---
title: How to override job configuration for specific deployments
sidebarTitle: Override Job Configuration
description: Override job variables on a deployment to set environment variables, specify a Docker image, allocate resources, and more.
---

There are two ways to deploy flows to work pools: with a [`prefect.yaml` file](/v3/deploy/infrastructure-concepts/prefect-yaml) or using the [Python `deploy` method](/v3/deploy/infrastructure-concepts/deploy-via-python). 
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.

## 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:
```json
{
  "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.

## 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:

```python
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)
```
### Use a `prefect.yaml` file

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

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

<Note>
    While not the focus of this guide, this deployment definition uses a default "global" `pull` step, 
    because one is not explicitly defined on the deployment. For reference, here's what that would look like at 
    the top of the `prefect.yaml` file:
    ```yaml
    pull:
    - prefect.deployments.steps.git_clone: &clone_repo
        repository: https://github.com/some-user/prefect-monorepo
        branch: main
    ```
</Note>

#### 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:

```yaml
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:

![Job variables in the UI](/v3/img/guides/job-variables.png)

#### 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:

```yaml
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
```

<Note>
    This assumes that the machine where `prefect deploy` is run would have these environment variables set.

    ```bash
    export EXECUTION_ENVIRONMENT=staging
    export MY_NOT_SO_SECRET_CONFIG=plumbus
    ```
   
</Note>

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.

### 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:
```python
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")
            }
        }
    )
```

<Note>
    The above example works assuming a couple things:
    - the machine where this script is run would have these environment variables set.

    ```bash
    export EXECUTION_ENVIRONMENT=staging
    export MY_NOT_SO_SECRET_CONFIG=plumbus
    ```


    - `demo_project/daily_flow.py` _already exists_ in the repository at the specified path
</Note>

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

```bash
python demo_project/daily_flow.py
```

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

![Job variables in the UI](/v3/img/guides/job-variables.png)


## 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.

### 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:

![Job variables through custom run](/v3/img/ui/deployment-job-variables.png)

### Use the CLI

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

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

### Use job variables in Terraform

import { TF } from "/snippets/resource-management/terraform.mdx"
import { deployments } from "/snippets/resource-management/vars.mdx"

<TF name="job variables" href={deployments.tf} />

### Use job variables in automations

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

![Job variables through automation action](/v3/img/ui/automations-action-job-variable.png)


