@Marvin provide an example of overriding a vertex-ai work pool's base job template using

job_variables

in

flow.deploy

To override a Vertex AI work pool's base job template using

job_variables

in

flow.deploy

, you can use the

job_variables

argument of the Python

flow.deploy

method. Here is an example to illustrate this:

python
from prefect import flow
import os

@flow
def my_flow():
    # Your flow logic here
    pass

if __name__ == "__main__":
    my_flow.deploy(
        name="my-vertex-ai-deployment",
        work_pool_name="vertex-ai",  # This should be your Vertex AI work pool name
        job_variables={
            "env": {
                "EXECUTION_ENVIRONMENT": os.environ.get("EXECUTION_ENVIRONMENT", "local"),
                "MY_NOT_SO_SECRET_CONFIG": os.environ.get("MY_NOT_SO_SECRET_CONFIG")
            },
            "stream_output": False  # Example to turn off streaming output
        }
    )

### References 1. Overriding Job Variables 2. Work Pools Concept 3. Vertex AI Integration This script deploys a flow called

my_flow

to a Vertex AI work pool, with specific job variables set in the deployment. Adjust the variables and flow logic as necessary to fit your use case.