Manage Docker Images

How ZenML uses Docker images to run your pipeline

This is an older version of the ZenML documentation. To read and view the latest version please visit this up-to-date URL.

When running locally, ZenML will execute the steps of your pipeline in the active Python environment. When using a remote orchestrators or step operators instead, ZenML builds Docker images to transport and run your pipeline code in an isolated and well-defined environment. For this purpose, a Dockerfile is dynamically generated and used to build the image using the local Docker client. This Dockerfile consists of the following steps:

  • Starts from a base image which needs to have ZenML installed. By default, this will use the official ZenML image for the Python and ZenML version that you're using in the active Python environment. If you want to use a different image as the base for the following steps, check out this guide.

  • Installs additional pip dependencies. ZenML will automatically detect which integrations are used in your stack and install the required dependencies. If your pipeline needs any additional requirements, check out our guide on including custom dependencies.

  • Copies your active stack configuration. This is needed so that ZenML can execute your code on the stack that you specified.

  • Copies your source files. These files need to be included in the Docker image so ZenML can execute your step code. Check out this section for more information on which files get included by default and how to exclude files.

Which files get included

ZenML will try to determine the root directory of your source files in the following order:

  • If you've created a ZenML repository for your project, the repository directory will be used.

  • Otherwise, the parent directory of the python file you're executing will be the source root. For example, running python /path/to/, the source root would be /path/to.

By default, ZenML will copy all contents of this root directory into the Docker image. If you want to exclude files to keep the image smaller, you can do so using a .dockerignore file in either of the following two ways:

  • Have a file called .dockerignore in your source root directory explained above.

  • Explicitly specify a .dockerignore file that you want to use:

    def my_pipeline(...):

Customizing the build process

This process explained above is all done automatically by ZenML and covers most basic use cases. This section covers all the different ways in which you can hook into the Docker building process to customize the resulting image to your needs.

How to install additional pip dependencies

You don't need to install the zenml pip package as well as any integration that is used for components of your active stack.

If you want ZenML to install additional pip dependencies on top of the base image, you can use any of the following three ways:

  • Specify a list of ZenML integrations that you're using in your pipeline:

    from zenml.integrations.constants import PYTORCH, EVIDENTLY
    @pipeline(required_integrations=[PYTORCH, EVIDENTLY])
    def my_pipeline(...):
  • Specify a list of pip requirements in code:

    @pipeline(requirements=["torch==1.12.0", "torchvision"]))
    def my_pipeline(...):
  • Specify a pip requirements file:

    def my_pipeline(...):

You can even combine these methods, but do make sure that your list of pip requirements doesn't overlap with the ones specified by your required integrations.

Using a custom base image

To have full control over the environment which is used to execute your pipelines, you can specify a custom base image which will be used as the starting point of the Docker image that ZenML will use to execute your code. For more information on how to specify a base image for the orchestrator or step operator you're using, visit the corresponding documentation page.

If you're going to use a custom base image, you need to make sure that it has Python, pip and ZenML installed for it to work.

Last updated