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Looking at the dependencies

You will notice that we have defined two requirements files, image-requirements.txt and local-requirements.txt:




-r image-requirements.txt

We use two requirements files because the workflow needs to be able to run in two different environments:

  • Remotely on Union or the local demo cluster on your machine
  • Locally in your Python environment

When deployed to Union (or your local demo cluster), each task in a Union workflow runs inside a container. The image-requirements.txt file includes all (and only) the packages needed to run the workflow in the container. This file is used to define the container image through the ImageSpec object (which we will see when we look at the Python code).

The local-requirements.txt file includes the contents of image-requirements.txt and adds the flytekit and flytekitplugins-env packages.

The flytekit package is needed in your local requirements file to define workflows and tasks and to provide the pyflyte CLI. (Though you may already have installed flytekit, it is good practice to have it listed in this file as well.) flytekit is not need in the image-requirements.txt file because the task container image is based on an image that already includes flytekit (you will see this in the ImageSpec definition later).

The flytekitplugins-env package is needed in your local requirements file because it provides (together with your local Docker installation) the functionality to build and push the container image defined by the ImageSpec. flytekitplugins-env is not needed in the image-requirements.txt file because it is not needed in the container image itself.

The image-requirements.txt file includes the pandas and scikit-learn packages, which are used by the task code in the following example project. These dependencies are needed both locally and in the container image.