Prefect workers are responsible for setting up execution infrastructure and starting flow runs on that infrastructure. A list of available workers can be found in the Work Pools, Workers & Agents documentation. What if you want to execute your flow runs on infrastructure that doesn’t have an available worker type? This tutorial will walk you through creating a custom worker that can run your flows on your chosen infrastructure.

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Worker Configuration

When setting up an execution environment for a flow run, a worker receives configuration for the infrastructure it is designed to work with. Examples of configuration values include memory allocation, CPU allocation, credentials, image name, etc. The worker then uses this configuration to create the execution environment and start the flow run. !!! tip “How are the configuration values populated?” The work pool that a worker polls for flow runs has a base job template associated with it. The template is the contract for how configuration values populate for each flow run. The keys in the job_configuration section of this base job template match the worker’s configuration class attributes. The values in the job_configuration section of the base job template are used to populate the attributes of the worker’s configuration class. The work pool creator gets to decide how they want to populate the values in the job_configuration section of the base job template. The values can be hard-coded, templated using placeholders, or a mix of these two approaches. Because you, as the worker developer, don’t know how the work pool creator will populate the values, you should set sensible defaults for your configuration class attributes as a matter of best practice.

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Implementing a BaseJobConfiguration Subclass

A worker developer defines their worker’s configuration to function with a class extending BaseJobConfiguration. BaseJobConfiguration has attributes that are common to all workers:
AttributeDescription
nameThe name to assign to the created execution environment.
envEnvironment variables to set in the created execution environment.
labelsThe labels assigned to the created execution environment for metadata purposes.
commandThe command to use when starting a flow run.
Prefect sets values for each attribute before giving the configuration to the worker. If you want to customize the values of these attributes, use the prepare_for_flow_run method. Here’s an example prepare_for_flow_run method that adds a label to the execution environment: 
def prepare_for_flow_run(
    self, flow_run, deployment = None, flow = None, work_pool = None, worker_name = None
):  
    super().prepare_for_flow_run(flow_run, deployment, flow, work_pool, worker_name)  
    self.labels.append("my-custom-label")
A worker configuration class is a Pydantic model, so you can add additional attributes to your configuration class as Pydantic fields. For example, if you want to allow memory and CPU requests for your worker, you can do so like this: 
from pydantic import Field
from prefect.workers.base import BaseJobConfiguration

class MyWorkerConfiguration(BaseJobConfiguration):
    memory: int = Field(
        default=1024,
        description="Memory allocation for the execution environment."
    )
    cpu: int = Field(
        default=500, 
        description="CPU allocation for the execution environment."
    )
This configuration class will populate the job_configuration section of the resulting base job template. For this example, the base job template would look like this: 
job_configuration:
    name: "{{ name }}"
    env: "{{ env }}"
    labels: "{{ labels }}"
    command: "{{ command }}"
    memory: "{{ memory }}"
    cpu: "{{ cpu }}"
variables:
    type: object
    properties:
        name:
          title: Name
          description: Name given to infrastructure created by a worker.
          type: string
        env:
          title: Environment Variables
          description: Environment variables to set when starting a flow run.
          type: object
          additionalProperties:
            type: string
        labels:
          title: Labels
          description: Labels applied to infrastructure created by a worker.
          type: object
          additionalProperties:
            type: string
        command:
          title: Command
          description: The command to use when starting a flow run. In most cases,
            this should be left blank and the command will be automatically generated
            by the worker.
          type: string
        memory:
            title: Memory
            description: Memory allocation for the execution environment.
            type: integer
            default: 1024
        cpu:
            title: CPU
            description: CPU allocation for the execution environment.
            type: integer
            default: 500
This base job template defines what values can be provided by deployment creators on a per-deployment basis and how those provided values will be translated into the configuration values that the worker will use to create the execution environment. Notice that each attribute for the class was added in the job_configuration section with placeholders whose name matches the attribute name. The variables section was also populated with the OpenAPI schema for each attribute. If a configuration class is used without explicitly declaring any template variables, the template variables will be inferred from the configuration class attributes.

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Customizing Configuration Attribute Templates

You can customize the template for each attribute for situations where the configuration values should use more sophisticated templating. For example, if you want to add units for the memory attribute, you can do so like this: 
from pydantic import Field
from prefect.workers.base import BaseJobConfiguration

class MyWorkerConfiguration(BaseJobConfiguration):
    memory: str = Field(
        default="1024Mi",
        description="Memory allocation for the execution environment.",
        json_schema_extra=dict(template="{{ memory_request }}Mi")
    )
    cpu: str = Field(
        default="500m", 
        description="CPU allocation for the execution environment.",
        json_schema_extra=dict(template="{{ cpu_request }}m")
    )
Notice that we changed the type of each attribute to str to accommodate the units, and we added a new json_schema_extra attribute to each attribute. The template key in json_schema_extra is used to populate the job_configuration section of the resulting base job template. For this example, the job_configuration section of the resulting base job template would look like this: 
job_configuration:
    name: "{{ name }}"
    env: "{{ env }}"
    labels: "{{ labels }}"
    command: "{{ command }}"
    memory: "{{ memory_request }}Mi"
    cpu: "{{ cpu_request }}m"
Note that to use custom templates, you will need to declare the template variables used in the template because the names of those variables can no longer be inferred from the configuration class attributes. We will cover how to declare the default variable schema in the Worker Template Variables section.

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Rules for Template Variable Interpolation

When defining a job configuration model, it’s useful to understand how template variables are interpolated into the job configuration. The templating engine follows a few simple rules:
  1. If a template variable is the only value for a key in the job_configuration section, the key will be replaced with the value template variable.
  2. If a template variable is part of a string (i.e., there is text before or after the template variable), the value of the template variable will be interpolated into the string.
  3. If a template variable is the only value for a key in the job_configuration section and no value is provided for the template variable, the key will be removed from the job_configuration section.
These rules allow worker developers and work pool maintainers to define template variables that can be complex types like dictionaries and lists. These rules also mean that worker developers should give reasonable default values to job configuration fields whenever possible because values are not guaranteed to be provided if template variables are unset.

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Template Variable Usage Strategies

Template variables define the interface that deployment creators interact with to configure the execution environments of their deployments. The complexity of this interface can be controlled via the template variables that are defined for a base job template. This control allows work pool maintainers to find a point along the spectrum of flexibility and simplicity appropriate for their organization. There are two patterns that are represented in current worker implementations:

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Pass-Through

In the pass-through pattern, template variables are passed through to the job configuration with little change. This pattern exposes complete control to deployment creators but also requires them to understand the details of the execution environment. This pattern is useful when the execution environment is simple, and the deployment creators are expected to have high technical knowledge. The Docker worker is an example of a worker that uses this pattern.

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Infrastructure as Code Templating

Depending on the infrastructure they interact with, workers can sometimes employ a declarative infrastructure syntax (i.e., infrastructure as code) to create execution environments (e.g., a Kubernetes manifest or an ECS task definition). In the IaC pattern, it’s often useful to use template variables to template portions of the declarative syntax which then can be used to generate the declarative syntax into a final form. This approach allows work pool creators to provide a simpler interface to deployment creators while also controlling which portions of infrastructure are configurable by deployment creators. The Kubernetes worker is an example of a worker that uses this pattern.

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Configuring Credentials

When executing flow runs within cloud services, workers will often need credentials to authenticate with those services. For example, a worker that executes flow runs in AWS Fargate will need AWS credentials. As a worker developer, you can use blocks to accept credentials configuration from the user. For example, if you want to allow the user to configure AWS credentials, you can do so like this: 
from prefect.workers.base import BaseJobConfiguration
from prefect_aws import AwsCredentials
from pydantic import Field

class MyWorkerConfiguration(BaseJobConfiguration):
    aws_credentials: AwsCredentials | None = Field(
        default=None,
        description="AWS credentials to use when creating AWS resources."
    )
Users can create and assign a block to the aws_credentials attribute in the UI and the worker will use these credentials when interacting with AWS resources.

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Worker Template Variables

Providing template variables for a base job template defines the fields that deployment creators can override per deployment. The work pool creator ultimately defines the template variables for a base job template, but the worker developer is able to define default template variables for the worker to make it easier to use. Default template variables for a worker are defined by implementing the BaseVariables class. Like the BaseJobConfiguration class, the BaseVariables class has attributes that are common to all workers:
AttributeDescription
nameThe name to assign to the created execution environment.
envEnvironment variables to set in the created execution environment.
labelsThe labels assigned the created execution environment for metadata purposes.
commandThe command to use when starting a flow run.
Additional attributes can be added to the BaseVariables class to define additional template variables. For example, if you want to allow memory and CPU requests for your worker, you can do so like this: 
from pydantic import Field
from prefect.workers.base import BaseVariables

class MyWorkerTemplateVariables(BaseVariables):
    memory_request: int = Field(
        default=1024,
        description="Memory allocation for the execution environment."
    )
    cpu_request: int = Field(
        default=500, 
        description="CPU allocation for the execution environment."
    )
When MyWorkerTemplateVariables is used in conjunction with MyWorkerConfiguration from the Customizing Configuration Attribute Templates section, the resulting base job template will look like this: 
job_configuration:
    name: "{{ name }}"
    env: "{{ env }}"
    labels: "{{ labels }}"
    command: "{{ command }}"
    memory: "{{ memory_request }}Mi"
    cpu: "{{ cpu_request }}m"
variables:
    type: object
    properties:
        name:
          title: Name
          description: Name given to infrastructure created by a worker.
          type: string
        env:
          title: Environment Variables
          description: Environment variables to set when starting a flow run.
          type: object
          additionalProperties:
            type: string
        labels:
          title: Labels
          description: Labels applied to infrastructure created by a worker.
          type: object
          additionalProperties:
            type: string
        command:
          title: Command
          description: The command to use when starting a flow run. In most cases,
            this should be left blank and the command will be automatically generated
            by the worker.
          type: string
        memory_request:
            title: Memory Request
            description: Memory allocation for the execution environment.
            type: integer
            default: 1024
        cpu_request:
            title: CPU Request
            description: CPU allocation for the execution environment.
            type: integer
            default: 500
Note that template variable classes are never used directly. Instead, they are used to generate a schema that is used to populate the variables section of a base job template and validate the template variables provided by the user. We don’t recommend using template variable classes within your worker implementation for validation purposes because the work pool creator ultimately defines the template variables. The configuration class should handle any necessary run-time validation.

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Worker Implementation

Workers set up execution environments using provided configuration. Workers also observe the execution environment as the flow run executes and report any crashes to the Prefect API.

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Attributes

To implement a worker, you must implement the BaseWorker class and provide it with the following attributes:
AttributeDescriptionRequired
typeThe type of the worker.Yes
job_configurationThe configuration class for the worker.Yes
job_configuration_variablesThe template variables class for the worker.No
_documentation_urlLink to documentation for the worker.No
_logo_urlLink to a logo for the worker.No
_descriptionA description of the worker.No

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Methods

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run

In addition to the attributes above, you must also implement a run method. The run method is called for each flow run the worker receives for execution from the work pool. The run method has the following signature: 
import anyio
import anyio.abc
from prefect.client.schemas.objects import FlowRun
from prefect.workers.base import BaseWorkerResult, BaseJobConfiguration


async def run(
    self,
    flow_run: FlowRun,
    configuration: BaseJobConfiguration,
    task_status: anyio.abc.TaskStatus = anyio.TASK_STATUS_IGNORED,
) -> BaseWorkerResult: ...
The run method is passed: the flow run to execute, the execution environment configuration for the flow run, and a task status object that allows the worker to track whether the flow run was submitted successfully. The run method must also return a BaseWorkerResult object. The BaseWorkerResult object returned contains information about the flow run execution. For the most part, you can implement the BaseWorkerResult with no modifications like so: 
from prefect.workers.base import BaseWorkerResult

class MyWorkerResult(BaseWorkerResult):
    """Result returned by the MyWorker."""
If you would like to return more information about a flow run, then additional attributes can be added to the BaseWorkerResult class.

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Worker Implementation Example

Below is an example of a worker implementation. This example is not intended to be a complete implementation but to illustrate the aforementioned concepts. 
import anyio
import anyio.abc
from prefect.client.schemas.objects import FlowRun
from prefect.workers.base import BaseWorker, BaseWorkerResult, BaseJobConfiguration, BaseVariables
from pydantic import Field

class MyWorkerConfiguration(BaseJobConfiguration):
    memory: str = Field(
        default="1024Mi",
        description="Memory allocation for the execution environment.",
        json_schema_extra=dict(template="{{ memory_request }}Mi")
    )
    cpu: str = Field(
        default="500m", 
        description="CPU allocation for the execution environment.",
        json_schema_extra=dict(template="{{ cpu_request }}m")
    )

class MyWorkerTemplateVariables(BaseVariables):
    memory_request: int = Field(
        default=1024,
        description="Memory allocation for the execution environment."
    )
    cpu_request: int = Field(
        default=500, 
        description="CPU allocation for the execution environment."
    )

class MyWorkerResult(BaseWorkerResult):
    """Result returned by the MyWorker."""

class MyWorker(BaseWorker):
    type = "my-worker"
    job_configuration = MyWorkerConfiguration
    job_configuration_variables = MyWorkerTemplateVariables
    _documentation_url = "https://example.com/docs"
    _logo_url = "https://example.com/logo"
    _description = "My worker description."

    async def run(
        self, flow_run: FlowRun, configuration: BaseJobConfiguration, task_status: anyio.abc.TaskStatus = anyio.TASK_STATUS_IGNORED,
    ) -> BaseWorkerResult:
        # Create the execution environment and start execution
        job = await self._create_and_start_job(configuration)

        # Use a unique ID to mark the run as started. This ID is later used to tear down infrastructure
        # if the flow run is cancelled.
        task_status.started(job.id) 

        # Monitor the execution
        job_status = await self._watch_job(job, configuration)

        exit_code = job_status.exit_code if job_status else -1 # Get result of execution for reporting
        return MyWorkerResult(
            status_code=exit_code,
            identifier=job.id,
        )
Most of the execution logic is omitted from the example above, but it shows that the typical order of operations in the run method is:
  1. Create the execution environment and start the flow run execution
  2. Mark the flow run as started via the passed task_status object
  3. Monitor the execution
  4. Get the execution’s final status from the infrastructure and return a BaseWorkerResult object
To see other examples of worker implementations, see the ProcessWorker and KubernetesWorker implementations.

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Integrating with the Prefect CLI

Workers can be started via the Prefect CLI by providing the --type option to the prefect worker start CLI command. To make your worker type available via the CLI, it must be available at import time. If your worker is in a package, you can add an entry point to your setup file in the following format: 
entry_points={
    "prefect.collections": [
        "my_package_name = my_worker_module",
    ]
},
Prefect will discover this entry point and load your work module in the specified module. The entry point will allow the worker to be available via the CLI.