prefect_dask.task_runners

Interface and implementations of the Dask Task Runner. Task Runners in Prefect are responsible for managing the execution of Prefect task runs. Generally speaking, users are not expected to interact with task runners outside of configuring and initializing them for a flow.

Example

import time

from prefect import flow, task

@task
def shout(number):
    time.sleep(0.5)
    print(f"#{number}")

@flow
def count_to(highest_number):
    for number in range(highest_number):
        shout.submit(number)

if __name__ == "__main__":
    count_to(10)

# outputs
#0
#1
#2
#3
#4
#5
#6
#7
#8
#9

Switching to a DaskTaskRunner:

import time

from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def shout(number):
    time.sleep(0.5)
    print(f"#{number}")

@flow(task_runner=DaskTaskRunner)
def count_to(highest_number):
    for number in range(highest_number):
        shout.submit(number)

if __name__ == "__main__":
    count_to(10)

# outputs
#3
#7
#2
#6
#4
#0
#1
#5
#8
#9

DaskTaskRunner

Bases: BaseTaskRunner

A parallel task_runner that submits tasks to the dask.distributed scheduler. By default a temporary distributed.LocalCluster is created (and subsequently torn down) within the start() contextmanager. To use a different cluster class (e.g. dask_kubernetes.KubeCluster), you can specify cluster_class/cluster_kwargs.

Alternatively, if you already have a dask cluster running, you can provide the cluster object via the cluster kwarg or the address of the scheduler via the address kwarg.

Multiprocessing safety

Note that, because the DaskTaskRunner uses multiprocessing, calls to flows in scripts must be guarded with if __name__ == "__main__": or warnings will be displayed.

Parameters:

Name	Type	Description	Default
cluster	Cluster	Currently running dask cluster; if one is not provider (or specified via address kwarg), a temporary cluster will be created in DaskTaskRunner.start(). Defaults to None.	None
address	string	Address of a currently running dask scheduler. Defaults to None.	None
cluster_class	string or callable	The cluster class to use when creating a temporary dask cluster. Can be either the full class name (e.g. "distributed.LocalCluster"), or the class itself.	None
cluster_kwargs	dict	Additional kwargs to pass to the cluster_class when creating a temporary dask cluster.	None
adapt_kwargs	dict	Additional kwargs to pass to cluster.adapt when creating a temporary dask cluster. Note that adaptive scaling is only enabled if adapt_kwargs are provided.	None
client_kwargs	dict	Additional kwargs to use when creating a dask.distributed.Client.	None

Examples:

Using a temporary local dask cluster:

from prefect import flow
from prefect_dask.task_runners import DaskTaskRunner

@flow(task_runner=DaskTaskRunner)
def my_flow():
    ...

Using a temporary cluster running elsewhere. Any Dask cluster class should work, here we use dask-cloudprovider:

DaskTaskRunner(
    cluster_class="dask_cloudprovider.FargateCluster",
    cluster_kwargs={
        "image": "prefecthq/prefect:latest",
        "n_workers": 5,
    },
)

Connecting to an existing dask cluster:

DaskTaskRunner(address="192.0.2.255:8786")

Source code in prefect_dask/task_runners.py

class DaskTaskRunner(BaseTaskRunner):
    """
    A parallel task_runner that submits tasks to the `dask.distributed` scheduler.
    By default a temporary `distributed.LocalCluster` is created (and
    subsequently torn down) within the `start()` contextmanager. To use a
    different cluster class (e.g.
    [`dask_kubernetes.KubeCluster`](https://kubernetes.dask.org/)), you can
    specify `cluster_class`/`cluster_kwargs`.

    Alternatively, if you already have a dask cluster running, you can provide
    the cluster object via the `cluster` kwarg or the address of the scheduler
    via the `address` kwarg.
    !!! warning "Multiprocessing safety"
        Note that, because the `DaskTaskRunner` uses multiprocessing, calls to flows
        in scripts must be guarded with `if __name__ == "__main__":` or warnings will
        be displayed.

    Args:
        cluster (distributed.deploy.Cluster, optional): Currently running dask cluster;
            if one is not provider (or specified via `address` kwarg), a temporary
            cluster will be created in `DaskTaskRunner.start()`. Defaults to `None`.
        address (string, optional): Address of a currently running dask
            scheduler. Defaults to `None`.
        cluster_class (string or callable, optional): The cluster class to use
            when creating a temporary dask cluster. Can be either the full
            class name (e.g. `"distributed.LocalCluster"`), or the class itself.
        cluster_kwargs (dict, optional): Additional kwargs to pass to the
            `cluster_class` when creating a temporary dask cluster.
        adapt_kwargs (dict, optional): Additional kwargs to pass to `cluster.adapt`
            when creating a temporary dask cluster. Note that adaptive scaling
            is only enabled if `adapt_kwargs` are provided.
        client_kwargs (dict, optional): Additional kwargs to use when creating a
            [`dask.distributed.Client`](https://distributed.dask.org/en/latest/api.html#client).

    Examples:
        Using a temporary local dask cluster:
        ```python
        from prefect import flow
        from prefect_dask.task_runners import DaskTaskRunner

        @flow(task_runner=DaskTaskRunner)
        def my_flow():
            ...
        ```

        Using a temporary cluster running elsewhere. Any Dask cluster class should
        work, here we use [dask-cloudprovider](https://cloudprovider.dask.org):
        ```python
        DaskTaskRunner(
            cluster_class="dask_cloudprovider.FargateCluster",
            cluster_kwargs={
                "image": "prefecthq/prefect:latest",
                "n_workers": 5,
            },
        )
        ```

        Connecting to an existing dask cluster:
        ```python
        DaskTaskRunner(address="192.0.2.255:8786")
        ```
    """

    def __init__(
        self,
        cluster: Optional[distributed.deploy.Cluster] = None,
        address: str = None,
        cluster_class: Union[str, Callable] = None,
        cluster_kwargs: dict = None,
        adapt_kwargs: dict = None,
        client_kwargs: dict = None,
    ):
        # Validate settings and infer defaults
        if address:
            if cluster or cluster_class or cluster_kwargs or adapt_kwargs:
                raise ValueError(
                    "Cannot specify `address` and "
                    "`cluster`/`cluster_class`/`cluster_kwargs`/`adapt_kwargs`"
                )
        elif cluster:
            if cluster_class or cluster_kwargs:
                raise ValueError(
                    "Cannot specify `cluster` and `cluster_class`/`cluster_kwargs`"
                )
            if not cluster.asynchronous:
                raise ValueError(
                    "The cluster must have `asynchronous=True` to be "
                    "used with `DaskTaskRunner`."
                )
        else:
            if isinstance(cluster_class, str):
                cluster_class = from_qualified_name(cluster_class)
            else:
                cluster_class = cluster_class

        # Create a copies of incoming kwargs since we may mutate them
        cluster_kwargs = cluster_kwargs.copy() if cluster_kwargs else {}
        adapt_kwargs = adapt_kwargs.copy() if adapt_kwargs else {}
        client_kwargs = client_kwargs.copy() if client_kwargs else {}

        # Update kwargs defaults
        client_kwargs.setdefault("set_as_default", False)

        # The user cannot specify async/sync themselves
        if "asynchronous" in client_kwargs:
            raise ValueError(
                "`client_kwargs` cannot set `asynchronous`. "
                "This option is managed by Prefect."
            )
        if "asynchronous" in cluster_kwargs:
            raise ValueError(
                "`cluster_kwargs` cannot set `asynchronous`. "
                "This option is managed by Prefect."
            )

        # Store settings
        self.address = address
        self.cluster_class = cluster_class
        self.cluster_kwargs = cluster_kwargs
        self.adapt_kwargs = adapt_kwargs
        self.client_kwargs = client_kwargs

        # Runtime attributes
        self._client: "distributed.Client" = None
        self._cluster: "distributed.deploy.Cluster" = cluster
        self._dask_futures: Dict[str, "distributed.Future"] = {}

        super().__init__()

    @property
    def concurrency_type(self) -> TaskConcurrencyType:
        return (
            TaskConcurrencyType.PARALLEL
            if self.cluster_kwargs.get("processes")
            else TaskConcurrencyType.CONCURRENT
        )

    def duplicate(self):
        """
        Create a new instance of the task runner with the same settings.
        """
        return type(self)(
            address=self.address,
            cluster_class=self.cluster_class,
            cluster_kwargs=self.cluster_kwargs,
            adapt_kwargs=self.adapt_kwargs,
            client_kwargs=self.client_kwargs,
        )

    def __eq__(self, other: object) -> bool:
        """
        Check if an instance has the same settings as this task runner.
        """
        if type(self) == type(other):
            return (
                self.address == other.address
                and self.cluster_class == other.cluster_class
                and self.cluster_kwargs == other.cluster_kwargs
                and self.adapt_kwargs == other.adapt_kwargs
                and self.client_kwargs == other.client_kwargs
            )
        else:
            return NotImplemented

    async def submit(
        self,
        key: UUID,
        call: Callable[..., Awaitable[State[R]]],
    ) -> None:
        if not self._started:
            raise RuntimeError(
                "The task runner must be started before submitting work."
            )

        # unpack the upstream call in order to cast Prefect futures to Dask futures
        # where possible to optimize Dask task scheduling
        call_kwargs = self._optimize_futures(call.keywords)

        if "task_run" in call_kwargs:
            task_run = call_kwargs["task_run"]
            flow_run = FlowRunContext.get().flow_run
            # Dask displays the text up to the first '-' as the name; the task run key
            # should include the task run name for readability in the Dask console.
            # For cases where the task run fails and reruns for a retried flow run,
            # the flow run count is included so that the new key will not match
            # the failed run's key, therefore not retrieving from the Dask cache.
            dask_key = f"{task_run.name}-{task_run.id.hex}-{flow_run.run_count}"
        else:
            dask_key = str(key)

        self._dask_futures[key] = self._client.submit(
            call.func,
            key=dask_key,
            # Dask defaults to treating functions are pure, but we set this here for
            # explicit expectations. If this task run is submitted to Dask twice, the
            # result of the first run should be returned. Subsequent runs would return
            # `Abort` exceptions if they were submitted again.
            pure=True,
            **call_kwargs,
        )

    def _get_dask_future(self, key: UUID) -> "distributed.Future":
        """
        Retrieve the dask future corresponding to a Prefect future.
        The Dask future is for the `run_fn`, which should return a `State`.
        """
        return self._dask_futures[key]

    def _optimize_futures(self, expr):
        def visit_fn(expr):
            if isinstance(expr, PrefectFuture):
                dask_future = self._dask_futures.get(expr.key)
                if dask_future is not None:
                    return dask_future
            # Fallback to return the expression unaltered
            return expr

        return visit_collection(expr, visit_fn=visit_fn, return_data=True)

    async def wait(self, key: UUID, timeout: float = None) -> Optional[State]:
        future = self._get_dask_future(key)
        try:
            return await future.result(timeout=timeout)
        except distributed.TimeoutError:
            return None
        except BaseException as exc:
            return await exception_to_crashed_state(exc)

    async def _start(self, exit_stack: AsyncExitStack):
        """
        Start the task runner and prep for context exit.
        - Creates a cluster if an external address is not set.
        - Creates a client to connect to the cluster.
        - Pushes a call to wait for all running futures to complete on exit.
        """

        if self._cluster:
            self.logger.info(f"Connecting to existing Dask cluster {self._cluster}")
            self._connect_to = self._cluster
            if self.adapt_kwargs:
                self._cluster.adapt(**self.adapt_kwargs)
        elif self.address:
            self.logger.info(
                f"Connecting to an existing Dask cluster at {self.address}"
            )
            self._connect_to = self.address
        else:
            self.cluster_class = self.cluster_class or distributed.LocalCluster

            self.logger.info(
                f"Creating a new Dask cluster with "
                f"`{to_qualified_name(self.cluster_class)}`"
            )
            self._connect_to = self._cluster = await exit_stack.enter_async_context(
                self.cluster_class(asynchronous=True, **self.cluster_kwargs)
            )
            if self.adapt_kwargs:
                adapt_response = self._cluster.adapt(**self.adapt_kwargs)
                if inspect.isawaitable(adapt_response):
                    await adapt_response

        self._client = await exit_stack.enter_async_context(
            distributed.Client(
                self._connect_to, asynchronous=True, **self.client_kwargs
            )
        )

        if self._client.dashboard_link:
            self.logger.info(
                f"The Dask dashboard is available at {self._client.dashboard_link}",
            )

    def __getstate__(self):
        """
        Allow the `DaskTaskRunner` to be serialized by dropping
        the `distributed.Client`, which contains locks.
        Must be deserialized on a dask worker.
        """
        data = self.__dict__.copy()
        data.update({k: None for k in {"_client", "_cluster", "_connect_to"}})
        return data

    def __setstate__(self, data: dict):
        """
        Restore the `distributed.Client` by loading the client on a dask worker.
        """
        self.__dict__.update(data)
        self._client = distributed.get_client()

duplicate

Create a new instance of the task runner with the same settings.

Source code in prefect_dask/task_runners.py

def duplicate(self):
    """
    Create a new instance of the task runner with the same settings.
    """
    return type(self)(
        address=self.address,
        cluster_class=self.cluster_class,
        cluster_kwargs=self.cluster_kwargs,
        adapt_kwargs=self.adapt_kwargs,
        client_kwargs=self.client_kwargs,
    )