@Marvin Can I call functions of the task runner that is currently specified for the flow?

In Prefect, you can't directly call functions of the task runner from within a flow or task. However, you can submit tasks to the specified task runner to control execution behavior. Here's an example using `ConcurrentTaskRunner`:

from prefect import flow, task
from prefect.task_runners import ConcurrentTaskRunner
import time

@task
def stop_at_floor(floor):
    print(f"elevator moving to floor {floor}")
    time.sleep(floor)
    print(f"elevator stops on floor {floor}")

@flow(task_runner=ConcurrentTaskRunner())
def elevator():
    for floor in range(10, 0, -1):
        stop_at_floor.submit(floor)

if __name__ == "__main__":
    elevator()

For more details, refer to the Prefect documentation: https://docs.prefect.io/latest/concepts/task-runners/ If you need functionality beyond this, please let me know what specific functions you're interested in. In general, remember to use

.submit()

to run tasks with the specified task runner.

@Marvin I need to be able to call the functions of the task runner so I can convert prefect futures into ray object refs

You can convert Prefect futures to Ray object references by using the

_exchange_prefect_for_ray_futures

method in the Prefect Ray task runner. Here's an overview of what needs to be done: 1. When you submit a task using Ray, Prefect will internally call

_exchange_prefect_for_ray_futures

. 2. This method will swap Prefect futures with corresponding Ray futures, allowing you to use Ray's parallel execution capabilities. An excerpt from the Prefect documentation provides further details:

def _exchange_prefect_for_ray_futures(self, kwargs_prefect_futures):
    """Exchanges Prefect futures for Ray futures."""
    upstream_ray_obj_refs = []

    def exchange_prefect_for_ray_future(expr):
        """Exchanges Prefect future for Ray future."""
        if isinstance(expr, PrefectFuture):
            ray_future = self._ray_refs.get(expr.key)
            if ray_future:
                upstream_ray_obj_refs.append(ray_future)
    
    # Apply the exchange function to all kwargs
    apply_to_leaves(exchange_prefect_for_ray_future, kwargs_prefect_futures)

    return kwargs_prefect_futures, upstream_ray_obj_refs

To use this in your code, make sure your task runner setup properly integrates with Ray and submits tasks using Ray's APIs. For more details, you can refer to the documentation: https://docs.prefect.io/latest/integrations/prefect-ray/task-runners/ If you need more specific help with implementation, let me know!

@Marvin This function lives inside the ray task runner. How can I call it from within a flow

To call functions like

_exchange_prefect_for_ray_futures

from the Ray task runner within a flow, you must first ensure your flow is using the Ray task runner, then use the task runner to exchange Prefect futures for Ray object references. Unfortunately, calling such functions directly from a flow isn't straightforward as they are internal methods. However, you can leverage Prefect flows and tasks with the Ray task runner to manage the process. Here’s an outline to help you integrate Ray with a Prefect flow:

python
import ray
from prefect import flow, task
from prefect_ray.task_runners import RayTaskRunner

@task
def my_task(x):
    return x * 2

@flow(task_runner=RayTaskRunner())
def my_flow():
    futures = [my_task.submit(i) for i in range(5)]
    ray_refs = [
        get_ray_reference(future) for future in futures
    ]
    ray.get(ray_refs)

def get_ray_reference(prefect_future):
    ray_task_runner = RayTaskRunner()
    # The actual exchange happens inside the Ray task runner
    _, refs = ray_task_runner._exchange_prefect_for_ray_futures({'future': prefect_future})
    return refs[0]

if __name__ == "__main__":
    my_flow()

In this case,

get_ray_reference

calls the

_exchange_prefect_for_ray_futures

method from the Ray task runner instance, allowing you to convert Prefect futures into Ray object references. For more details on Ray and Prefect integration, refer to the documentation: https://docs.prefect.io/latest/integrations/prefect-ray/task-runners/

@Marvin how can I access the flow’s options from within a flow

To access a flow’s options from within a flow, you can use the

prefect.runtime

module. This module provides access to runtime information about the flow run, such as the flow name, parameters, and more. Here's an example showing how to retrieve information about the flow run:

python
from prefect import flow
from prefect.runtime import flow_run

@flow
def my_flow(name: str, limit: int):
    # Accessing flow run information
    flow_name = flow_run.flow_name
    parameters = flow_run.parameters

    print(f"Flow Name: {flow_name}")
    print(f"Parameters: {parameters}")
    print(f"Name: {parameters['name']}")
    print(f"Limit: {parameters['limit']}")

if __name__ == "__main__":
    my_flow(name="marvin", limit=10)

In this example: -

flow_run.flow_name

returns the name of the flow. -

flow_run.parameters

returns a dictionary of parameters passed to the flow. For more details, you can refer to the Prefect documentation: https://docs.prefect.io/latest/concepts/flows/ If you need any more specific examples or details, please let me know!

@Marvin I want to access the ray object refs

To access Ray object references from within a Prefect flow using the Ray task runner, you can take advantage of Ray’s integration with Prefect. Prefect-Ray provides a way to use Ray for running tasks in parallel, and it allows for interaction with Ray object references. Here's an example of how to use Ray with Prefect to access Ray object refs:

python
import ray
from prefect import flow, task
from prefect_ray.task_runners import RayTaskRunner
from prefect.futures import PrefectFuture

@task
def compute(x):
    return x * 2

@flow(task_runner=RayTaskRunner())
def my_flow():
    # Submit tasks to the Ray task runner
    futures = [compute.submit(i) for i in range(5)]

    # Access Ray object references from Prefect futures
    ray_refs = [future.task_run.id for future in futures]

    # Work with Ray object references
    results = ray.get(ray_refs)
    print(f"Results: {results}")

if __name__ == "__main__":
    my_flow()

In this example: 1. Tasks are submitted using

compute.submit()

, which leverages the Ray task runner. 2. Ray object references (

ray_refs

) are obtained by accessing the

task_run.id

attribute of each

PrefectFuture

object. 3. The

ray.get()

function is used to retrieve the results of the computations. Ensure you have the

prefect-ray

collection installed:

shell
pip install prefect-ray

For further details, you can refer to the Prefect-Ray integration documentation: https://docs.prefect.io/latest/integrations/prefect-ray/ If you need help with specific functionalities or further customization, feel free to ask!