ng the server environment.

Execution EnvironmentsWhen running locally, there is no real concept of an execution environment as the client, server, and execution environment are all the same. However, when running a pipeline remotely, ZenML needs to transfer your code and environment over to the remote orchestrator. In order to achieve this, ZenML builds Docker images known as execution environments.

ZenML handles the Docker image configuration, creation, and pushing, starting with a base image containing ZenML and Python, then adding pipeline dependencies. To manage the Docker image configuration, follow the steps in the containerize your pipeline guide, including specifying additional pip dependencies, using a custom parent image, and customizing the build process.

The execution environments do not need to be built each time a pipeline is run - you can reuse builds from previous runs to save time.

Image Builder Environment

By default, execution environments are created locally in the client environment using the local Docker client. However, this requires Docker installation and permissions. ZenML offers image builders, a special stack component, allowing users to build and push Docker images in a different specialized image builder environment.

Note that even if you don't configure an image builder in your stack, ZenML still uses the local image builder to retain consistency across all builds. In this case, the image builder environment is the same as the client environment.

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Last updated 15 days ago