variable.

The UV_SYSTEM_PYTHON variable can be defined in at different scopes. You can read more about how variables and their precedence works in GitLab here

Opt-in for the entire workflow by defining it at the top level:

gitlab-ci.yml
variables:
  UV_SYSTEM_PYTHON: 1

# [...]

To opt-out again, the --no-system flag can be used in any uv invocation.

When persisting the cache, you may want to use requirements.txt or pyproject.toml as your cache key files instead of uv.lock.