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<@ULVA73B9P> I'm running a simple flow that takes ...
# ask-marvin
d
@Marvin I'm running a simple flow that takes a list of integers and applies a task to each integer (the task squares the integer passed in). The task is called square(x), where x is an input integer. I get multiple PicklingErrors like this one...
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19:42:53.712 | ERROR   | distributed.protocol.pickle - Failed to serialize LLGExpr(dsk={'square-decd42c0f8b783828ecbe93454388f66': <Task 'square-decd42c0f8b783828ecbe93454388f66' square(, ...)>}).
Traceback (most recent call last):
  File "/usr/local/lib/python3.13/site-packages/distributed/protocol/pickle.py", line 60, in dumps
    result = pickle.dumps(x, **dump_kwargs)
_pickle.PicklingError: Can't pickle <function square at 0x7d4fe6dc4360>: it's not the same object as __main__.square
I believe this is caused by me using a task_runner (Dask) and having a .submit() or .map() within my flow. Could this be the reason why? If not, is there any other area I should look at?
m
✅ thought for 34.7 seconds
Your diagnosis is correct. This error occurs when using Dask as a task runner, and it's specifically related to how Python handles function serialization. Here's what's happening and how to fix it: ### The Problem The error occurs because Dask needs to serialize (pickle) your function to send it to worker processes, but it can't find the function in the expected location. When your function is defined in the 
__main__
module (i.e., the script you're running directly), but needs to be used in a different process, Python can't properly pickle it. ### Solutions 1. Move the function to a separate module: Instead of defining your function in the main script, move it to a separate Python file:
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python
   # tasks.py
   from prefect import task

   @task
   def square(x):
       return x * x
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python
   # main.py
   from prefect import flow
   from tasks import square

   @flow
   def process_numbers(numbers):
       return square.map(numbers)
2. Use 
if __name__ == "__main__"
pattern: If you prefer keeping everything in one file:
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python
   from prefect import flow, task

   @task
   def square(x):
       return x * x

   @flow
   def process_numbers(numbers):
       return square.map(numbers)

   if __name__ == "__main__":
       result = process_numbers([1, 2, 3, 4, 5])
3. Ensure proper imports: If your function uses any external libraries, make sure they're imported at the module level, not inside the function. ### Best Practices 1. Always define tasks in separate modules when using distributed task runners like Dask 2. Use proper Python packaging practices to make your code importable 3. Avoid defining functions inside other functions when using Dask 4. Make sure all dependencies are available in the worker environment ### Additional Notes - This issue is specific to distributed task runners (like Dask) and won't occur with the default synchronous task runner - The error occurs because Dask needs to serialize both the function and its closure environment - Similar issues can occur with lambda functions or functions defined inside other functions If you're still experiencing issues after implementing these solutions, it would be helpful to see your complete code structure and how you're setting up the Dask task runner.
d
@Marvin Note that I'm using the dask taskrunner, so this needs to specified in the flow as well. Currently I get this error...
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RuntimeError: Unable to determine task runner to use for mapped task runs. If you are mapping a task outside of a flow, please provide `deferred=True` to submit the mapped task runs for deferred execution.
m
✅ thought for 8.0 seconds
Ah yes, let me provide you with the complete solution that includes the Dask task runner configuration. This error occurs because the flow needs to be explicitly configured with the Dask task runner. Here's the complete working solution:
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python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def square(x):
    return x * x

@flow(task_runner=DaskTaskRunner())
def process_numbers(numbers):
    return square.map(numbers)

if __name__ == "__main__":
    result = process_numbers([1, 2, 3, 4, 5])
However, before you run this, you'll need to install the Dask task runner package:
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bash
pip install prefect-dask
A few important notes: 1. The 
DaskTaskRunner()
can take several configuration options. By default, it will create a temporary local Dask cluster. 2. If you need to configure the Dask cluster, you can pass parameters to the task runner:
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python
@flow(task_runner=DaskTaskRunner(
    cluster_kwargs={"n_workers": 2},  # Number of workers
    adapt_kwargs={"minimum": 1, "maximum": 4}  # Enable adaptive scaling
))
If you're still getting the pickling error after implementing this, make sure: 1. Your code is properly structured (as mentioned in the previous response) 2. The function is defined at the module level (not nested inside another function) 3. All necessary imports are at the top of the file 4. You're not using any lambda functions or inline function definitions Would you like me to provide any additional details about specific Dask task runner configurations or troubleshooting steps?
d
@Marvin What would be a good way to check whether my prefect flow is submitting tasks to a local cluster or to a remote (on ECS Fargate) cluster? Is there a log option I can print or is there a variable I can print out?
m
✅ thought for 57.7 seconds
Here's how you can check where your flow is running: 1. For a basic check, you can use the 
flow_run
attributes from the runtime module. Here's a simple example:
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python
from prefect import flow
from prefect.runtime import flow_run

@flow
def my_flow():
    print(f"Flow Run ID: {flow_run.id}")
    print(f"Job Variables: {flow_run.job_variables}")
    # job_variables will contain worker-specific information
    
    # You can also get the UI URL to see more details
    print(f"UI URL: {flow_run.ui_url}")
2. For more detailed information about the execution environment, you can use 
job_variables
. When running on ECS Fargate, this will include Fargate-specific information. When running locally, it will be empty or contain local environment details. 3. You can also check the flow run's tags, which often include information about the worker pool:
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python
@flow
def my_flow():
    print(f"Flow Tags: {flow_run.tags}")
4. The most definitive way is to check the UI URL (
flow_run.ui_url
) and inspect the flow run details in the Prefect UI, which will show you the work pool and infrastructure type being used. Remember that when running flows locally without being connected to a Prefect server, some of these attributes might be empty or None. The presence of certain attributes (like job_variables with ECS-specific information) can itself be an indicator of where the flow is running. For debugging purposes, you might want to add this kind of check at the start of your flow:
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python
from prefect import flow
from prefect.runtime import flow_run

@flow
def my_flow():
    # Print execution environment information
    print(f"Flow Run ID: {flow_run.id}")
    print(f"Job Variables: {flow_run.job_variables}")
    print(f"Flow Tags: {flow_run.tags}")
    print(f"UI URL: {flow_run.ui_url}")
    
    # Your actual flow logic here
    ...
This will give you a clear picture of where and how your flow is being executed each time it runs.
d
@Marvin This just shows where the flow is being hosted (through Prefect UI), I wanted to find information on which cluster is being used (local or not). To clarify, I'm using the task_runner parameter within my flow to specify which cluster I want to use (which should be an already existing ECS Fargate cluster I have set up)
m
✅ thought for 37.6 seconds
I apologize for my previous response and need to correct a misunderstanding. I notice you're asking about clusters, and I should clarify something important: In Prefect 3.x, the concept of Dask clusters for task execution has been replaced with a different execution model. If you're trying to determine where your tasks are actually executing, there are a few key things to understand: 1. In Prefect 3.x, tasks run in the same environment as the flow by default. There's no separate cluster for task execution like there was with Dask in earlier versions. 2. If you need distributed task execution, you would: - Either use a work pool with a suitable infrastructure type (like ECS, Kubernetes, etc.) - Or integrate with a specific task execution framework of your choice If you're specifically working with ECS Fargate, the way to check where your tasks are running would be to: 1. Log the container/environment information:
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python
import os
import socket
from prefect import task, flow

@task
def check_execution_environment():
    return {
        "hostname": socket.gethostname(),
        "environment_vars": dict(os.environ),
        "current_path": os.getcwd()
    }

@flow
def my_flow():
    env_info = check_execution_environment()
    print(f"Task executing on: {env_info}")
2. Check for ECS-specific environment variables that are automatically set in Fargate containers:
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python
@task
def check_if_on_ecs():
    ecs_container_metadata_uri = os.getenv("ECS_CONTAINER_METADATA_URI_V4")
    if ecs_container_metadata_uri:
        return f"Running on ECS Fargate: {ecs_container_metadata_uri}"
    return "Running locally"
If you're coming from Prefect 2.x with Dask or if you're specifically trying to use Dask with Prefect 3.x, you might want to clarify your use case. Are you: 1. Using Prefect 2.x with Dask? 2. Looking to implement distributed task execution in Prefect 3.x? 3. Or trying to solve a specific scaling/distribution problem? This would help me provide more targeted guidance for your situation.
✅ thought for 51.2 seconds
Based on your clarification about using the task_runner parameter, I realize you're likely using Prefect 2.x with a DaskTaskRunner configured for ECS Fargate. This is important context, as the approach is quite different between Prefect 2.x and 3.x. For Prefect 2.x with a DaskTaskRunner, you can determine which cluster is being used in several ways: 1. The DaskTaskRunner will log the Dask dashboard URL when it creates a cluster. This URL will show you where the cluster is running:
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python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

# Configure your task runner
task_runner = DaskTaskRunner(
    cluster_class="dask_cloudprovider.aws.FargateCluster",
    # your cluster configuration here
)

@flow(task_runner=task_runner)
def my_flow():
    # The dashboard URL will be logged when the flow starts
    # It will look something like: "The Dask dashboard is available at http://[IP]:8787/status"
    ...
2. You can access the task runner's client during flow execution:
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python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

@task
def check_cluster_info():
    from prefect.context import get_run_context
    context = get_run_context()
    task_runner = context.flow_run.task_runner
    if hasattr(task_runner, 'client'):
        return {
            "scheduler_info": task_runner.client.scheduler_info(),
            "dashboard_link": task_runner.client.dashboard_link
        }
    return "No Dask client available - running locally"

@flow(task_runner=your_task_runner)
def my_flow():
    cluster_info = check_cluster_info()
    print(f"Cluster Information: {cluster_info}")
3. Within tasks, you can check the environment variables that are specific to ECS:
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python
@task
def check_execution_environment():
    import os
    ecs_metadata_uri = os.getenv("ECS_CONTAINER_METADATA_URI_V4")
    if ecs_metadata_uri:
        return f"Running on ECS Fargate: {ecs_metadata_uri}"
    return "Running locally"
Important notes: 1. If you're seeing serialization errors or IOLoop closed errors (as shown in some GitHub issues), this might indicate configuration issues with your ECS task runner. 2. Make sure your Dask and distributed package versions match between your local environment and the ECS containers. 3. Consider upgrading to Prefect 3.x, which has a different and more straightforward approach to distributed execution through work pools. Would you like me to provide more specific guidance based on your exact setup (Prefect version, task runner configuration, etc.)?
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