Override job configuration for specific deployments

There are two ways to deploy flows to work pools: with a prefect.yaml file or using the Python deploy method. In both cases, you can add or override job variables to the work pool’s defaults for a given deployment. You can override both a work pool and a deployment when a flow run is triggered.

This guide explores common patterns for overriding job variables in both deployment methods.

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Job variables

Job variables are infrastructure-related values that are configurable on a work pool. You can override job variables on a per-deployment or per-flow run basis. This allows you to dynamically change infrastructure from the work pool’s defaults.

For example, when you create or edit a work pool, you can specify a set of environment variables to set in the runtime environment of the flow run.

You could set the following value in the env field of any work pool:

{
  "EXECUTION_ENV": "staging",
  "MY_NOT_SO_SECRET_CONFIG": "plumbus",
}

Rather than hardcoding these values into your work pool in the UI (and making them available to all deployments associated with that work pool), you can override these values for a specific deployment.

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Override job variables on a deployment

Here’s an example repo structure:

» tree
.
|── README.md
|── requirements.txt
|── demo_project
|   |── daily_flow.py

With a demo_flow.py file like:

import os
from prefect import flow, task


@task
def do_something_important(not_so_secret_value: str) -> None:
    print(f"Doing something important with {not_so_secret_value}!")


@flow(log_prints=True)
def some_work():
    environment = os.environ.get("EXECUTION_ENVIRONMENT", "local")
    
    print(f"Coming to you live from {environment}!")
    
    not_so_secret_value = os.environ.get("MY_NOT_SO_SECRET_CONFIG")
    
    if not_so_secret_value is None:
        raise ValueError("You forgot to set MY_NOT_SO_SECRET_CONFIG!")

    do_something_important(not_so_secret_value)

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Use a prefect.yaml file

Imagine you have the following deployment definition in a prefect.yaml file at the root of your repository:

deployments:
- name: demo-deployment
  entrypoint: demo_project/demo_flow.py:some_work
  work_pool:
    name: local
  schedule: null

pull:
- prefect.deployments.steps.git_clone: &clone_repo
    repository: https://github.com/some-user/prefect-monorepo
    branch: main

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Hard-coded job variables

To provide the EXECUTION_ENVIRONMENT and MY_NOT_SO_SECRET_CONFIG environment variables to this deployment, you can add a job_variables section to your deployment definition in the prefect.yaml file:

deployments:
- name: demo-deployment
  entrypoint: demo_project/demo_flow.py:some_work
  work_pool:
    name: local
    job_variables:
        env:
            EXECUTION_ENVIRONMENT: staging
            MY_NOT_SO_SECRET_CONFIG: plumbus
  schedule: null

Then run prefect deploy -n demo-deployment to deploy the flow with these job variables.

You should see the job variables in the Configuration tab of the deployment in the UI:

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Use existing environment variables

To use environment variables that are already set in your local environment, you can template these in the prefect.yaml file using the {{ $ENV_VAR_NAME }} syntax:

deployments:
- name: demo-deployment
  entrypoint: demo_project/demo_flow.py:some_work
  work_pool:
    name: local
    job_variables:
        env:
            EXECUTION_ENVIRONMENT: "{{ $EXECUTION_ENVIRONMENT }}"
            MY_NOT_SO_SECRET_CONFIG: "{{ $MY_NOT_SO_SECRET_CONFIG }}"
  schedule: null

export EXECUTION_ENVIRONMENT=staging
export MY_NOT_SO_SECRET_CONFIG=plumbus

Run prefect deploy -n demo-deployment to deploy the flow with these job variables, and you should see them in the UI under the Configuration tab.

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Use the .deploy() method

If you’re using the .deploy() method to deploy your flow, the process is similar. But instead of
prefect.yaml defining the job variables, you can pass them as a dictionary to the job_variables argument of the .deploy() method.

Add the following block to your demo_project/daily_flow.py file from the setup section:

if __name__ == "__main__":
    flow.from_source(
        source="https://github.com/zzstoatzz/prefect-monorepo.git",
        entrypoint="src/demo_project/demo_flow.py:some_work"
    ).deploy(
        name="demo-deployment",
        work_pool_name="local", 
        job_variables={
            "env": {
                "EXECUTION_ENVIRONMENT": os.environ.get("EXECUTION_ENVIRONMENT", "local"),
                "MY_NOT_SO_SECRET_CONFIG": os.environ.get("MY_NOT_SO_SECRET_CONFIG")
            }
        }
    )

export EXECUTION_ENVIRONMENT=staging
export MY_NOT_SO_SECRET_CONFIG=plumbus

Run the script to deploy the flow with the specified job variables.

python demo_project/daily_flow.py

The job variables should be visible in the UI under the Configuration tab.

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Override job variables on a flow run

When running flows, you can pass in job variables that override any values set on the work pool or deployment. Any interface that runs deployments can accept job variables.

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Use the custom run form in the UI

Custom runs allow you to pass in a dictionary of variables into your flow run infrastructure. Using the same env example from above, you could do the following:

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Use the CLI

Similarly, runs kicked off through the CLI accept job variables with the -jv or --job-variable flag.

prefect deployment run \
  --id "fb8e3073-c449-474b-b993-851fe5e80e53" \
  --job-variable MY_NEW_ENV_VAR=42 \
  --job-variable HELLO=THERE

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Use job variables in automations

Additionally, runs kicked off through automation actions can use job variables, including ones rendered from Jinja templates.