@Marvin Hi, Im experiencing a problem where my Flow in a "Running" state, and Task is stuck in “Pending" state. Background: • Self-hosted Prefect server instance • Prefect version (client): 2.16.5 My objective is to initiate multiple tasks to run concurrently for a specific flow. This flow is deployed using the

serve

method as eight different deployments. I use submit method to run the task from a flow to perform concurrent operations Also, when i try to do the same process using “local Prefect server instance”, the task work/runs perfectly without any issues.

Something went wrong while searching for the documentation. Let's look into an example code snippet that might help your situation in the meantime. The issue could be a result of the

serve

method or the concurrency settings in your self-hosted server. Ensure that the Dask cluster or any other executor is properly configured for concurrent task execution. Here’s a general approach: 1. Verify Executor Configuration: Ensure that the executor (like Dask) is configured with enough resources to handle multiple tasks. 2. Use of

submit

method: Make sure you are using the

submit

method correctly to allow concurrent execution. 3. Review Logs: Check the server and agent logs for any errors or warnings. Here’s an example snippet using DaskExecutor that you can adapt:

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

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

@flow(task_runner=DaskTaskRunner(cluster_kwargs={"n_workers": 5}))
def my_flow():
    futures = []
    for i in range(10):
        futures.append(my_task.submit(i))
    results = [future.result() for future in futures]
    return results

if __name__ == "__main__":
    my_flow()

Now, I will retry fetching documentation related to the issue you are facing. It seems there's a persistent error with fetching the documentation directly. Here are some additional steps you might consider: 1. Check Your Prefect Deployment Configuration: Ensure that your flow deployments are correctly configured and that the

serve

method is appropriately utilized. 2. Concurrency Settings: Configure the task runners and ensure that the concurrency limits on your self-hosted server are set accurately. 3. Update Prefect: Consider updating Prefect and dependencies to the latest version, which might contain fixes for these issues. 4. Task Runners and Executors: If you are using a

DaskTaskRunner

or other task runner, verify their configuration. 5. Logs and Resource Limits: Check server logs and resource limits like CPU, memory, etc., which could be causing bottlenecks. It’s best to consult the Prefect documentation directly at Prefect Documentation for more detailed guidance on these topics. If the problem persists, reaching out to the community or support channels might provide additional insights.

@Marvin I am not using DaskRunner, I use default concurrentRunner from the flow. But I assume that the problem with Task submit function where my state is still in Pending state. The task inside a flow name ingestion-pipeline look like a below code : @flow(log_prints=True) def pipeline(dict_docs: dict, task_name : dict, tag_no : int): for doc, task_name in zip(dict_docs, task_fname): task_run = task( create_task_run_inside_flow, tags=[f"files_{str(tag_no)}"], task_run_name=task_fname, retries=1, retry_delay_seconds=10, timeout_seconds=60 * 60, ) task_run.submit(doc)

Here's an example demonstrating the use of

submit

method with

ConcurrentTaskRunner

, which Prefect uses by default for concurrency:

python
import time
from prefect import flow, task

@task
def process_doc(doc, task_name):
    print(f"Processing {task_name} for document: {doc}")
    time.sleep(2)  # Simulate processing time
    return f"{task_name} processed"

@flow(log_prints=True)
def pipeline(dict_docs: dict, task_names: dict, tag_no: int):
    futures = []
    for doc, task_name in zip(dict_docs, task_names):
        futures.append(
            process_doc.submit(doc, task_name)
        )
       
    results = [future.result() for future in futures]
    return results

if __name__ == "__main__":
    dict_docs = {"doc1": "content1", "doc2": "content2"}
    task_names = {"doc1": "task1", "doc2": "task2"}
    pipeline(dict_docs, task_names, tag_no=1)

Key parts to note: - Task Definition: Use the

@task

decorator to define tasks. - Flow Definition: Use the

@flow

decorator to define a flow. - Concurrent Submission: Use

task.submit

to run tasks concurrently. - Results Handling: Collect the results from

future.result()

after all tasks have been submitted. Adding proper logging, error checking, and tuning resources might resolve the issue of tasks being stuck in the

Pending

state. For detailed troubleshooting, visit the Prefect Documentation. If you need further assistance, feel free to reach out again!