ater stages if more pipelines are decorated there.Pipeline names are their unique identifiers, so using the same name for different pipelines will create a mixed history where two versions of a pipeline are two very different entities.

.dockerignore

Containerized orchestrators and step operators load your complete project files into a Docker image for execution. To speed up the process and reduce Docker image sizes, exclude all unnecessary files (like data, virtual environments, git repos, etc.) within the .dockerignore.

Dockerfile (optional)

By default, ZenML uses the official zenml Docker image as a base for all pipeline and step builds. You can use your own Dockerfile to overwrite this behavior. Learn more here.

Notebooks

Collect all your notebooks in one place.

.zen

By running zenml init at the root of your project, you define the project scope for ZenML. In ZenML terms, this will be called your "source's root". This will be used to resolve import paths and store configurations.

Although this is optional, it is recommended that you do this for all of your projects.

All of your import paths should be relative to the source's root.

run.py

Putting your pipeline runners in the root of the repository ensures that all imports that are defined relative to the project root resolve for the pipeline runner. In case there is no .zen defined this also defines the implicit source's root.

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Last updated 19 days ago