Configure Python environments
Navigating multiple development environments.
Last updated
Navigating multiple development environments.
Last updated
ZenML deployments often involve multiple environments. This guide helps you manage dependencies and configurations across these environments.
Here is a visual overview of the different environments:
The client environment (sometimes known as the runner environment) is where the ZenML pipelines are compiled, i.e., where you call the pipeline function (typically in a run.py script). There are different types of client environments:
A local development environment
A CI runner in production.
A ZenML Pro runner.
A runner image orchestrated by the ZenML server to start pipelines.
In all the environments, you should use your preferred package manager (e.g., pip or poetry) to manage dependencies. Ensure you install the ZenML package and any required integrations.
The client environment typically follows these key steps when starting a pipeline:
Compiling an intermediate pipeline representation via the @pipeline function.
Creating or triggering pipeline and step build environments if running remotely.
Triggering a run in the orchestrator.
Please note that the @pipeline function in your code is only ever called in this environment. Therefore, any computational logic that is executed in the pipeline function needs to be relevant to this so-called compile time, rather than at execution time, which happens later.
The ZenML server environment is a FastAPI application managing pipelines and metadata. It includes the ZenML Dashboard and is accessed when you deploy ZenML. To manage dependencies, install them during ZenML deployment, but only if you have custom integrations, as most are built-in.
See also here for more on configuring the server environment.
When 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.
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.
