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# task_runners
# `prefect_ray.task_runners`
Interface and implementations of the Ray Task Runner.
[Task Runners](https://docs.prefect.io/latest/develop/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:
```python theme={null}
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 `RayTaskRunner`:
```python theme={null}
import time
from prefect import flow, task
from prefect_ray import RayTaskRunner
@task
def shout(number):
time.sleep(0.5)
print(f"#{number}")
@flow(task_runner=RayTaskRunner)
def count_to(highest_number):
shout.map(range(highest_number)).wait()
if __name__ == "__main__":
count_to(10)
# outputs
#3
#7
#2
#6
#4
#0
#1
#5
#8
#9
```
## Classes
### `PrefectRayFuture`
**Methods:**
#### `add_done_callback`
```python theme={null}
add_done_callback(self, fn: Callable[['PrefectRayFuture[R]'], Any])
```
#### `result`
```python theme={null}
result(self, timeout: float | None = None, raise_on_failure: bool = True) -> R
```
#### `wait`
```python theme={null}
wait(self, timeout: float | None = None) -> None
```
### `RayTaskRunner`
A parallel task\_runner that submits tasks to `ray`.
By default, a temporary Ray cluster is created for the duration of the flow run.
Alternatively, if you already have a `ray` instance running, you can provide
the connection URL via the `address` kwarg.
Args:
address (string, optional): Address of a currently running `ray` instance; if
one is not provided, a temporary instance will be created.
init\_kwargs (dict, optional): Additional kwargs to use when calling `ray.init`.
Examples:
Using a temporary local ray cluster:
```python theme={null}
from prefect import flow
from prefect_ray.task_runners import RayTaskRunner
@flow(task_runner=RayTaskRunner())
def my_flow():
...
```
Connecting to an existing ray instance:
```python theme={null}
RayTaskRunner(address="ray://:10001")
```
**Methods:**
#### `duplicate`
```python theme={null}
duplicate(self)
```
Return a new instance of with the same settings as this one.
#### `map`
```python theme={null}
map(self, task: 'Task[P, Coroutine[Any, Any, R]]', parameters: dict[str, Any], wait_for: Iterable[PrefectFuture[Any]] | None = None) -> PrefectFutureList[PrefectRayFuture[R]]
```
#### `map`
```python theme={null}
map(self, task: 'Task[Any, R]', parameters: dict[str, Any], wait_for: Iterable[PrefectFuture[Any]] | None = None) -> PrefectFutureList[PrefectRayFuture[R]]
```
#### `map`
```python theme={null}
map(self, task: 'Task[P, R]', parameters: dict[str, Any], wait_for: Iterable[PrefectFuture[Any]] | None = None) -> PrefectFutureList[PrefectRayFuture[R]]
```
#### `submit`
```python theme={null}
submit(self, task: 'Task[P, Coroutine[Any, Any, R]]', parameters: dict[str, Any], wait_for: Iterable[PrefectFuture[Any]] | None = None, dependencies: dict[str, set[RunInput]] | None = None) -> PrefectRayFuture[R]
```
#### `submit`
```python theme={null}
submit(self, task: 'Task[P, R]', parameters: dict[str, Any], wait_for: Iterable[PrefectFuture[Any]] | None = None, dependencies: dict[str, set[RunInput]] | None = None) -> PrefectRayFuture[R]
```
#### `submit`
```python theme={null}
submit(self, task: Task[P, R], parameters: dict[str, Any], wait_for: Iterable[PrefectFuture[Any]] | None = None, dependencies: dict[str, set[RunInput]] | None = None)
```
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