`prefect.workers.base`

Classes

`BaseJobConfiguration`

Methods:

`from_template_and_values`

from_template_and_values(cls, base_job_template: dict[str, Any], values: dict[str, Any], client: 'PrefectClient | None' = None)

Creates a valid worker configuration object from the provided base configuration and overrides. Important: this method expects that the base_job_template was already validated server-side.

`is_using_a_runner`

is_using_a_runner(self) -> bool

`json_template`

json_template(cls) -> dict[str, Any]

Returns a dict with job configuration as keys and the corresponding templates as values Defaults to using the job configuration parameter name as the template variable name. e.g.

{
    key1: '{{ key1 }}',     # default variable template
    key2: '{{ template2 }}', # `template2` specifically provide as template
}

`prepare_for_flow_run`

prepare_for_flow_run(self, flow_run: 'FlowRun', deployment: 'DeploymentResponse | None' = None, flow: 'APIFlow | None' = None, work_pool: 'WorkPool | None' = None, worker_name: str | None = None, worker_id: 'UUID | None' = None) -> None

Prepare the job configuration for a flow run. This method is called by the worker before starting a flow run. It should be used to set any configuration values that are dependent on the flow run. Args:

flow_run: The flow run to be executed.
deployment: The deployment that the flow run is associated with.
flow: The flow that the flow run is associated with.
work_pool: The work pool that the flow run is running in.
worker_name: The name of the worker that is submitting the flow run.
worker_id: The backend ID of the worker that is submitting the flow run.

`resolve_for_flow_run`

resolve_for_flow_run(cls, flow_run: 'FlowRun') -> Self

Build a fully-prepared job configuration for an existing flow run. Reads the flow run’s deployment (when present) and flow, merges deployment- and flow-run-level job variables over the work pool’s base template, instantiates the configuration, and stamps attribution metadata via prepare_for_flow_run.

`BaseVariables`

Methods:

`model_json_schema`

model_json_schema(cls, by_alias: bool = True, ref_template: str = '#/definitions/{model}', schema_generator: Type[GenerateJsonSchema] = GenerateJsonSchema, mode: Literal['validation', 'serialization'] = 'validation') -> dict[str, Any]

TODO: stop overriding this method - use GenerateSchema in ConfigDict instead?

`BaseWorkerResult`

`BaseWorker`

Methods:

`cleanup_handler_registry`

cleanup_handler_registry(self) -> WorkerCleanupHandlerRegistry

`client`

client(self) -> PrefectClient

`get_all_available_worker_types`

get_all_available_worker_types() -> list[str]

Returns all worker types available in the local registry.

`get_and_submit_flow_runs`

get_and_submit_flow_runs(self) -> list['FlowRun']

`get_default_base_job_template`

get_default_base_job_template(cls) -> dict[str, Any]

`get_description`

get_description(cls) -> str

`get_documentation_url`

get_documentation_url(cls) -> str

`get_flow_run_logger`

get_flow_run_logger(self, flow_run: 'FlowRun') -> PrefectLogAdapter

`get_logo_url`

get_logo_url(cls) -> str

`get_name_slug`

get_name_slug(self) -> str

`get_status`

get_status(self) -> dict[str, Any]

Retrieves the status of the current worker including its name, current worker pool, the work pool queues it is polling, and its local settings.

`get_worker_class_from_type`

get_worker_class_from_type(type: str) -> Optional[Type['BaseWorker[Any, Any, Any]']]

Returns the worker class for a given worker type. If the worker type is not recognized, returns None.

`handled_cleanup_kinds`

handled_cleanup_kinds(self) -> tuple[CleanupKind, ...]

`is_worker_still_polling`

is_worker_still_polling(self, query_interval_seconds: float) -> bool

This method is invoked by a webserver healthcheck handler and returns a boolean indicating if the worker has recorded a scheduled flow run poll within a variable amount of time. The query_interval_seconds is the same value that is used by the loop services - we will evaluate if the _last_polled_time was within that interval x 30 (so 10s -> 5m) The instance property self._last_polled_time is currently set/updated in get_and_submit_flow_runs()

`kill_infrastructure`

kill_infrastructure(self, infrastructure_pid: str, configuration: C, grace_seconds: int = 30) -> None

Kill infrastructure for a flow run. Override this method in subclasses to implement infrastructure-specific termination logic. Args:

infrastructure_pid: The infrastructure identifier from the flow run.
configuration: The job configuration for connecting to infrastructure.
grace_seconds: Time to allow for graceful shutdown before force killing.

Raises:

NotImplementedError: If the worker doesn’t support killing infrastructure.
InfrastructureNotFound: If the infrastructure doesn’t exist.
InfrastructureNotAvailable: If the infrastructure can’t be killed by this worker.

`limiter`

limiter(self) -> anyio.CapacityLimiter

`max_cleanup_concurrency`

max_cleanup_concurrency(self) -> int

`run`

run(self, flow_run: 'FlowRun', configuration: C, task_status: Optional[anyio.abc.TaskStatus[int]] = None) -> R

Runs a given flow run on the current worker.

`setup`

setup(self) -> None

Prepares the worker to run.

`start`

start(self, run_once: bool = False, with_healthcheck: bool = False, printer: Callable[..., None] = print) -> None

Starts the worker and runs the main worker loops. By default, the worker will run loops to poll for scheduled/cancelled flow runs and sync with the Prefect API server. If run_once is set, the worker will only run each loop once and then return. If with_healthcheck is set, the worker will start a healthcheck server which can be used to determine if the worker is still polling for flow runs and restart the worker if necessary. Args:

run_once: If set, the worker will only run each loop once then return.
with_healthcheck: If set, the worker will start a healthcheck server.
printer: A print-like function where logs will be reported.

`submit`

submit(self, flow: 'Flow[..., FR]', parameters: dict[str, Any] | None = None, job_variables: dict[str, Any] | None = None, flow_run: 'FlowRun | None' = None) -> 'PrefectFlowRunFuture[FR]'

EXPERIMENTAL: The interface for this method is subject to change. Submits a flow to run via the worker. Args:

flow: The flow to submit
parameters: The parameters to pass to the flow
job_variables: Job variables for infrastructure configuration
flow_run: Optional existing flow run to retry (reuses ID instead of creating new)

Returns:

A flow run future

`sync_with_backend`

sync_with_backend(self) -> None

Updates the worker’s local information about it’s current work pool and queues. Sends a worker heartbeat to the API.

`teardown`

teardown(self, *exc_info: Any) -> None

Cleans up resources after the worker is stopped.

`work_pool`

work_pool(self) -> WorkPool

​prefect.workers.base

​Classes

​BaseJobConfiguration

​from_template_and_values

​is_using_a_runner

​json_template

​prepare_for_flow_run

​resolve_for_flow_run

​BaseVariables

​model_json_schema

​BaseWorkerResult

​BaseWorker

​cleanup_handler_registry

​client

​get_all_available_worker_types

​get_and_submit_flow_runs

​get_default_base_job_template

​get_description

​get_documentation_url

​get_flow_run_logger

​get_logo_url

​get_name_slug

​get_status

​get_worker_class_from_type

​handled_cleanup_kinds

​is_worker_still_polling

​kill_infrastructure

​limiter

​max_cleanup_concurrency

​run

​setup

​start

​submit

​sync_with_backend

​teardown

​work_pool

`prefect.workers.base`

Classes

`BaseJobConfiguration`

`from_template_and_values`

`is_using_a_runner`

`json_template`

`prepare_for_flow_run`

`resolve_for_flow_run`

`BaseVariables`

`model_json_schema`

`BaseWorkerResult`

`BaseWorker`

`cleanup_handler_registry`

`client`

`get_all_available_worker_types`

`get_and_submit_flow_runs`

`get_default_base_job_template`

`get_description`

`get_documentation_url`

`get_flow_run_logger`

`get_logo_url`

`get_name_slug`

`get_status`

`get_worker_class_from_type`

`handled_cleanup_kinds`

`is_worker_still_polling`

`kill_infrastructure`

`limiter`

`max_cleanup_concurrency`

`run`

`setup`

`start`

`submit`

`sync_with_backend`

`teardown`

`work_pool`