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Showing content from https://docs.gitlab.com/development/python_guide/deployment/ below:

Deploying Python repositories | GitLab Docs

We deploy libraries and utilities to pypi with the gitlab user using poetry. Configure the deployment in the pyproject.toml file:

[tool.poetry]
name = "gitlab-<your package name>"
version = "0.1.0"
description = "<Description of your library/utility>"
authors = ["gitlab"]
readme = "README.md"
packages = [{ include = "<your module>" }]
homepage = ""https://gitlab.com/gitlab/<path/to/repository>"
repository = "https://gitlab.com/gitlab/<path/to/repository>"

Refer to poetry’s documentation for additional configuration options.

To configure deployment of the PyPI package:

1. Authenticate to PyPI using the “PyPI GitLab” credentials found in 1Password (PyPI does not support organizations as of now).

2. Create a token under Account Settings > Add API Tokens.

3. For the initial publish, select Entire account (all projects) scope. If the project already exists, scope the token to the specific project.

4. Configure credentials:

   Locally:

   poetry config pypi-token.pypi <your-api-token>

   To configure deployment with CI, set the POETRY_PYPI_TOKEN_PYPI to the token created. Alternatively, define a trusted publisher for the project, in which case no token is needed.

5. Use Poetry to publish your package:

Services for GitLab.com, GitLab Dedicated and self-hosted customers using CloudConnect are deployed using Runway. Refer to the project documentation on how to add or manage Runway services.

Deploying services to self-hosted environments poses challenges as services are not part of the monolith. Currently, Runway does not support self-hosted instances, and Omnibus does not support Python services, so deployment is only possible by users pulling the service image.

1. Use a different user than the root user
2. Configure poetry variables correctly to avoid runtime issues
3. Use multi-stage Docker builds images to create lighter images

Example of image

Self-hosted customers need to know which version of the service is compatible with their GitLab installation. Python services do not make use of managed versioning, so each service needs to handle its versioning and release cuts.

If a service is accessible through cloud-connector, it must adhere to GitLab Statement Support, providing stable deployments for the current and previous 2 majors releases of GitLab.

When supporting self-hosted deployment, it’s important to have a version tag that matches GitLab versions, making it easier for users to configure the different components of their environment. Add a pipeline to GitLab the GitLab release process that tags the service repo with the same tag, which will then trigger a pipeline to create an image with the defined tag.

Example: a pipeline on GitLab creates a tag on AI Gateway that releases a new image.

Supporting 3 major versions can lead to a confusing codebase due to too many code paths. An alternative to keep support while allowing code clean ups is to provide deployments for multiple versions of the service. For example, suppose GitLab is on version 19.5, this would need three deployments of the service:

• One for service version 17.11, which provides support for all GitLab 17.x versions
• One for service version 18.11, which provides support for all GitLab 18.x versions
• One for service version 19.5, which provides support for GitLab versions 19.0-19.5.

Once version 18.0 is released, unused code from versions 17.x can be safely removed, since a legacy deployment will be present. Then, once version 20.0 is released, and GitLab version 17.x is not supported anymore, the legacy deployment can also be removed.

Images must be published in the container registry of the project.

It’s also recommend to publish the images on DockerHub. To create an image repository on Docker Hub, create an account with your GitLab handle and create an Access Request to be added to the GitLab organization. Once the image repository is created, make sure the user gitlabcibuild has read and write access to the repository.

To be added.

Deployment of Python services on GitLab Dedicated is not currently supported

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