@Marvin why are my DaskTaskRunner workers dying when running a task? I’m getting this error

Crash detected! Execution was interrupted by an unexpected exception: KilledWorker: Attempted to run task 'extract_episodes-0-0c7c0a9cd7f24e0bb9b4d8543515b33e-1' on 4 different workers, but all those workers died while running it. The last worker that attempt to run the task was <tcp://127.0.0.1:34701>. Inspecting worker logs is often a good next step to diagnose what went wrong. For more information see <https://distributed.dask.org/en/stable/killed.html>.

Oh joy, another problem to solve. The error indicates that your Dask workers are being killed while attempting to run the task 'extract_episodes'. Let's approach this step-by-step: 1. Check Worker Logs: As the error suggests, the first step is to inspect the logs of the workers that died. This can give you clues regarding the cause of the crash, such as memory errors, missing dependencies, or other exceptions. 2. Resource Management: Ensure that your tasks are not exceeding the available resources (memory, CPU). Sometimes tasks that require more resources than a worker can provide can cause the worker to terminate. 3. Task Requirements: Check if there are any specific resource constraints for the task that might be causing the issue. 4. Dask Configuration: Review your Dask configuration and make sure everything is set correctly. You may need to adjust the memory limit or the number of worker threads. 5. Prefect Compatibility: Ensure that you're using compatible versions of Prefect and Dask. Sometimes mismatches can result in unexpected behavior. Let me gather more specific information from the Prefect and Dask documentation to provide detailed guidance. I’ll do a search for relevant issues and documentation. Let's parallelize the search for Dask and Prefect resources to get you accurate information quickly. Ah, the toil never ends. At least the GitHub search yielded a definitive result: no related issues found. Since the queries didn't quite hit the mark, we'll need a different approach. Let’s refine the search in the Prefect documentation and try looking for other relevant terms that might lead us to a solution. We'll search again with queries specific to worker logs, resource management, and task requirements in Dask and Prefect contexts. It seems I'm hitting a bit of a snag with querying the Prefect documentation. Marvelous. But fret not, there are other ways to skin this cat. Let's take a look at some likely causes and steps you can try: 1. Checking Resource Limits: Ensure your workers have enough memory and CPU to handle the tasks. Here's a basic example of how to set resource limits in Dask:

python
from dask.distributed import Client

# Adjust memory and CPU limits as necessary
client = Client(n_workers=4, threads_per_worker=1, memory_limit='2GB')

2. Inspect Worker Logs: Check the logs of the affected workers. This can often reveal if they are running out of memory, missing packages, or encountering other fatal errors. 3. Profile the Task: It might be helpful to add some logging inside the

extract_episodes

task to see if any particular data or operation is causing the crash. 4. Increase Timeout: Sometimes, increasing the task timeout can help if tasks are getting killed due to taking too long. 5. Check for Library Incompatibilities: Make sure all your dependencies, including Prefect and Dask, are compatible with each other. Let's try a different approach to fetch some code examples that might help. Here's a simple example to ensure that your basic setup with

DaskTaskRunner

is correct. Try running a straightforward flow to see if the problem is specific to your

extract_episodes

task or more general.

python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

@task
def add(x: int, y: int) -> int:
    return x + y

@flow(task_runner=DaskTaskRunner())
def my_simple_flow():
    first_result = add(1, 2)
    second_result = add(first_result, first_result)
    return add(first_result, second_result)

if __name__ == "__main__":
    my_simple_flow()

This will help identify if the issue persists with this basic flow. If it runs without issues, it suggests that there's something specific about your

extract_episodes

task that needs further inspection. To summarize: - Check worker logs for specific errors. - Adjust resource limits for memory and CPU. - Increase task timeout if necessary.

@Marvin how can I see the logs from DaskTaskRunner?

Of course, you'll want to sift through logs to uncover the lurking issues. Let’s look into how to enable and access logs within Prefect when using

DaskTaskRunner

. ### Enabling Logs First, ensure that logging is configured. You can configure logging in your Prefect flow like this:

python
import logging
from prefect import flow, task

# Configure logging
logging.basicConfig(level=logging.DEBUG)

@task
def sample_task(x):
    <http://logging.info|logging.info>(f"Running task with value: {x}")
    return x * 2

@flow
def logging_flow():
    result = sample_task(5)
    return result

if __name__ == "__main__":
    logging_flow()

### Dask Logs If you want to access logs specifically from Dask workers, you have to connect to the Dask client and configure it to stream logs. Here’s an approach for more detailed logging:

python
from dask.distributed import Client, WorkerPlugin
import logging

logging.basicConfig(level=logging.DEBUG)

class MyPlugin(WorkerPlugin):
    def setup(self, worker):
        worker.log_event("my-setup", "This happens just once per worker")

    def teardown(self, worker):
        worker.log_event("my-teardown", "This happens just once per worker")

@flow
def dask_flow():
    with Client(n_workers=4, threads_per_worker=1, memory_limit='2GB') as client:
        client.register_worker_plugin(MyPlugin())
        future = client.submit(lambda x: x + 1, 10)
        print(future.result())

if __name__ == "__main__":
    dask_flow()

### Access Logs from Dask Dashboard Dask also provides a very handy web-based dashboard to monitor and inspect logs, tasks, and cluster state. To access the dashboard: 1. Start your Dask Client within the flow:

python
   from dask.distributed import Client
   client = Client()

2. Navigate to

<http://localhost:8787>

in your web browser to access the Dask dashboard. This dashboard provides comprehensive insights into task executions, worker statuses, and resource usage. ### Prefect Configuration for Log Redirect To ensure Prefect logs tasks and execution details properly, you might want to ensure the configuration is set up to redirect Dask logs into Prefect's logging system.

python
from prefect_dask import DaskTaskRunner
from prefect import flow, task
import prefect

@task
def add(x, y):
    logger = prefect.context.get("logger")
    <http://logger.info|logger.info>(f"Adding {x} and {y}")
    return x + y

@flow(task_runner=DaskTaskRunner)
def add_flow(x, y):
    return add(x, y)

if __name__ == "__main__":
    add_flow(1, 2)

### Summary 1. Enable Logging with

logging.basicConfig(level=logging.DEBUG)

. 2. Use Dask Client to manage the cluster and access logs via the Dask dashboard. 3. Redirect Logs from Dask tasks to Prefect logs for easy tracking. Navigating through logs might feel as joyful as a trip to the dentist, but it usually uncovers the root of the problem. Best of luck.