In this example, you will create a work pool and worker to deploy your flow, and then execute it with the Prefect API. You must have Docker installed and running on your machine.

​
Create a work pool

A work pool provides default infrastructure configurations that all jobs inherit and can override. You can adjust many defaults, such as the base Docker image, container cleanup behavior, and resource limits.

To set up a Docker type work pool with the default values, run:

prefect work-pool create --type docker my-docker-pool

Or create the work pool in the UI.

To confirm the work pool creation was successful, run:

prefect work-pool ls

You should see your new my-docker-pool listed in the output.

Next, check that you can see this work pool in your Prefect UI. Navigate to the Work Pools tab and verify that you see my-docker-pool listed. When you click into my-docker-pool, you should see a red status icon signifying that this work pool is not ready.

To make the work pool ready, you’ll need to start a worker. We’ll show how to do this next.

​
Start a worker

Workers are a lightweight polling process that kick off scheduled flow runs on a specific type of infrastructure (such as Docker). To start a worker on your local machine, open a new terminal and confirm that your virtual environment has prefect installed.

Run the following command in this new terminal to start the worker:

prefect worker start --pool my-docker-pool

You should see the worker start. It’s now polling the Prefect API to check for any scheduled flow runs it should pick up and then submit for execution. You’ll see your new worker listed in the UI under the Workers tab of the Work Pools page with a recent last polled date. The work pool should have a Ready status indicator.

Keep this terminal session active for the worker to continue to pick up jobs. Since you are running this worker locally, the worker will if you close the terminal. In a production setting this worker should run as a daemonized or managed process.

​
Create the deployment

From the previous steps, you now have:

  • A work pool
  • A worker

Next, you’ll create a deployment from your flow code.

​
Automatically bake your code into a Docker image

Create a deployment from Python code by calling the .deploy method on a flow:

buy.py
from prefect import flow

@flow(log_prints=True)
def buy():
    print("Buying securities")

if __name__ == "__main__":
    buy.deploy(
        name="my-code-baked-into-an-image-deployment",
        work_pool_name="my-docker-pool",
        image="my_registry/my_image:my_image_tag"
    )

Now, run the script to create a deployment (in future examples this step is omitted for brevity):

python buy.py

You should see messages in your terminal that Docker is building your image. When the deployment build succeeds, you will see information in your terminal showing you how to start a worker for your deployment, and how to run your deployment. Your deployment is visible on the Deployments page in the UI.

By default, .deploy builds a Docker image with your flow code baked into it and pushes the image to the Docker Hub registry specified in the image argument`.

Authentication to Docker Hub

Your environment must be authenticated to your Docker registry to push an image to it.

You can specify a registry other than Docker Hub by providing the full registry path in the image argument.

If building a Docker image, your environment with your deployment needs Docker installed and running.

To avoid pushing to a registry, set push=False in the .deploy method:


if __name__ == "__main__":
    buy.deploy(
        name="my-code-baked-into-an-image-deployment",
        work_pool_name="my-docker-pool",
        image="my_registry/my_image:my_image_tag",
        push=False
    )

To avoid building an image, set build=False in the .deploy method:


if __name__ == "__main__":
    buy.deploy(
        name="my-code-baked-into-an-image-deployment",
        work_pool_name="my-docker-pool",
        image="my_registry/no-build-image:1.0",
        build=False
    )

The specified image must be available in your deployment’s execution environment for accessible flow code.

Prefect generates a Dockerfile for you that builds an image based off of one of Prefect’s published images. The generated Dockerfile copies the current directory into the Docker image and installs any dependencies listed in a requirements.txt file.

​
Automatically build a custom Docker image with a local Dockerfile

If you want to use a custom Dockerfile, specify the path to the Dockerfile with the DockerImage class:

custom_dockerfile.py
from prefect import flow
from prefect.docker import DockerImage


@flow(log_prints=True)
def buy():
    print("Selling securities")


if __name__ == "__main__":
    buy.deploy(
        name="my-custom-dockerfile-deployment",
        work_pool_name="my-docker-pool",
        image=DockerImage(
            name="my_image",
            tag="deploy-guide",
            dockerfile="Dockerfile"
    ),
    push=False
)

The DockerImage object enables image customization.

For example, you can install a private Python package from GCP’s artifact registry like this:

  1. Create a custom base Dockerfile.

    FROM python:3.12

ARG AUTHED_ARTIFACT_REG_URL
COPY ./requirements.txt /requirements.txt

RUN pip install --extra-index-url ${AUTHED_ARTIFACT_REG_URL} -r /requirements.txt

  2. Create your deployment with the DockerImage class:

    private-package.py
    from prefect import flow
from prefect.deployments.runner import DockerImage
from prefect.blocks.system import Secret
from myproject.cool import do_something_cool


@flow(log_prints=True)
def my_flow():
    do_something_cool()


if __name__ == "__main__":
    artifact_reg_url: Secret = Secret.load("artifact-reg-url")

    my_flow.deploy(
        name="my-deployment",
        work_pool_name="my-docker-pool",
        image=DockerImage(
            name="my-image",
            tag="test",
            dockerfile="Dockerfile",
            buildargs={"AUTHED_ARTIFACT_REG_URL": artifact_reg_url.get()},
        ),
    )

Note that you used a Prefect Secret block to load the URL configuration for the artifact registry above.

See all the optional keyword arguments for the DockerImage class.

Default Docker namespace

You can set the PREFECT_DEFAULT_DOCKER_BUILD_NAMESPACE setting to append a default Docker namespace to all images you build with .deploy. This is helpful if you use a private registry to store your images.

To set a default Docker namespace for your current profile run:

prefect config set PREFECT_DEFAULT_DOCKER_BUILD_NAMESPACE=<docker-registry-url>/<organization-or-username>

Once set, you can omit the namespace from your image name when creating a deployment:

with_default_docker_namespace.py
if __name__ == "__main__":
    buy.deploy(
        name="my-code-baked-into-an-image-deployment",
        work_pool_name="my-docker-pool",
        image="my_image:my_image_tag"
    )

The above code builds an image with the format <docker-registry-url>/<organization-or-username>/my_image:my_image_tag when PREFECT_DEFAULT_DOCKER_BUILD_NAMESPACE is set.

​
Store your code in git-based cloud storage

While baking code into Docker images is a popular deployment option, many teams store their workflow code in git-based storage, such as GitHub, Bitbucket, or GitLab.

If you don’t specify an image argument for .deploy, you must specify where to pull the flow code from at runtime with the from_source method.

Here’s how to pull your flow code from a GitHub repository:

git_storage.py
from prefect import flow

if __name__ == "__main__":
    flow.from_source(
        "https://github.com/my_github_account/my_repo/my_file.git",
        entrypoint="flows/no-image.py:hello_world",
    ).deploy(
        name="no-image-deployment",
        work_pool_name="my-docker-pool",
        build=False
    )

The entrypoint is the path to the file the flow is located in and the function name, separated by a colon.

See the Store flow code guide for more flow code storage options.

​
Additional configuration with .deploy

Next, see deployment configuration options.

To pass parameters to your flow, you can use the parameters argument in the .deploy method. Just pass in a dictionary of key-value pairs.

pass_params.py
from prefect import flow


@flow
def hello_world(name: str):
    print(f"Hello, {name}!")


if __name__ == "__main__":
    hello_world.deploy(
        name="pass-params-deployment",
        work_pool_name="my-docker-pool",
        parameters=dict(name="Prefect"),
        image="my_registry/my_image:my_image_tag",
    )

The job_variables parameter allows you to fine-tune the infrastructure settings for a deployment. The values passed in override default values in the specified work pool’s base job template.

You can override environment variables, such as image_pull_policy and image, for a specific deployment with the job_variables argument.

job_var_image_pull.py
if __name__ == "__main__":
    get_repo_info.deploy(
        name="my-deployment-never-pull",
        work_pool_name="my-docker-pool",
        job_variables={"image_pull_policy": "Never"},
        image="my-image:my-tag",
        push=False
    )

Similarly, you can override the environment variables specified in a work pool through the job_variables parameter:

job_var_env_vars.py
if __name__ == "__main__":
    get_repo_info.deploy(
        name="my-deployment-never-pull",
        work_pool_name="my-docker-pool",
        job_variables={"env": {"EXTRA_PIP_PACKAGES": "boto3"} },
        image="my-image:my-tag",
        push=False
    )

The dictionary key “EXTRA_PIP_PACKAGES” denotes a special environment variable that Prefect uses to install additional Python packages at runtime. This approach is an alternative to building an image with a custom requirements.txt copied into it.

See Override work pool job variables for more information about how to customize these variables.

​
Work with multiple deployments with deploy

Create multiple deployments from one or more Python files that use .deploy. You can manage these deployments independently of one another to deploy the same flow with different configurations in the same codebase.

To create multiple work pool-based deployments at once, use the deploy function, which is analogous to the serve function:

from prefect import deploy, flow


@flow(log_prints=True)
def buy():
    print("Buying securities")


if __name__ == "__main__":
    deploy(
        buy.to_deployment(name="dev-deploy", work_pool_name="my-docker-pool"),
        buy.to_deployment(name="prod-deploy", work_pool_name="my-docker-pool"),
        image="my-registry/my-image:dev",
        push=False,
    )

In the example above you created two deployments from the same flow, but with different work pools. Alternatively, you can create two deployments from different flows:

from prefect import deploy, flow


@flow(log_prints=True)
def buy():
    print("Buying securities.")


@flow(log_prints=True)
def sell():
    print("Selling securities.")


if __name__ == "__main__":
    deploy(
        buy.to_deployment(name="buy-deploy"),
        sell.to_deployment(name="sell-deploy"),
        work_pool_name="my-docker-pool",
        image="my-registry/my-image:dev",
        push=False,
    )

In the example above, the code for both flows is baked into the same image.

You can specify one or more flows to pull from a remote location at runtime with the from_source method. Here’s an example of deploying two flows, one defined locally and one defined in a remote repository:

from prefect import deploy, flow


@flow(log_prints=True)
def local_flow():
    print("I'm a flow!")


if __name__ == "__main__":
    deploy(
        local_flow.to_deployment(name="example-deploy-local-flow"),
        flow.from_source(
            source="https://github.com/org/repo.git",
            entrypoint="flows.py:my_flow",
        ).to_deployment(
            name="example-deploy-remote-flow",
        ),
        work_pool_name="my-docker-pool",
        image="my-registry/my-image:dev",
    )

You can pass any number of flows to the deploy function. This is useful if using a monorepo approach to your workflows.

​
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