Workers & Work Pools

Docker

This tutorial requires the use of Docker.

Why workers and work pools?

Workers and work pools bridge the Prefect orchestration layer with the infrastructure the flows are actually executed on.

Choosing Between workers and flow.serve()

The earlier section discussed the serve approach. For many use cases, serve is sufficient to meet scheduling and orchestration needs. Workers and work pools are optional. Just remember, if infrastructure needs escalate, workers and work pools can become a handy tool. The best part? You're not locked into one method. You can seamlessly combine approaches as needed.

Deployment definition methods differ slightly for workers

If you choose to use worker-based execution, the way you define deployments will be different. Deployments for workers are configured through the Prefect CLI with prefect deploy. A deployment created with serve cannot be submitted to a worker.

The primary reason to use workers and work pools is for dynamic infrastructure provisioning and configuration. For example, you might have a workflow that has expensive infrastructure requirements and is run infrequently. In this case, you don't want an idle process running within that infrastructure. Instead, use a lightweight worker to dynamically provision the infrastructure only when the workflow is scheduled to run.

Other advantages to using workers and work pools include:

• You can configure default infrastructure configurations on your work pools that all jobs inherit and can override
• Platform teams can use work pools to expose opinionated (and enforced!) interfaces to the infrastructure that they oversee
• Work pools can be used to prioritize (or limit) runs through the use of work queues

The architecture of a worker/work pool deployment can be summarized with the following diagram:

graph TD
    subgraph your_infra["Your Execution Environment"]
        worker["Worker"]
                subgraph flow_run_infra[Flow Run Infra]
                    flow_run_a(("Flow Run A"))
                end
                subgraph flow_run_infra_2[Flow Run Infra]
                    flow_run_b(("Flow Run B"))
                end      
    end

    subgraph api["Prefect API"]
                Deployment --> |assigned to| work_pool
        work_pool(["Work Pool"])
    end

    worker --> |polls| work_pool
    worker --> |creates| flow_run_infra
    worker --> |creates| flow_run_infra_2

^{Notice above that the worker is in charge of provisioning the flow run infrastructure. In context of this tutorial, that flow run infrastructure is an ephemeral Docker container to host each flow run. Different worker types create different types of flow run infrastructure.}

Now that we’ve reviewed the concepts of a work pool and worker, let’s create them so that you can deploy your tutorial flow, and execute it later using the Prefect API.

Setting up the worker and work pool

For this tutorial you will create a Docker type work pool via the CLI.

Using the Docker work pool type means that all work sent to this work pool will run within a dedicated Docker container using a Docker client available to the worker.

Other work pool types

There are work pool types for serverless computing environments such as AWS ECS, Azure Container Instances, and GCP Cloud Run. Kubernetes is also a popular type of work pool.

These are expanded upon in the Guides section.

Create the work pool

In your terminal run the following command to set up a Docker type work pool.

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

Let’s confirm that the work pool was successfully created by running the following command in the same terminal. You should see your new my-docker-pool in the output list.

prefect work-pool ls 

Finally, let’s double 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 the my-docker-pool, select the "Work Queues" tab. You should see a red status icon listed for the default work queue signifying that this queue is not ready to submit work. Using and configuring work queues is an advanced deployment mode. You can learn more about them in the work queue documentation.

To get the work queue healthy and ready to submit flow runs, you need to start a worker.

Starting a worker

Workers are a lightweight polling process that kick off scheduled flow runs on a certain type of infrastructure (like Docker). To start a worker on your laptop, 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 request 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. You should also be able to see a Healthy status indicator in the default work queue under the work queue tab - progress!

You will need to keep this terminal session active in order for the worker to continue to pick up jobs. Since you are running this worker locally, the worker will terminate if you close the terminal. Therefore, in a production setting this worker should run as a daemonized or managed process. See next steps for more information on this.

Now that you’ve set up your work pool and worker, we have what we need to kick off and execute flow runs of flows deployed to this work pool. Let's deploy your tutorial flow to my-docker-pool.

Create the deployment

From our previous steps, we now have:

1. A flow
2. A work pool
3. A worker

Now it’s time to put it all together.

In your terminal (not the terminal in which the worker is running), navigate to your repo_info.py file that we created in the first section. For best results, this file should be in its own otherwise empty directory. Now run the following command from the root of this directory to begin deploying your flow:

prefect deploy

Specifying an entrypoint

In non-interactive settings (like CI/CD), you can specify the entrypoint of your flow directly in the CLI.

For example, if get_repo_info is defined in repo_info.py, provide deployment details with flags prefect deploy repo_info.py:get_repo_info -n my-deployment -p my-docker-pool.

When running prefect deploy interactively, the CLI will discover all flows in your working directory. Select the flow you want to deploy, and the deployment wizard will walk you through the rest of the deployment creation process:

1. Deployment name: Choose a name, like my-deployment.
2. Would you like to configure a schedule for this deployment? (y/n): Type n for now, you can set up a schedule later.
3. Which work pool would you like to deploy this flow to? (use arrow keys): Select the work pool you just created, my-docker-pool.
4. Would you like to build a custom Docker image for this deployment? (y/n): Select y to have Prefect build an image for you.
5. Repository name (e.g. your Docker Hub username): For the purposes of the tutorial, you can input anything you'd like here.
6. Image name (my-first-deployment): Hit Enter to use the default image name.
7. Image tag (latest): Hit Enter to use the default image tag latest.
8. Would you like to push this image to a remote registry? (y/n): Select n for now; we can keep this image local.
9. Would you like to save configuration for this deployment for faster deployments in the future? (y/n): Select y to initiate a prefect.yaml file.

Disable interactive mode

You can disable the prefect deploy command's interactive prompts by passing in the --no-prompt flag, e.g. prefect --no-prompt deploy -n my-deployment-name. Alternatively, you can enable it by passing in the --prompt flag. This can be used for all prefect commands. To disable interactive mode for all prefect commands, set the PREFECT_CLI_PROMPT setting to 0.

Prefect will now build a custom Docker image containing your workflow code that the worker can use to dynamically spawn Docker containers whenever this workflow needs to run.

Modify the deployment

If you selected y on the last prompt to save configuration, you should see a new prefect.yaml file appear. This file will allow you to easily modify and define multiple deployments for this repo.

The prefect.yaml file not only holds settings for various deployments but can also contain instructions that help set up the execution environment for your flow runs. In the context of this tutorial, we employ a build action to create a Docker image that the worker will use when running your flow.

Upon examining the auto-generated prefect.yaml, you'll notice that the parameters for your deployment mirror the values you provided to the deployment creation wizard:

prefect.yaml

build:
- prefect_docker.deployments.steps.build_docker_image:
    requires: prefect-docker>=0.3.1
    id: build-image
    dockerfile: auto
    image_name: docker-user/deployment-image
    tag: latest

deployments:
- name: my-deployment
  version: null
  tags: []
  description: null
  entrypoint: my_flow.py:get_repo_info
  parameters: {}
  work_pool:
    name: my-docker-pool
    work_queue_name: null
    job_variables:
      image: '{{ build-image.image }}' ## Resultant image from the build action
  schedule: null

It's worth noting that the prefect.yaml supports referencing dynamic values. You can see that our deployment references the Docker image produced from the build action above.

The job_variables section allows you to fine-tune the infrastructure settings for a specific deployment. These values override default values in the specified work pool's base job template.

When testing images locally without pushing them to a registry (to avoid potential errors like docker.errors.NotFound), it's recommended to include an image_pull_policy job_variable set to Never. However, for production workflows, always consider pushing images to a remote registry for more reliability and accessibility.

Here's how you can easily set the image_pull_policy to be Never for this tutorial deployment without affecting the default value set on your work pool:

prefect.yaml

  work_pool:
    name: local-docker
    work_queue_name: null
    job_variables:
      image: '{{ build-image.image }}'
      image_pull_policy: 'Never'
  schedule: null

To register this update to your deployment's parameters with Prefect's API, run:

prefect deploy --name my-deployment

Now everything is set up for us to submit a flow-run to the work pool:

prefect deployment run 'get_repo_info/my-deployment'

Common Pitfalls

• When running prefect deploy, double check that you are at the root of your repo, otherwise the worker may attempt to use an incorrect flow entrypoint during remote execution!
• Ensure that you have pushed any changes to your flow script to your GitHub repo - at any given time, your worker will pull the code that exists there!

Did you know?

A Prefect flow can have more than one deployment. This can be useful if you want your flow to run in different execution environments or have multiple schedules.

Next steps

• Learn about deploying multiple flows and CI/CD with our prefect.yaml.
• Check out some of our other work pools.
• Concepts contain deep dives into Prefect components.
• Guides provide step-by-step recipes for common Prefect operations including:
  • Deploying on Kubernetes
  • Deploying flows in Docker
  • Writing tests

And more!