Environment Configuration#

The EnvironmentParams class lets you manage configurations such as Docker image setups, package installations, and other dependencies for your components.

Class Details#

EnvironmentParams provides the following attributes:

  • base_image (str): Base Docker image for the runtime environment.

  • target_image (str): Final Docker image to build.

  • packages_to_install (List[str]): Additional Python packages to install.

  • pip_index_urls (List[str]): Custom Python package sources.

  • install_kfp_package (bool): Whether to include the Kubeflow Pipelines SDK.

Example Usage#

Here’s an example:

from ml_orchestrator.env_params import EnvironmentParams

params = EnvironmentParams(
    base_image="python:3.9",
    packages_to_install=["pandas", "numpy"],
    kfp_package_path="https://github.com/kubeflow"
)

print(params.comp_vars())