@Marvin Hello. I was wondering if I could get some help here. I am trying to send tasks to raytaskrunner and I understand I can do as follows:

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

@task
def parallel_task(task_id: int):
    return f"Result from task {task_id}"

@task
def join_task(results):
    combined_result = " | ".join(results)
    return f"Joined result: {combined_result}"

@flow(task_runner=RayTaskRunner(address="<ray://raycluster-kuberay-head-svc.kuberay.svc.cluster.local:10001>"))
def fork_and_join_workflow():
    # Fork: Execute 50 parallel tasks
    parallel_results = [parallel_task.submit(i) for i in range(50)]
    
    # Join: Aggregate results from all tasks
    results = [result.result() for result in parallel_results]
    joined_result = join_task(results)
    
    print(joined_result)

if __name__ == "__main__":
    fork_and_join_workflow()

Now I want to allocate 1 GPU and 4 CPU for each task that is submitted over to ray, how can I make sure of that?! In regular ray, I can just decorate a function with @ray.remote(num_gpus=1, num_cpus=4), but I cannot do that with prefect tasks?

To allocate 1 GPU and 4 CPU resources for each task you submit to Ray through Prefect, you need to configure resource allocation within RayTaskRunner. Unfortunately, the Prefect tasks don't directly support annotations like

@ray.remote(num_gpus=1, num_cpus=4)

. However, you can achieve your goal by manually specifying these resources when using the RayTaskRunner. Although there isn't a direct way mentioned in Prefect's documentation to do this just by setting the configuration in

RayTaskRunner

(like you might with Dask), you would typically handle resource constraints within the Ray environment itself. Here's a possible approach based on the general practice with Ray: 1. Specify Resources at Task Definition: You can handle this by setting resource demands when you define the task using Ray directly, and then integrating it with Prefect. 2. Control Resources in Your Ray Cluster: Ensure your Ray cluster is set with appropriate resource constraints, which Prefect’s RayTaskRunner will utilize when sending tasks to the cluster. You might need to manually start your Ray cluster with appropriate resource specifications, ensuring that the Prefect tasks leverage the cluster's configuration. This isn't directly done through Prefect but requires configuring your Ray cluster correctly. For more detailed guidance: - Prefect 3.x Ray Integration - Prefect 2.x Ray and Dask Task Runners I couldn't find a precise example in the GitHub issues regarding your specific query, so you might want to keep an eye on Prefect's GitHub discussions for any updates or community solutions! And as always, try not to let the idea of resource management wear you down—kind of like taking a long walk in a park without any benches... the benches being the correctly allocated CPUs and GPUs. 🪑💻😂