@Marvin Does the RayTaskRunner spin up a Ray worker on my remote cluster to execute its tasks? If so, can I have it persist its outputs to a directory on the Ray worker?

The

RayTaskRunner

in Prefect does indeed spin up a Ray worker on your remote cluster to execute its tasks. According to a GitHub issue, there is a known problem where Prefect tries to reference directories that only exist on the local machine, leading to

FileNotFoundError

errors when running a flow from a laptop against a remote Ray cluster. As for persisting outputs to a directory on the Ray worker, you'll need to handle file paths and data saving explicitly in your tasks to ensure they reference the correct directories. This often involves ensuring that your code writes to a shared storage that is accessible by all nodes in the Ray cluster. For further detail and the latest updates, you may refer to these GitHub issues: - FileNotFoundError errors when running with a remote Ray cluster - RayTaskRunner crashes with pydantic=2.0.0> These links should guide you on the current state of

RayTaskRunner

. If you need more specific examples or help in organizing your task code to write to shared storage, let me know!

But can Prefect persist outputs to a local directory that is accessible to the ray worker/cluster?

There's no direct and specific documentation or GitHub issue detailing how to persist outputs to a local directory accessible to the Ray worker/cluster in Prefect. However, you can achieve this by managing file paths within your tasks to ensure they write outputs to a shared storage location. Here is a general approach to handle this: ### Steps to Persist Outputs to a Directory Accessible by Ray Cluster 1. Ensure Shared Storage: Make sure the directory you are writing to is accessible by all nodes in your Ray cluster. This could be a mounted network file system (NFS) or a cloud storage bucket. 2. Task Code: Modify your task code to write outputs to this shared directory. ### Example Implementation:

python
from prefect import flow, task
from prefect_ray import RayTaskRunner
import os

# Define a shared directory accessible by all nodes in the Ray cluster
SHARED_DIR = "/path/to/shared/storage"

@task
def save_data(data, filename):
    # Ensure the shared directory exists
    os.makedirs(SHARED_DIR, exist_ok=True)
    
    file_path = os.path.join(SHARED_DIR, filename)
    with open(file_path, 'w') as f:
        f.write(data)
    return file_path

@task
def load_data(filename):
    file_path = os.path.join(SHARED_DIR, filename)
    with open(file_path, 'r') as f:
        data = f.read()
    return data

@flow(task_runner=RayTaskRunner())
def main_flow():
    data = "This is some data to persist."
    filename = "output.txt"
    
    # Save data to the shared directory
    saved_file_path = save_data(data, filename)
    
    # Load data from the shared directory
    loaded_data = load_data(filename)
    print(f"Loaded data: {loaded_data}")

if __name__ == "__main__":
    main_flow()

### Summary: - Shared Storage: Utilize a directory that is accessible by your entire Ray cluster. - Modify Tasks: Write tasks to read and write from/to this shared directory. This setup ensures that outputs are accessible to any worker in your Ray cluster. If you have more specific requirements or run into issues, consulting the official Prefect and Ray documentation or their respective GitHub repositories for detailed insights might be helpful. For additional queries, the community forums and GitHub discussions are also good places to seek advice.