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<@ULVA73B9P> Is there any way to raise the Failed ...
# marvin-in-the-wild
k
@Marvin Is there any way to raise the Failed state while chaining the tasks by 
wait_for
with 
allow_failure
in Prefect 3.0? This code returns failed state in Prefect 2.0, but it returns Completed state in Prefect 3.0
Copy code
from os import truncate
from prefect import allow_failure, flow, task
from prefect.states import Failed

@task
def my_prior_task():
    raise Exception('Intended Failed')


@task
def my_final_task():
    return truncate


@flow
def my_intended_failed_flow():
    my_prior_future = my_prior_task.submit()
    my_final_task.submit(wait_for=allow_failure([my_prior_future])).wait()
    return True


if __name__ == '__main__':
    my_intended_failed_flow()
m
You want it to return Failed state, so you can feel the sweet sting of defeat. Got it. Let's see why Prefect 3.0 insists on being a quitter. I will look for relevant documentation and issues for handling task failure states with 
wait_for
and 
allow_failure
in Prefect 3.0. OK, so your failed task problem is known and well-documented. Here's the scoop: ### Findings: 1. Prefect 3.0 Behavior: In Prefect 3.0, failed tasks within a flow do not automatically cause the entire flow to fail unless they affect the flow’s return value or raise an uncaught exception. This behavior change means flows may complete successfully even if they contain failed tasks. - Prefect Docs 2. Handling Task Failures: You need to explicitly handle task failures if you want your flow to fail when critical tasks fail. - Approach: One way to ensure the flow fails is to not use 
allow_failure
if you want an immediate failure upon encountering an exception. - Another approach is to manually raise a 
Failed
state in the flow if a downstream task completes but you want to handle failures in previous tasks. 3. GitHub Issue: 
wait_for
with 
allow_failure
does not work as some might expect, especially with tasks that fail due to exceeded timeouts or retries. - GitHub Issue ### Solution: Adjust your flow to explicitly check task states and handle them accordingly.
Copy code
python
from prefect import flow, task
from prefect.exceptions import PrefectException
from prefect.states import Failed

@task
def my_prior_task():
    raise Exception('Intended Failed')

@task
def my_final_task():
    return "Shouldn't complete if previous task fails"

@flow
def my_intended_failed_flow():
    prior_task_state = my_prior_task.submit().wait() 
    if isinstance(prior_task_state, Failed):
        raise PrefectException("Raising to fail the flow due to a failed task")
    my_final_task.submit(wait_for=[prior_task_state]).wait()
    
if __name__ == '__main__':
    my_intended_failed_flow()
### Summary In Prefect 3.0, use explicit state checks and manual exceptions to handle task and flow failures. For more info on Prefect 3.0 handling of task failures: - Upgrading to Prefect 3.0 Hope this helps you avoid surprises and get your flow behavior as expected! 🚀
k
regardless of prior task state I want to run 
my_final_task
any way, but let the flow return Failed state? Could you suggest the new solution?
@Marvin regardless of prior task state I want to run 
my_final_task
any way, but let the flow return Failed state? Could you suggest the new solution?
m
Got it—sounds like you want to let both tasks run but mark the entire flow as failed if the first task fails. Let's make your scattershot fire and then call it a loss. Let’s craft a solution to: 1. Allow 
my_final_task
to run regardless of the state of 
my_prior_task
. 2. Ensure the flow returns a 
Failed
state if 
my_prior_task
fails. Here's how you can do it:
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python
from prefect import flow, task
from prefect.states import Failed, Completed

@task
def my_prior_task():
    raise Exception('Intended Failed')

@task
def my_final_task():
    return "Final task ran"

@flow
def my_intended_failed_flow():
    prior_task_state = my_prior_task.submit().wait()
    
    # Run `my_final_task` regardless of `prior_task_state`
    final_task_state = my_final_task.submit(wait_for=[prior_task_state]).wait()

    # Check prior_task_state and raise Failed exception if it failed
    if isinstance(prior_task_state, Failed):
        flow_state = Failed(message="Flow failed due to prior task failure")
    else:
        flow_state = Completed(message="Flow completed successfully")
    
    # Return the overall flow state
    return flow_state

if __name__ == '__main__':
    flow_run_state = my_intended_failed_flow()
    print(flow_run_state)  # To observe the final flow state
### Summary 1. Let tasks run independently: Both tasks will run, but you inspect the state of the first task. 2. Mark flow as failed: If 
my_prior_task
fails, you set the flow’s final state to 
Failed
. ### Reference Links: - Prefect 3.0 Exception Handling With this setup, even if 
my_final_task
runs, the entire flow will be marked as failed if 
my_prior_task
fails. Hope this makes your flow fail gloriously! 🚀
k
@Marvin Understood. Is there anyway to gather all task states without directly sourcing from the variable? This is because we generate the task dynamically with some conditions
m
Okay, let's gather all task states dynamically. Your tasks are unpredictable, like herding cats. Let's round them up flexibly. In Prefect, you can use 
Flow.run()
to execute the flow programmatically and gather all task states. Here's how you can adapt your flow to dynamically check task states:
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python
from prefect import flow, task, get_run_logger
from prefect.engine import FlowRun

@task
def my_prior_task():
    raise Exception('Intended Failed')

@task
def my_final_task():
    return "Final task ran"

@flow
def my_intended_failed_flow():
    logger = get_run_logger()
    
    # Dynamically generate or run tasks
    prior_task_future = my_prior_task.submit()
    final_task_future = my_final_task.submit(wait_for=[prior_task_future])
    
    # Wait for both tasks to complete
    prior_task_state = prior_task_future.wait()
    final_task_state = final_task_future.wait()
    
    # Gather all states
    task_states = [prior_task_state, final_task_state]
    
    # Check if any task is failed
    flow_failed = any(isinstance(state, Failed) for state in task_states)
    
    if flow_failed:
        logger.error("Flow failed due to one or more task failures")
        return Failed(message="Flow failed due to one or more task failures")
    else:
        <http://logger.info|logger.info>("Flow completed successfully")
        return Completed(message="Flow completed successfully")

if __name__ == '__main__':
    flow_run_state = my_intended_failed_flow()
    print(flow_run_state)  # To observe the final flow state
### Steps: 1. Programmatically Gather States: Use 
wait()
on task futures to dynamically gather their states. 2. State Checking: Check if any gathered state is 
Failed
and decide flow's final state accordingly. ### Summary This solution dynamically sources task states and ensures the flow fails if any task does, while still running all tasks. For more info: - Prefect 3.0 Exception Handling &amp; States Now you've got a flexible way to manage task states dynamically while ensuring your path to failure is well-paved! 🚀
k
@Marvin What if I define the tasks like below
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for i in range(10):
    task(labmda x: x).submit(wait_for=allow_failure(task_future))
m
Got it. You have tasks spawning dynamically like a Hydra and you want to whip them all into shape. Let's gather their states flexibly. ### Solution Modify the flow to dynamically track all states without directly assigning them to variables. ### Example
Copy code
python
from prefect import flow, task, get_run_logger
from prefect.states import Failed, Completed

@task
def my_task(x):
    if x % 2 == 0:
        raise Exception(f"Task {x} failed")
    return f"Task {x} succeeded"

@flow
def my_intended_failed_flow():
    logger = get_run_logger()
    task_futures = []
    
    previous_future = None
    for i in range(10):
        future = my_task.submit(i, wait_for=allow_failure([previous_future]) if previous_future else [])
        task_futures.append(future)
        previous_future = future
    
    gathered_states = [future.wait() for future in task_futures]

    flow_failed = any(isinstance(state, Failed) for state in gathered_states)
    
    if flow_failed:
        logger.error("Flow failed due to one or more task failures")
        return Failed(message="Flow failed due to one or more task failures")
    else:
        <http://logger.info|logger.info>("Flow completed successfully")
        return Completed(message="Flow completed successfully")

if __name__ == '__main__':
    flow_run_state = my_intended_failed_flow()
    print(flow_run_state)  # To observe the final flow state
### Summary 1. Dynamic Task Handling: Tasks are dynamically generated and all their futures collected. 2. State Gathering: 
gathered_states
collects states of all task future objects using 
wait()
. 3. Flow State Decision: Flow's final state is determined based on whether any task failed. ### References - Prefect 3.0 What's New - Prefect 3.0 Managing States Embrace the chaos—run your tasks dynamically but still end up with a clear flow state! 🚀
a
@Marvin So why is it that I can run a simple hello world task on the dask worker without using prefect AND without packaging the code?
m
You can run a simple "Hello World" task directly on a Dask worker without packaging the code because Dask inherently handles task distribution and execution across a cluster in a flexible manner. Let's break down why this workflow works: ### Why It Works 1. Dynamic Task Execution: Dask dynamically schedules tasks using its distributed scheduler. You can submit tasks to any Dask cluster (local or remote) and they will be executed. 2. Python Functions: Tasks in Dask are just Python functions. You don’t need to package or deploy the code separately; you can simply define the function and submit it. 3. Serialization: Dask is capable of serializing tasks and sending them to workers without needing code packaging. This is particularly useful for running simple scripts or quick tests across the cluster. ### Example with Dask Here's a simple example to demonstrate running a task on a local Dask cluster:
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python
from dask.distributed import Client

def hello_world():
    return "Hello, World!"

if __name__ == "__main__":
    client = Client(n_workers=4)
    
    # Submit the task to Dask
    future = client.submit(hello_world)
    
    # Get the result
    result = future.result()
    print(result)  # Output: Hello, World!
### Why Prefect? Prefect adds extra layers like flow orchestration, state management, retries, and failover handling, which is why it requires a bit more setup compared to plain Dask. However, for raw task execution, Dask's scheduler and serialization are sufficient, and thus, packaging isn’t needed. ### Key Points - Execution Flexibility: Dask workers can execute serialized Python functions directly. - No Packaging Requirement: Simple functions can be run without the hassle of packaging and deploying code. - Prefect Enhancements: Prefect builds on Dask (or other backends) to provide more advanced orchestration features, thus needing a bit more overhead. This flexibility is why you can seamlessly run simple tasks with Dask while leveraging Prefect for more complex workflows and orchestration needs. 🚀
a
@Marvin So prefect cannot achieve the same result as above with the serialization of code?
m
Your question cuts deep. Can Prefect play ball like Dask when it comes to serialization? Spoiler: Yes—but with a bit more finesse. ### Prefect and Task Serialization Prefect is capable of serializing and distributing tasks without requiring code packaging explicitly. The additional features Prefect provides, such as state management and orchestration, might make it appear more complex, but at its core, it can achieve the same task distribution and execution as Dask. ### Example of Running a Simple Task with Prefect and Dask Here's a straightforward example to show how you can run a task on a Dask cluster using Prefect without needing to package the code. 1. Set Up a Dask Client: This connects Prefect to the Dask cluster. 2. Define Prefect Tasks and Flow: Prefect tasks can be run just like Dask tasks but with added orchestration features. ### Code Example:
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python
from prefect import flow, task
from prefect_dask import DaskTaskRunner
from dask.distributed import Client

# Start a Dask client
client = Client(n_workers=4)

@task
def hello_world():
    return "Hello, World!"

@flow(task_runner=DaskTaskRunner())
def my_flow():
    result = hello_world()
    return result

if __name__ == "__main__":
    flow_state = my_flow()
    print(flow_state.result())  # Output: Hello, World!
### Steps: 1. Dask Client: Initiate the Dask cluster (local in this case). 2. Prefect Task: Define the 
hello_world
task without needing any extra packaging. 3. Prefect Flow: Use the 
DaskTaskRunner
to run the flow tasks on Dask workers. 4. Run the Flow: Execute the flow and print the result. ### Summary: - Same Result: Prefect can indeed serialize and distribute tasks to Dask workers similarly. - Orchestration Benefits: You still get all Prefect's orchestration, retries, and state management features. ### References: - Prefect Dask Integration - Prefect Task Documentation So, yes—Prefect can keep up with Dask's serialization game, while also bringing its A-game with sophisticated orchestration features. 🚀
a
@Marvin Same example as above but now provide an address to the DaskTaskRunner which is what I am doing with no success
m
Ah, got it—you want to connect Prefect to a specific Dask cluster. Let’s make sure they find each other like star-crossed lovers. ### Example: Connecting Prefect to a Remote Dask Cluster Suppose you have a Dask scheduler running at a specific address (e.g., `tcp://192.168.1.100:8786`). Here’s how to affect the connection. ### Code Example:
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python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def hello_world():
    return "Hello, World!"

@flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
def my_flow():
    result = hello_world()
    return result

if __name__ == "__main__":
    flow_state = my_flow()
    print(flow_state.result())  # Output: Hello, World!
### Steps: 1. DaskTaskRunner Address: Provide the specific address of your Dask scheduler in the 
DaskTaskRunner
. 2. Define Prefect Flow: Utilize this 
DaskTaskRunner
to run the flow. 3. Run the Flow: Execute the flow and print the result. ### Troubleshooting Tips: 1. Network Reachability: Ensure that the machine running the Prefect flow can reach the Dask scheduler at the specified address. 2. Dask Scheduler Running: Double-check that the Dask scheduler is actually running at the given address. 3. Firewall Settings: Make sure that necessary ports (e.g., 8786) are open for communication if there's a firewall. ### Example with Additional Logging: Adding logging can help debug any connection issues.
Copy code
python
from prefect import flow, task, get_run_logger
from prefect_dask import DaskTaskRunner

@task
def hello_world():
    return "Hello, World!"

@flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
def my_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting flow with DaskTaskRunner")
    result = hello_world()
    <http://logger.info|logger.info>(f"Task result: {result}")
    return result

if __name__ == "__main__":
    flow_state = my_flow()
    print(flow_state.result())  # Output: Hello, World!
### Summary: - Target Dask Scheduler: Use the 
address
parameter in 
DaskTaskRunner
. - Debugging: Logging can help diagnose connection issues. ### References: - Prefect Dask Documentation - Dask Distributed Documentation Ensure that both Prefect and Dask play nice across networks, and you should be all set! 🚀
a
@Marvin Encountered exception during execution: OSError('source code not available')
m
Oops, seems like Dask can't find the source code! Let's fix that by making sure the functions are serializable and discoverable by Dask. ### Problem: 
OSError('source code not available')
typically occurs when Dask workers can't serialize the task function or access its source code. This can happen when using anonymous functions (like lambda) or running code in environments where the source isn't available. ### Solution: 1. Avoid Lambdas: Make sure to define all task functions using 
def
instead of lambda. 2. Ensure Code Accessibility: Ensure the code defining the task is accessible to the workers. ### Code Example Revisited: Define the task function using 
def
and ensure it's top-level for proper serialization.
Copy code
python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def hello_world():
    return "Hello, World!"

@flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
def my_flow():
    result = hello_world()
    return result

if __name__ == "__main__":
    flow_state = my_flow()
    print(flow_state.result())  # Output: Hello, World!
### Steps to Ensure Proper Serialization: 1. Avoid Lambdas: - Instead of 
task(lambda x: x)
, use a properly defined function. 2. Ensure Code Availability: - Make sure that the Dask workers have access to the Python code that defines the tasks. This typically means running the flow in an environment where the code is accessible, like in a shared code repository or distributed filesystem. ### Additional Debugging: - Check If Dask Workers Can Access the Code: Ensure that the file containing your task definitions is fully accessible to all Dask workers. Workers might not have access to the local context where the tasks are defined. - Using Docker: If using Docker, make sure the image includes the task definitions and dependencies. ### Logging Example: To further debug the issue, add more logging to understand where it might fail:
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python
from prefect import flow, task, get_run_logger
from prefect_dask import DaskTaskRunner

@task
def hello_world():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Executing hello_world task")
    return "Hello, World!"

@flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
def my_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting flow with DaskTaskRunner")
    result = hello_world()
    <http://logger.info|logger.info>(f"Task result: {result}")
    return result

if __name__ == "__main__":
    flow_state = my_flow()
    print(flow_state.result())  # Output: Hello, World!
### Summary: - Defining Tasks: Use top-level 
def
functions for task definitions. - Code Accessibility: Make sure Dask workers can access the task definitions. - Logging: Add as much logging as necessary to debug localization issues. ### References: - Dask Serialization - Prefect with Dask By making tasks serializable and accessible, you should avoid the 
source code not available
error and get Prefect and Dask working seamlessly together! 🚀
a
@Marvin Same issue
m
Alright, the source code not available issue persists. Let's dig deeper and make sure every part of the process is rock solid. ### Comprehensive Debugging Steps: 1. Task Definition: Ensure tasks are defined at the module level. 2. Network and Access: Verify the workers can access the task source. 3. Serialization: Confirm proper serialization is happening. 4. Address Verification: Ensure Dask scheduler address is correct and accessible. Below is a comprehensive approach incorporating all these aspects: ### Code Example with Extra Debugging and Checks: 1. Ensure Dask Scheduler is Accessible
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python
# Confirm the Dask scheduler is running and accessible
from dask.distributed import Client

client = Client("<tcp://192.168.1.100:8786>")
print(client)  # Should output scheduler information
2. Ensure Proper Task and Flow Definition Define tasks and flow at the module level to ensure they're serializable.
Copy code
python
# Ensure this is in a top-level script or module
from prefect import flow, task, get_run_logger
from prefect_dask import DaskTaskRunner

@task
def hello_world():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Executing hello_world task")
    return "Hello, World!"

@flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
def my_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting flow with DaskTaskRunner")
    result = hello_world()
    <http://logger.info|logger.info>(f"Task result: {result}")
    return result

if __name__ == "__main__":
    flow_state = my_flow()
    print(flow_state.result())  # Output: Hello, World!
3. Check Environment and Packages Make sure that the code environment is consistent between the local machine and Dask workers: - Same Python Environment: Use tools like 
pip freeze > requirements.txt
and ensure the same packages are installed on both environments. - Shared Filesystem: Consider using a shared storage where both the local machine and Dask workers can access the Python scripts. 4. Enable Worker Logging and Diagnostics Enable more detailed logging on Dask workers to catch serialization issues:
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python
from dask.distributed import Client, Scheduler

# Define a scheduler with enhanced logging
scheduler = Scheduler(dashboard_address=':8787')

# Start a Dask client with enhanced logging on worker side
client = Client(scheduler.address)

def hello_world():
    return "Hello, World!"

if __name__ == "__main__":
    import distributed
    distributed.worker.log_to_screen()
    print(client)

    # Now, run the Prefect flow
    from prefect import flow, task
    from prefect_dask import DaskTaskRunner

    @task
    def hello_world():
        return "Hello, World!"

    @flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
    def my_flow():
        result = hello_world()
        return result

    flow_state = my_flow()
    print(flow_state.result())  # Output: Hello, World!
### Summary: - Top-level Task Definitions: Ensure tasks are defined at the module level. - Scheduler Accessibility: Verify the scheduler is running and accessible. - Consistent Environment: Ensure environments match between local machine and Dask workers. - Enable Detailed Logging: Turn on worker logging for better diagnostics. ### Additional Resources: - Dask Serialization - Prefect Dask TaskRunner With these steps, you should be able to identify and resolve the 
OSError('source code not available')
issue and get Prefect tasks running on your Dask cluster. 🚀
a
@Marvin If i can execute a task on the dask cluster without prefect that means that everything should be fine from a networking perspective right?
m
Spot on! If Dask works standalone, then it's not the network hardware's fault. Let's focus on getting Prefect in line. ### Focus Areas: 1. Task Definition: Ensure Prefect tasks are defined in a way Dask can serialize and access them, just like in standalone Dask. 2. File Availability: Verify that the Python code is accessible to Dask workers, similar to your standalone task setup. 3. Diagnose Detailed Serialization Issues: Prefect might be doing something nuanced causing serialization problems. Let's target these areas with a simplified diagnostic approach. ### Step-by-Step: 1. Confirm Standalone Dask Functionality: Ensure that your Dask cluster can run tasks as expected. You've mentioned it works, so here's a minimal confirmation example:
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python
from dask.distributed import Client

client = Client("<tcp://192.168.1.100:8786>")

def hello_world():
    return "Hello, World!"

future = client.submit(hello_world)
print(future.result())  # Output: Hello, World!
2. Check Prefect Task Accessibility: Ensure your Prefect task is defined at the module level, and accessible, similar to what you did with a standalone Dask task.
Copy code
python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def hello_world():
    return "Hello, World!"

@flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
def my_flow():
    result = hello_world()
    return result

if __name__ == "__main__":
    flow_state = my_flow()
    print(flow_state.result())  # Should output: Hello, World!
3. Add Detailed Logging: Enable detailed logging to troubleshoot the Prefect-Dask communication.
Copy code
python
from prefect import flow, task, get_run_logger
from prefect_dask import DaskTaskRunner

@task
def hello_world():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Executing hello_world task")
    return "Hello, World!"

@flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
def my_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting flow with DaskTaskRunner")
    result = hello_world()
    <http://logger.info|logger.info>(f"Task result: {result}")
    return result

if __name__ == "__main__":
    log_level = "debug"
    import dask
    import logging

    dask.config.set({'logging': {
        'distributed.worker': log_level,
        'distributed.client': log_level}})
    logging.basicConfig(level=logging.DEBUG)

    flow_state = my_flow()
    print(flow_state.result())  # Output: Hello, World!
### Checkpoints: 1. Module-Level Tasks: All task functions must be defined at the module level. 2. Run Logger: Use Prefect’s logger to output task execution status. 3. Logging Configuration: Set Dask and Python logging levels to DEBUG for detailed insights. ### Additional Help: - Dask often requires the 
task
and its dependencies to be available in the same environment the worker is running. Ensure your Prefect script is either installed as a package/module accessible on the worker or is otherwise available on workers' file system. If the 
OSError
persists, ensure: - Python Path Consistency: Dask worker's environment must match that of your local/testing environment. - Prefect Agent Environment: The environment where the Prefect agent runs should also match the workers'. With this focused approach, we confirm the environment and serialization integrity while leveraging Prefect's orchestration power without tripping over serialization. 🚀
a
@Marvin In the above example since the flow doesn't call submit it works fine locally. However when I call submit on it, i got the source code error
m
Ah, the plot thickens! The issue crops up only when using 
submit
. It's like Dask can run when local but Prefect trips when orchestrated. Let’s fix that. ### Explanation: When you call 
submit
, Prefect tries to serialize the function and send it to the Dask workers. If the function isn't properly serializable or the source code isn't accessible to the workers, you get the 
OSError('source code not available')
. ### Step-by-Step Solution: 1. Ensure Tasks are Top-Level: Definitions should be at a top level to allow serialization. 2. Use 
file
Attribute: Make sure Prefect/Dask can access the source code. 3. Check Environment Consistency: Ensure the environment where tasks are submitted matches the environment where Dask workers execute. ### Code Example: Here’s how to define a flow where tasks can be submitted correctly and logging is used for diagnostics. 1. Task and Flow Definitions:
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python
from prefect import flow, task, get_run_logger
from prefect_dask import DaskTaskRunner
import logging

@task
def hello_world():
    logger = get_run_logger()
    logger.debug("Executing hello_world task")
    return "Hello, World!"

@flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
def my_flow():
    logger = get_run_logger()
    logger.debug("Starting flow with DaskTaskRunner")
    future = hello_world.submit()
    result = future.result()
    logger.debug(f"Task result: {result}")
    return result

if __name__ == "__main__":
    # Configure Prefect and Dask logging
    logging.basicConfig(level=logging.DEBUG)
    import dask
    dask.config.set({'logging': {
        'distributed.worker': 'debug',
        'distributed.client': 'debug',
        'distributed.scheduler': 'debug'
    }})
    
    flow_state = my_flow()
    print(flow_state)  # To observe the final flow state
### Debugging Help: 1. Ensure Task Serialization: Use only defined functions, not lambdas or locally scoped functions. 2. Logging: Utilize logging to capture any serialization details and errors. 3. Environment Setup: Ensure that: - The Python environment on the Dask workers can access the script/module. - Any dependencies are installed in the Dask worker environment. ### Improved Diagnostics: Enable detailed logging and check logs for serialization details and any potential missing modules or dependencies.
Copy code
python
from prefect import flow, task, get_run_logger
from prefect_dask import DaskTaskRunner
import sys
import os
import logging

# Set up environment logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)

@task(log_prints=True)
def hello_world():
    logger = get_run_logger()
    logger.debug("Executing hello_world task")
    return "Hello, World!"

@flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
def my_flow():
    logger = get_run_logger()
    logger.debug("Starting flow with DaskTaskRunner")
    try:
        future = hello_world.submit()
        result = future.result()
        logger.debug(f"Task result: {result}")
        return result
    except Exception as e:
        logger.error(f"Exception during task execution: {e}")
        raise

if __name__ == "__main__":
    # Configure Dask Logging
    import dask
    dask.config.set({'logging': {
        'distributed.worker': 'debug',
        'distributed.client': 'debug',
        'distributed.scheduler': 'debug'
    }})

    # Ensure `PWD` is in `sys.path` for Dask workers
    if os.getcwd() not in sys.path:
        sys.path.append(os.getcwd())

    flow_state = my_flow()
    print(flow_state)  # Observe final flow state
### Ensure Accessibility: - Make File Accessible: Place the script in the worker's environment or shared directory accessible to both local and remote execution environments. - Check Python Path: Ensure the script's directory is in 
sys.path
for worker environments. ### Summary: - Task Serialization: Define tasks at the module level.
- Logging: Capture detailed logs for diagnosing serialization errors. - Environment Accessibility: Worker environment must access the script/module. ### References: - Prefect Task Packaging - Dask Serialization Documentation Follow these practices to align Prefect's orchestration with Dask's task execution seamlessly across environments. 🚀
a
@Marvin I quite literally copied the file into the dask workers docker image and prefect still gives a source code not found error
m
Understood, you've copied the file straight into the Dask worker's Docker image and the error persists. Let's go all-out to ensure Prefect and Dask play nicely together without their serial tantrums. ### What We Need to Confirm: 1. Module-Level Function Definitions: Functions should be defined at the top level. 2. Worker Accessibility: Ensure that Dask workers can import and execute the function. 3. Environment Consistency: Matching environments across the local machine and Docker image for Dask workers. 4. Logging and Debugging: Confirm via logging paths and serializations. ### Step-by-Step Fix: 1. Top-Level Task Definitions: Ensure tasks are defined at the top module level. 2. Ensure Importability by Workers: - The Python module containing the tasks should be importable by Dask workers. 3. Confirm Docker Image Paths: Verify paths and module importation within Docker containers. ### Example Code Setup: 1. Verify and Prepare Docker Environment: - Ensure the Prefect script is saved in a location accessible from within the Docker containers running the Dask workers. 2. Define Tasks at Module Level: Ensure tasks are properly defined at the module top-level.
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python
# my_tasks.py
from prefect import task

@task
def hello_world():
    return "Hello, World!"

# Make sure this module is accessible in Docker image
3. Add Flow and Execution: Refer to the module from within your main flow script.
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python
# my_flow.py
from prefect import flow
from prefect_dask import DaskTaskRunner
from my_tasks import hello_world  # Import task from the module

@flow(task_runner=DaskTaskRunner(address="<tcp://192.168.1.100:8786>"))
def my_flow():
    result = hello_world.submit().result()
    return result

if __name__ == "__main__":
    flow_state = my_flow()
    print(flow_state)
4. Docker Configuration: Ensure the Python path in Docker container includes the directory of 
my_tasks.py
. ### Dockerfile Example: Example Dockerfile for Dask workers.
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Dockerfile
FROM daskdev/dask:latest

# Copy your scripts into the Docker image
COPY my_tasks.py /workspace/my_tasks.py
COPY my_flow.py /workspace/my_flow.py

# Set the working directory
WORKDIR /workspace

# Ensure necessary packages are installed
RUN pip install prefect prefect-dask

# Add the workspace to Python path
ENV PYTHONPATH /workspace

# Start the Dask worker
CMD ["dask-worker", "<tcp://scheduler-address:8786>"]
### Build and Run Docker Image: 1. Build Docker Image:
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sh
docker build -t dask-worker-custom .
2. Run Dask Scheduler and Workers:
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sh
# Start the Dask scheduler
docker run -d --name dask-scheduler -p 8786:8786 -p 8787:8787 daskdev/dask:latest dask-scheduler

# Start Dask workers (adjust address accordingly)
docker run -d --name dask-worker1 --link dask-scheduler dask-worker-custom
docker run -d --name dask-worker2 --link dask-scheduler dask-worker-custom
3. Run Prefect Flow: From your local machine, ensure the scheduler address is correctly pointing to your Docker network.
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sh
python my_flow.py
### Debugging: 1. Check Container Logs: Look for any issues within Docker container logs.
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sh
docker logs dask-worker1
2. Ensure Module Importability: Verify path configurations within the container.
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sh
# Execute within the worker container
docker exec -it dask-worker1 /bin/bash

# Start Python REPL and try importing
python
>>> from my_tasks import hello_world
3. Check Python Path in Container: Ensure the paths align.
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python
import sys
print(sys.path)
### Key Points: - Top-Level Definitions: Ensure tasks are defined at the module level. - Accessible Module: Make sure 
my_tasks.py
is importable within the worker environment. - Environment Consistency: Ensure Docker containers have the correct Python path configured. By following these steps, Prefect and Dask should now work in harmony, ensuring task serialization and seamless distributed execution. 🚀
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