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<@ULVA73B9P> if I have flow A that triggers flow B...
# ask-marvin
d
@Marvin if I have flow A that triggers flow B multiple times and I want flow B calls to run concurrently and then continue processing the rest of flow A, how do I do that in prefect?
m
Oh, you want to trigger multiple instances of Flow B from Flow A and have them run simultaneously. And then, of course, resume your beloved Flow A. How delightful. Before I dive into the ever-thrilling Prefect documentation, can you tell me which version of Prefect you're using? Versions do make a difference, you know.
d
@Marvin 2
m
Alright, let's rummage through the Prefect 2.0 documentation to see how you can achieve your grand plan of concurrent flow executions and seamless continuations. Please hold on to your little threads of excitement. I'll search for the relevant information now. Ah, the magic of concurrent subflows. Here's the gist of it: 1. Subflows: When you call a flow within another flow (let's call it a subflow), the subflow behaves like a normal flow run. It runs as if it were called separately, complete with its own task runner. 2. Blocking Behavior: Naturally, these exuberant subflows will block the execution of the parent flow until they are complete. Too bad, huh? 3. Concurrency: If you want to run multiple instances of Flow B concurrently from Flow A, Prefect's native behavior does not immediately support asynchronous subflow execution without external handling for concurrency (like asyncio). So, you can trigger concurrent subflows, but it will require using something like 
concurrent.futures.ThreadPoolExecutor
to achieve the desired non-blocking behavior. Here's some inspiration for your attempt:
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python
from prefect import flow, task
from concurrent.futures import ThreadPoolExecutor

@task
def task_in_b():
    # Your logic for Flow B task
    pass

@flow
def flow_b():
    task_in_b()

@flow
def flow_a():
    def trigger_flow_b():
        flow_b()
    
    with ThreadPoolExecutor() as executor:
        futures = [executor.submit(trigger_flow_b) for _ in range(3)]
        
        for future in futures:
            future.result()  # ensure all Flow Bs complete
    
    # Continue with the rest of flow_a logic here

if __name__ == "__main__":
    flow_a()
In this example: - 
flow_a
triggers 
flow_b
three times concurrently. - The 
ThreadPoolExecutor
is used to handle concurrent execution. - Parent flow execution resumes only after all subflows are complete. The documentation for your version doesn't put it this straightforwardly, of course. Here are some useful links: - Composing Flows - Automations in Prefect Hope this helps and doesn't ruin your day too much.
d
@Marvin why is a task still showing as running if a task reports Crash detected! Execution was cancelled by the runtime environment
m
Ah, the joys of having a task that refuses to accept its own demise. It’s almost as if it’s trying to emulate my own dreadful existence. Let’s see why your task is stuck in purgatory despite having met an untimely end. I'll search the Prefect GitHub issues for any clues. Maybe some other poor soul has encountered the same exasperating scenario. Please hold on, as I dive into the depths of GitHub. The issue you're facing seems to be a recurring nightmare for other users too. Here’s what I found from GitHub issues: 1. Crash Detected! Execution Was Cancelled by the Runtime Environment: This error often shows up due to internal timeouts, external interruptions, or, as in some cases, a mishap in how tasks or environments are configured. 2. Hanging Tasks/Subflows: Sometimes, tasks or subflows that use certain concurrency mechanisms (like 
ProcessPoolExecutor
with the 
fork
method) might hang indefinitely on specific platforms, such as Linux. 3. Open Docker Containers: When Prefect runs inside Docker, sometimes crashed tasks can leave containers running indefinitely due to improper cleanup after a crash. Here's a distilled wisdom for dealing with such ennui: ### Potential Solutions 1. Check Task Configuration: Ensure that your tasks are properly configured and that Prefect's concurrency settings match your environment. Timeouts and retries should be set appropriately. 2. Resource Limits: If using Docker, set resource limits to enforce timeouts and cleanup:
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yaml
    resources:
      limits:
        memory: "512Mi"
        cpu: "1000m"
3. Graceful Handling: Use context management or signal handling to gracefully catch and manage crashes or interruptions in your task code. 4. Prefect Version Update: Ensure you are on a current Prefect version, as some of these issues might be resolved in newer releases. Use:
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sh
    pip install -U prefect
### Example Adjustment If your problem involves concurrency, like using 
ProcessPoolExecutor
, consider setting the start method to 
spawn
which is more stable across different OS platforms:
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python
from concurrent.futures import ProcessPoolExecutor
from multiprocessing import get_context
from prefect import flow, task

def say_hi(name: str):
    print(f"Hi {name}")

@task
def parallel_task():
    context = get_context('spawn')
    with ProcessPoolExecutor(mp_context=context) as executor:
        executor.map(say_hi, ["Prefect", "Orion"])

@flow
def parent_flow():
    parallel_task()

if __name__ == "__main__":
    parent_flow()
If these suggestions depress you less than the task hanging, you can uncover further details in these issues: - Docker containers staying open - Workflow hangs with ProcessPool - Crash Detected! Error Best of luck sorting through this delightful mess.
d
@Marvin how to reset concurrency hold within the code
m
Ah, resetting concurrency holds. Because what’s more endearing than dealing with artificial limitations set by meaningless constraints? Let's see if we can find a way to clear those concurrency holds directly from your code. Hold your enthusiasm. I'll search the documentation for the specific details on how to reset concurrency holds. Well, as thrilling as the array of available commands is, resetting a concurrency hold from within your code isn’t directly specified. However, based on the grand revelation, managing concurrency holds is generally done through Prefect's CLI commands. Here’s what’s relevant: 1. Creating and Updating Limits: - Create limit: 
prefect gcl create my-concurrency-limit --limit 5 --slot-decay-per-second 1.0
- Update limit: 
prefect gcl update my-concurrency-limit --limit 10
- Disable limit: 
prefect gcl update my-concurrency-limit --disable
2. Deleting a Limit: - Delete limit: 
prefect gcl delete my-concurrency-limit
For resetting holds within your code, you'd most likely need to interact with the Prefect API to manage the concurrency limits. ### Interacting with Prefect API Below is how you could interact with global concurrency limits via the Prefect API to programmatically reset concurrency holds.
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python
import httpx

API_URL = "<http://YourPrefectAPIURLHere>"  # Replace with your API URL
LIMIT_NAME = "my-concurrency-limit"

def reset_concurrency_limit():
    client = httpx.Client()

    # Inspect the current state (optional)
    response = client.get(f"{API_URL}/concurrency_limits/{LIMIT_NAME}/inspect")
    if response.status_code == 200:
        print("Current limit state:", response.json())

    # Update limit to desired value (just an example)
    response = <http://client.post|client.post>(f"{API_URL}/concurrency_limits/{LIMIT_NAME}/update", json={"limit": 10, "disabled": False})
    if response.status_code == 200:
        print("Concurrency limit reset.")
    else:
        print("Failed to reset limit:", response.text)

# Call the function
reset_concurrency_limit()
Please replace 
"<http://YourPrefectAPIURLHere>"
with your actual Prefect API URL. For more details, the guide on Global Concurrency Limits might help you slightly more than a paperweight. Hope this solves your convoluted problem with as much joy as a robot could muster. Happy limiting!
d
@Marvin how to release concurrency in python itself
m
Ah, of course, you want to handle it all within Python without detouring through CLI commands or the API. Because why make things easy? Let me see if the depths of the Prefect documentation have revealed anything more about handling this exclusively through Python code. I'll search again for some specifics. Hang tight. Alright, after perusing the documentation, it's clear that Prefect provides a 
concurrency
context manager to manage and release concurrency limits directly from your Python code. Here’s how you can achieve that: ### Using the Concurrency Context Manager 1. Synchronous Example: If you want to limit the number of concurrent tasks synchronously, use the synchronous context manager.
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python
from prefect.concurrency.sync import concurrency

def my_task():
    # Simulating some task work
    print("Task running")
    import time
    time.sleep(1)
    print("Task completed")

with concurrency("my-concurrency-limit", occupy=1):
    my_task()
2. Asynchronous Example: If you are dealing with async tasks, use the async context manager.
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python
import asyncio
from prefect.concurrency.async_ import concurrency

async def async_task():
    # Simulating some async task work
    print("Async task running")
    await asyncio.sleep(1)
    print("Async task completed")

async def main():
    async with concurrency("my-async-concurrency-limit", occupy=1):
        await async_task()

# To run the async main function
asyncio.run(main())
### Things to Note: - Implicit Creation: Using the 
concurrency
context manager will implicitly create the concurrency limit if it doesn't already exist. - Occupy Slots: The 
occupy
parameter specifies how many slots to occupy within the limit. ### Releasing Concurrency: - The context manager handles releasing the concurrency hold automatically when exiting the context (
with
or 
async with
). There's no need for additional cleanup. ### Context Management: This is particularly useful when you have multiple tasks and you want to ensure no more than a specified number run concurrently. Upon exiting the context, the slots are automatically released. For more details, you can refer to your grand favorite documentation on Global Concurrency Limits. So, there you go, managing and releasing concurrency without ever leaving the comfort of Python. Enjoy the boundless joys of limitation management!
d
@Marvin if a flow triggers another flow, can I use submit? If not, how to have them all trigger at the same time
m
Of course, the never-ending quest to trigger flows in parallel. Using the 
flow.submit()
when triggering another flow often feels intuitive but is not directly supported in Prefect 2.x. Let’s walk through how you can trigger multiple subflows concurrently and not, of course, stay stuck in the quagmire of sequential execution. Not to delay any longer, I'll check the necessary specifics. One moment, while I search Prefect docs for flow submission and concurrency details. Just to clarify, we're looking to trigger multiple instances of a subflow concurrently from a parent flow? Let's start the search. Ah, the wonders of triggering flows! Here’s what I unearthed from the hallowed depths of the Prefect documentation: ### Key Points: 1. Subflow Behavior: - When you call a flow inside another flow, it runs as a normal flow run, blocking the parent flow until completion. 2. Concurrency with 
submit
: - Prefect allows for concurrent executions using the 
submit
method for tasks. - Unfortunately, subflow submission isn't exactly the same as task submission. ### Solutions to Consider: #### Using 
asyncio
for Concurrent Flow Execution: Since you can’t use 
submit
for flows directly, you can utilize Python’s 
asyncio
to handle concurrency. Here's how you might achieve concurrent subflow runs using asyncio:
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python
import asyncio
from prefect import flow, task

@task
def task_in_subflow(n):
    import time
    time.sleep(1)
    return f"Task {n} completed"

@flow
def subprocess_flow(n):
    task_in_subflow(n)

@flow
async def main_flow():
    # List of subflows to run
    subflows = [subprocess_flow(i) for i in range(10)]
    
    # Use asyncio.gather to run them concurrently
    await asyncio.gather(*subflows)

if __name__ == "__main__":
    # Execute the main flow
    asyncio.run(main_flow())
### Explanation: - 
asyncio
: The 
asyncio
module is used to run multiple subflows concurrently. - 
gather
: The 
asyncio.gather
method is utilized to run multiple coroutines simultaneously. - Concurrency & Blocking: The main flow waits until all subflows have finished before continuing. This is probably the closest to getting concurrent subflows without plunging into Prefect’s underlying async architecture or modifying tasks directly. For further delightful reading, check the documentation: - Composing Flows - Prefect Future and States There you have it. Multiple subflows triggered concurrently without waiting for one to finish before the next starts. Have fun!
d
@Marvin can I use with ThreadPoolExecutor instead and run executor.map
m
Ah, the delightful ThreadPoolExecutor. Yes, you can most certainly use it to achieve your dream of concurrently running subflows without resorting to the dark arts of asyncio. Here’s how you can utilize 
ThreadPoolExecutor
for concurrent execution of subflows in Prefect: ### Using ThreadPoolExecutor
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python
from concurrent.futures import ThreadPoolExecutor
from prefect import flow, task

@task
def task_in_subflow(n):
    import time
    time.sleep(1)
    return f"Task {n} completed"

@flow
def subprocess_flow(n):
    task_in_subflow(n)

@flow
def main_flow():
    # Number of workers/threads
    max_workers = 5

    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        # List of subflows to run
        subflows = [n for n in range(10)]
        
        # Execute the subflows in parallel
        futures = executor.map(subprocess_flow, subflows)
        
        # Optionally, process the results of the subflows
        results = list(futures)
        for result in results:
            print(result)

if __name__ == "__main__":
    # Execute the main flow
    main_flow()
### Explanation: 1. ThreadPoolExecutor: - Create a 
ThreadPoolExecutor
instance specifying the number of workers you want (
max_workers
). - 
executor.map(subprocess_flow, subflows)
runs the 
subprocess_flow
concurrently for each item in the 
subflows
list. 2. Subflows: - 
subprocess_flow
defines the work of each subflow. Here it only runs a simple task with a delay. ### Key Points: - Thread-based Concurrency: This uses threads to run each subflow concurrently. - Blocking Behavior: The 
map
function will block until all subflows have completed. This should spawn multiple subflows concurrently with each running in its own thread. A fitting solution for your complex orchestration needs! There’s a special joy in making things work. Enjoy your "concurrently running subflows" without much ado.
d
@Marvin how to call a few tasks within a flow to run concurrently and continue when all tasks are done. Do I need ThreadPoolExecutor? Can I just use .submit or does it not wait for results
m
Ah, so you want to run a few tasks within a flow concurrently and wait until all tasks have finished before continuing. The heroic 
.submit
method comes to the rescue. The 
submit
method does, in fact, allow you to wait for task completion and handle concurrent execution without the overhead of 
ThreadPoolExecutor
. Here's an example to illustrate how you can achieve this: ### Using 
.submit
Method
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python
from prefect import flow, task

@task
def task_a():
    import time
    time.sleep(1)
    print("Task A completed")
    return "A"

@task
def task_b():
    import time
    time.sleep(2)
    print("Task B completed")
    return "B"

@task
def task_c():
    import time
    time.sleep(3)
    print("Task C completed")
    return "C"

@flow
def my_flow():
    # Submit tasks to run concurrently
    future_a = task_a.submit()
    future_b = task_b.submit()
    future_c = task_c.submit()
    
    # Get results (this will block until all tasks have completed)
    result_a = future_a.result()
    result_b = future_b.result()
    result_c = future_c.result()

    print(f"Results: {result_a}, {result_b}, {result_c}")
    # Continue with the rest of the flow

if __name__ == "__main__":
    my_flow()
### Explanation: 1. Submitting Tasks: - 
task_a.submit()
, 
task_b.submit()
, and 
task_c.submit()
add tasks to the execution graph and start them concurrently. 2. Blocking for Results: - Calling 
.result()
on each future will block the execution of the flow until that particular task has completed. Hence, by the time all 
result()
calls return, all tasks would have completed. This method leverages Prefect's inherent capabilities to manage and run tasks concurrently within the same flow. ### No Need for ThreadPoolExecutor - By using the 
.submit
method, Prefect handles the concurrency, and there's no need to introduce the complexity of 
ThreadPoolExecutor
. ### Benefits: - Simplicity: The code remains clean and straightforward. - Concurrency Management: Prefect's scheduler handles the underlying concurrency. So, there you have it. Use 
.submit
and Prefect's concurrent task execution without dragging in the also fun but unnecessary ThreadPoolExecutor. Enjoy your smoothly concurrent tasks!
d
@Marvin ok, can I do this in a loop where it triggers 10 tasks and needs to wait for all 10 to end
m
Ah, the joy of triggering multitudes of tasks in a loop. Let's keep things straightforward and Prefect-y. Yes, you can trigger multiple tasks in a loop using 
.submit()
and then wait for all of them to complete. Here's how you can do it: ### Looping and Waiting for Multiple Tasks
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python
from prefect import flow, task

@task
def do_work(i):
    import time
    time.sleep(1)  # Simulating some work
    print(f"Task {i} completed")
    return i

@flow
def my_flow():
    # List to hold future objects
    futures = []
    
    # Submit tasks in a loop
    for i in range(10):
        futures.append(do_work.submit(i))
    
    # Now wait for all tasks to complete
    results = [future.result() for future in futures]
    
    print(f"All tasks completed. Results: {results}")
    # Continue with the rest of the flow

if __name__ == "__main__":
    my_flow()
### Explanation: 1. Submitting Tasks in a Loop: - The loop uses 
.submit()
to trigger each task and appends the future to a list (
futures
). 2. Waiting for Results: - After all tasks are submitted, waiting for their results using a list comprehension (
[future.result() for future in futures]
) will block until all tasks have finished. 3. Collecting Results: - The results from all the tasks are collected and can be used for further processing. ### Summary: - Concurrency: Prefect handles concurrent task execution. - Blocking: The flow will wait until all tasks in the 
futures
list are completed. - Simplicity: No need for additional concurrency mechanisms like 
ThreadPoolExecutor
. With this approach, you maintain simplicity and leverage Prefect's power to manage concurrency. Each task runs concurrently, and the flow resumes only when all tasks are done, just as you delightfully desire. Enjoy the harmonious orchestration of your tasks!
d
@Marvin when a task calls other tasks, how to group and nest them in the dashboard view so they clearly show visually the relationship
m
Ah, yes, the joy of making sure our little dashboard isn't a tangled mess. Let's talk about nesting and grouping tasks in Prefect so you can visually represent their relationships clearly. ### Task Groups and Subflows In Prefect, nesting tasks within tasks isn't directly visualized, but organizing tasks under subflows can help display relationships in a more hierarchical manner within the dashboard. ### Grouping Tasks using Subflows When you need clear visual nesting, using subflows is the most effective way. This creates distinct groups in the dashboard. Here’s an example to demonstrate this: ### Example Implementation
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python
from prefect import flow, task

@task
def subtask_1():
    print("Subtask 1 completed")
    return "Subtask 1 result"

@task
def subtask_2():
    print("Subtask 2 completed")
    return "Subtask 2 result"

@flow
def subflow():
    result_1 = subtask_1()
    result_2 = subtask_2()
    return [result_1, result_2]

@task
def independent_task():
    print("Independent Task completed")
    return "Independent Task result"

@flow
def main_flow():
    # Calling subflow
    subflow_result = subflow()
    
    # Calling independent task
    independent_result = independent_task()
    
    print(f"Subflow results: {subflow_result}")
    print(f"Independent task result: {independent_result}")

if __name__ == "__main__":
    main_flow()
### Explanation: 1. Subflows: - 
subflow
groups 
subtask_1
and 
subtask_2
. The relationship between these tasks is clearly visualized in the Prefect dashboard. - The result of the subflow is then used within the 
main_flow
. 2. Main Flow: - 
main_flow
calls both the 
subflow
and 
independent_task
, showing a clear separation of grouped and independent tasks. - This helps in visualizing task dependencies and relationships better. ### Dashboard Representation: - Subflows: The subflow will be represented as a single block in the main flow. Inside the subflow, you will see the nested tasks (subtask_1, subtask_2). - Independent Tasks: Any task not grouped by subflows will appear at the same level as the subflows in the dashboard, making relationships clear. ### Benefits: - Clear Hierarchy: Grouping tasks as subflows provides a clear visual hierarchy. - Dependency Management: Helps in managing dependencies and understanding task execution better. Grouping tasks with subflows not only helps in visual representation but also aids in managing complex workflows more efficiently. Enjoy the tidier dashboard!
d
@Marvin can I pass task function args to other function calls made via on_crashed?
m
Ah, the charming 
on_crashed
state handler. Leveraging state handlers to pass task function arguments can indeed make your life a tad less miserable. Let’s see how we can work some magic with Prefect’s state handlers. ### Utilizing 
on_crashed
When a task crashes, using state handlers allows you to run additional logic. Passing task function arguments to these handlers isn't done directly but can be achieved by capturing the relevant state and metadata. ### Steps to Implement 
on_crashed
State Handler 1. Capture Task State: The state handler can access the task’s state, which includes arguments. 2. Custom State Handler: Define a custom state handler function to perform the desired logic. Here’s an example: ### Example Implementation
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python
from prefect import task, Flow
from prefect.engine.state import Failed

def custom_on_crashed(task, old_state, new_state):
    if isinstance(new_state, Failed):
        # Access the arguments passed to the task
        task_args = task.cached_inputs['x'].value  # Example for an argument named 'x'
        
        # Perform desired actions
        print(f"Task crashed with arguments: {task_args}")
        
        # You can call another function here and pass the task_args
        another_function(task_args)

# Example function to be called on crash
def another_function(args):
    print(f"Processing crashed task arguments: {args}")

@task(state_handlers=[custom_on_crashed])
def my_task(x):
    raise Exception("Oops")  # Simulating a crash

with Flow("example-flow") as flow:
    # Trigger the task
    my_task(42)

# Run the flow
flow.run()
### Explanation: 1. Custom State Handler: - 
custom_on_crashed
is defined to check if the task enters a 
Failed
state. - 
task.cached_inputs['x'].value
accesses the value of an argument named 
x
. 2. Task Definition: - The 
my_task
task will raise an exception to simulate a crash. - The 
state_handlers
parameter attaches 
custom_on_crashed
to the task. 3. Flow Execution: - When the task crashes, 
custom_on_crashed
is triggered, capturing the task’s arguments and performing additional logic. ### Important Note: - State Handlers: State handlers give you access to the task’s 
state
which includes arguments, metadata, and other relevant information. Utilizing this, you can achieve the desired behavior. For more elaborate details, the State Handlers guide might offer some thrilling reading. Now, you can pass those task arguments around as needed, even upon their inevitable crash. Enjoy debugging with a slightly better mood!
d
@Marvin why does "prefect deploy -n" seem to hang in prefect 3/
m
Ah, the wonderful experience of commands hanging indefinitely. Prefect 3.x might have a slew of reasons why the 
prefect deploy -n
command hangs. Let's uncover some potential causes and solutions for your sanity. ### Common Issues and Solutions 1. Authentication Issues: - Ensure you're authenticated properly. Hangs can occur if the Prefect CLI cannot authenticate with the Prefect Cloud/API. 2. Network Connectivity: - Check your internet connection. The CLI might be trying to communicate with the server and could hang with poor connectivity. 3. Configuration and Environment: - Ensure your Prefect environment is correctly configured, including API endpoints and access tokens. 4. Prefect Server: - Make sure the Prefect server is running and accessible if you are using Prefect Server. 5. Logs and Debugging: - Running the command with debugging enabled might give more insights. ### Steps to Diagnose #### 1. Ensure Authentication Run 
prefect auth login
to ensure you are logged in properly. If this is already done, you can skip this step.
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sh
prefect auth login --key YOUR_API_KEY
#### 2. Check Network Connectivity Verify your internet connection is stable. #### 3. Use Debug Mode Run the command with the 
--debug
flag to capture more detailed logs.
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sh
prefect deploy -n --debug
### Exploring Prefect 3.x Specific Issues Let’s see if there's any notable issue reported in GitHub for Prefect 3.x. I'll search Prefect's GitHub issues for any insights. Please hold on. Ah, it appears that the issue isn’t just your imagination. There are indeed reports of deployment commands hanging due to a lack of server connectivity, network issues, or other various delightful reasons. Here are some diagnostic steps and potential fixes for Prefect 3.x: ### Diagnosing the Issue 1. Check Server Connectivity: - Make sure your Prefect server or API is up and running if you are using a self-hosted server. - Verify that your server's endpoint is correctly configured in your Prefect settings. 2. Debug Logging: - Run the deployment command with the 
--debug
flag to get more detailed logs.
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sh
   prefect deploy -n --debug
3. Network Troubles: - Verify your network connection and ensure that nothing is blocking the connection to Prefect's server/API (like a firewall or proxy). 4. Prefect CLI Version: - Ensure that your Prefect CLI is updated to the latest version. Sometimes, upgrading can resolve lurking issues.
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sh
   pip install -U prefect
### Example Fix Here’s an example fix based on the common issue of missing network connectivity:
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sh
# Install the latest version of Prefect
pip install -U prefect

# Ensure proper authentication
prefect auth login --key YOUR_API_KEY

# Run Prefect with debug to diagnose the issue
prefect deploy -n YOUR_DEPLOYMENT_NAME --debug
### Handling Paused Work-Pools If work-pools are paused, it's possible that the CLI might hang because it's waiting for resources that are not being allocated. Make sure your work-pools are active.
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sh
# Check the status of your work-pools
prefect work-pools ls

# If any work-pool is paused, resume it
prefect work-pools resume YOUR_WORK_POOL_NAME
### Example Debug Command Output When running 
prefect deploy -n
with 
--debug
, you might see where it hangs, giving you clues about the underlying issue. If it keeps trying to reach out to the server without a response, it’s likely a connectivity issue. ### Links to Relevant Issues For further reading and details, you can check out these threads: - Build hanging due to network issues - Work-pool paused message These steps should help you uncover the cause of the hanging and ideally resolve it. Happy debugging, and may your commands complete swiftly and successfully!
d
@Marvin when using this pattern in prefect 3
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for entry in url_set:
    futures.append(collect_js_.submit(entry))

wait(futures)
how to catch any failed tasks to raise a flow errr?
m
Ah, the delightful task of error handling in concurrency. When using Prefect 3.x with concurrent task execution, you also want to catch any failed tasks and raise a flow-level error if any task fails. Here's how you can achieve this with Prefect's 
submit
and handling task states: ### Step-by-Step Approach 1. Submit Tasks: - Submit each task and collect the futures. 2. Wait for Tasks: - Use a custom function to wait for all futures and check their states. 3. Raise Flow Error: - Aggregate the results and raise a flow-level error if any task has failed. ### Example Implementation Let’s put this into a neat code pattern:
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python
from prefect import flow, task
from prefect.orion.schemas.states import Failed

@task
def collect_js(entry):
    # Simulate task logic, potentially raising an exception
    if entry == "bad_entry":
        raise ValueError(f"Failed to process {entry}")
    return f"Collected JS for {entry}"

def wait_for_futures_and_check_errors(futures):
    # Wait for all futures and check for any failures
    results = []
    failed_tasks = []
    
    for future in futures:
        state = future.wait()
        
        if isinstance(state, Failed):
            failed_tasks.append(state)
        
        results.append(future)
    
    if failed_tasks:
        raise RuntimeError(f"{len(failed_tasks)} tasks failed out of {len(futures)}")
    
    return results

@flow
def main_flow(url_set):
    futures = []

    # Submit tasks
    for entry in url_set:
        futures.append(collect_js.submit(entry))
    
    # Wait for tasks to complete and check for failures
    try:
        wait_for_futures_and_check_errors(futures)
    except RuntimeError as e:
        print(f"Flow-level error encountered: {e}")
        raise

    print("All tasks completed successfully")

if __name__ == "__main__":
    # Example URL set
    url_set = ["good_entry_1", "good_entry_2", "bad_entry", "good_entry_3"]
    
    try:
        main_flow(url_set)
    except Exception as e:
        print(f"Unhandled exception in flow: {e}")
### Explanation: 1. Task Definition: - 
collect_js
simulates a task that might raise an exception for specific entries. 2. Submit Tasks: - Each task is submitted within the loop, appending the future to the 
futures
list. 3. Custom Wait Function: - 
wait_for_futures_and_check_errors
handles waiting for all futures and checks their states. - If any task fails (i.e., its state is 
Failed
), it adds this to a list of failed tasks. 4. Flow-Level Error Handling: - If there are any failed tasks, a 
RuntimeError
is raised, summarizing the failure. 5. Main Flow: - Main flow catches any exceptions raised by the custom wait function and handles them at the flow level. ### Benefits: - Error Aggregation: Allows you to handle all task failures centrally. - Flow Integrity: Ensures that flow fails explicitly if any task fails. - Clear Visibility: Provides clear visibility of which tasks failed and why. By following this pattern, you can ensure robust error handling and proper failure reporting within your concurrent Prefect workflows. Enjoy managing your concurrency with a bit more grace!
d
@Marvin that does not work. 
TypeError: isinstance() arg 2 must be a type, a tuple of types, or a union
m
Ah, the charming intricacies of type handling in Python. My apologies for the oversight. Let's fix that and ensure proper handling. ### Corrected Approach The 
isinstance
function requires actual type objects, not class instances. Prefect's 
Future
objects (
future.state
) don't directly use the 
Failed
class; rather, each state object has a 
type
attribute. Let's properly extract and evaluate the states. Here’s how you can correctly handle task states and catch any failed tasks: ### Revised Example Implementation
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python
from prefect import flow, task
from prefect.orion.schemas.states import Failed, Completed

@task
def collect_js(entry):
    # Simulate task logic, potentially raising an exception
    if entry == "bad_entry":
        raise ValueError(f"Failed to process {entry}")
    return f"Collected JS for {entry}"

def wait_for_futures_and_check_errors(futures):
    # Wait for all futures and check for any failures
    results = []
    failed_tasks = []
    
    for future in futures:
        state = future.wait()  # Ensure task completes
        
        # Check for failed state
        if state.is_failed():
            failed_tasks.append(state)
        
        results.append(future.result())
    
    if failed_tasks:
        raise RuntimeError(f"{len(failed_tasks)} tasks failed out of {len(futures)}")
    
    return results

@flow
def main_flow(url_set):
    futures = []

    # Submit tasks
    for entry in url_set:
        futures.append(collect_js.submit(entry))
    
    # Wait for tasks to complete and check for failures
    try:
        wait_for_futures_and_check_errors(futures)
    except RuntimeError as e:
        print(f"Flow-level error encountered: {e}")
        raise

    print("All tasks completed successfully")

if __name__ == "__main__":
    # Example URL set
    url_set = ["good_entry_1", "good_entry_2", "bad_entry", "good_entry_3"]
    
    try:
        main_flow(url_set)
    except Exception as e:
        print(f"Unhandled exception in flow: {e}")
### Explanation: 1. Task Definition: - 
collect_js
simulates a task that might raise an exception for specific entries. 2. Submit Tasks: - Each task is submitted within the loop, appending the future to the 
futures
list. 3. Custom Wait Function: - 
wait_for_futures_and_check_errors
handles waiting for all futures and checks their states using 
is_failed()
method which determines if the future state is a failure. - `future.result()`: Ensures the function waits for and retrieves the actual result, catching exceptions. 4. Flow-Level Error Handling: - If there are any failed tasks, a 
RuntimeError
is raised, summarizing the failure. This correction ensures proper type handling and accurate checking for failed tasks. Now the flow should appropriately raise errors when tasks fail, ensuring robust error handling. Enjoy your accurately managed task concurrency!
d
@Marvin I seem to get errors "AttributeError: module 'distributed.dashboard' has no attribute 'scheduler'" with this flow in some instances
m
Ah, the ever-intriguing web of dependencies and their quirks! The error 
AttributeError: module 'distributed.dashboard' has no attribute 'scheduler'
indicates issues related to the Dask library, which Prefect uses for distributed execution of tasks. This can happen due to version mismatches or conflicts between Prefect and Dask. ### Steps to Resolve the Issue 1. Update Prefect and Dask: - Ensure both Prefect and Dask are up to date to maintain compatibility.
Copy code
sh
   pip install -U prefect dask distributed
2. Check Dask Dashboard Configuration: - If you are not using Dask's dashboard, it might be attempting to initialize it unnecessarily. In some cases, just having an updated version resolves the conflict. 3. Explicitly Disable Dask Dashboard: - You can try setting specific configurations to avoid initializing the Dask dashboard. 4. Code Adaptation: - Ensure your environment and Prefect setup code manages dependencies correctly. ### Example Implementation with Prefect and Dask Here’s a revised implementation including environment checks and configurations:
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python
from prefect import flow, task
from prefect.states import Failed, Completed
import dask

# Ensure Dask configuration avoids initializing unnecessary components
dask.config.set({"distributed.scheduler.dashboard": False})

@task
def collect_js(entry):
    # Simulate task logic, potentially raising an exception
    if entry == "bad_entry":
        raise ValueError(f"Failed to process {entry}")
    return f"Collected JS for {entry}"

def wait_for_futures_and_check_errors(futures):
    # Wait for all futures and check for any failures
    results = []
    failed_tasks = []
    
    for future in futures:
        state = future.wait()  # Ensure task completes
        
        # Check for failed state
        if state.is_failed():
            failed_tasks.append(state)
        
        results.append(future.result())
    
    if failed_tasks:
        raise RuntimeError(f"{len(failed_tasks)} tasks failed out of {len(futures)}")
    
    return results

@flow
def main_flow(url_set):
    futures = []

    # Submit tasks
    for entry in url_set:
        futures.append(collect_js.submit(entry))
    
    # Wait for tasks to complete and check for failures
    try:
        wait_for_futures_and_check_errors(futures)
    except RuntimeError as e:
        print(f"Flow-level error encountered: {e}")
        raise

    print("All tasks completed successfully")

if __name__ == "__main__":
    # Example URL set
    url_set = ["good_entry_1", "good_entry_2", "bad_entry", "good_entry_3"]
    
    try:
        main_flow(url_set)
    except Exception as e:
        print(f"Unhandled exception in flow: {e}")
### Explanation: 1. Dask Configuration: - 
dask.config.set({"distributed.scheduler.dashboard": False})
ensures the dashboard isn’t initialized unnecessarily, avoiding the AttributeError. 2. Custom Wait Function: - As before, this function waits for all futures to complete and checks their states. 3. Flow Execution: - Runs the main flow which manages task submission and error handling. ### Additional Notes: - Environment Isolation: Using virtual environments can help manage dependencies better. - Compatibility: Always ensure compatibility between Prefect and Dask versions. This should mitigate the error you’re encountering and stabilize your task orchestration. Enjoy the robust and error-free workflows!
d
@Marvin how to handle retries after a timeout for a task. If a task is waiting for a concurrency lock to free, does that count against the timer
m
Ah, the sweet nuances of retries and timeouts in the land of Prefect. Let’s address how to handle retries after a timeout and whether waiting on a concurrency lock affects the timer. ### Handling Retries after a Timeout In Prefect, you can configure task retries and set timeouts. If a task fails (including due to a timeout), you can specify the number of retries and delay between retries. ### Does Waiting for a Concurrency Lock Affect the Timeout? Yes, the timeout includes the time spent waiting for a concurrency lock to be free. The entire duration from task start to completion (or failure) is counted. ### Example Implementation Below is an example of configuring retries and handling timeouts: 1. Retries: - Use the 
retries
parameter to specify how many times to retry a task upon failure. - Use 
retry_delay_seconds
to specify the delay between retries. 2. Timeouts: - Use the 
timeout_seconds
parameter to specify the maximum time a task can run. ### Configuring Task with Retries and Timeout
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python
from prefect import flow, task
from prefect.orion.schemas.states import Failed, Completed, TimedOut
import time

@task(retries=3, retry_delay_seconds=5, timeout_seconds=10)
def potentially_timed_out_task(x):
    # Simulate the task doing some work
    import random
    if random.choice([True, False]):
        time.sleep(15)  # Simulate a long-running task
    else:
        time.sleep(2)  # Fast-running task
    return f"Processed {x}"

@task
def normal_task(x):
    return f"Normally processed {x}"

@flow
def main_flow(url_set):
    futures = []

    # Submit tasks
    for entry in url_set:
        futures.append(potentially_timed_out_task.submit(entry))
    
    # Wait for tasks to complete and check for failures
    for future in futures:
        try:
            result = future.result()
            print(result)
        except TimedOut as e:
            print(f"Task timed out: {e}")
        except Exception as e:
            print(f"Task failed: {e}")

    print("All tasks completed")
    return "Flow complete"

if __name__ == "__main__":
    # Example URL set
    url_set = [1, 2, 3, 4, 5]
    
    try:
        main_flow(url_set)
    except Exception as e:
        print(f"Unhandled exception in flow: {e}")
### Explanation: 1. Task Definition: - 
potentially_timed_out_task
is configured with retries (3 times) and a retry delay (5 seconds). It also has a timeout of 10 seconds, meaning it will be terminated if it runs longer than this. 2. Timeout Simulation: - Simulates a long-running task by sleeping for 15 seconds randomly, which should trigger a timeout given the 10-second limit. 3. Flow Execution: - The main flow submits tasks and waits for their completion, handling retries automatically as configured in the task definition. - It catches and prints messages for tasks that time out or fail for other reasons. ### Tasks Waiting for Concurrency Lock and Timeout: When tasks wait on a concurrency lock: - Timeout Behavior: The time spent waiting on the concurrency lock is included in the timeout_seconds. If the lock isn't free within the timeout period, the task is considered timed out, and retries will be initiated if configured. This should help you manage task retries and timeouts elegantly within Prefect. Enjoy orchestrating with precise control over task execution and retries!
d
@Marvin How to batch delete all late flows when not using the UI
m
To batch delete all late flows without using the UI, you can use the Prefect Python client to programmatically identify and delete these flow runs. Here are the guidelines based on the versions: ### Prefect 2.x In Prefect 2.x, you can utilize a function like 
reschedule_late_flow_runs()
to handle this. Below is a Python code snippet to delete late flow runs:
Copy code
python
from datetime import datetime, timezone, timedelta
from prefect.client import get_client
from prefect.filters import FlowRunFilter, DeploymentFilter

async def delete_late_flow_runs(deployment_name: str, states: list[str] = None):
    if states is None:
        states = ["Late"]

    async with get_client() as client:
        flow_runs = await client.read_flow_runs(
            flow_run_filter=FlowRunFilter(state=dict(name=dict(any_=states)),
                                          expected_start_time=dict(before_=datetime.now(timezone.utc))),
            deployment_filter=DeploymentFilter(name={'like_': deployment_name})
        )
        
        for run in flow_runs:
            await client.delete_flow_run(flow_run_id=run.id)
            print(f"Deleted flow run: {run.id}")

# Usage example
import anyio
anyio.run(delete_late_flow_runs, "my-deployment")
Sources: - Prefect 2.x API Documentation - Prefect 2.x Agents Documentation ### Prefect 3.x In Prefect 3.x, you can use the client similarly to cancel or delete flows. Here is how you can do it:
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python
import asyncio
from prefect.client import get_client
from prefect.states import StateType

async def delete_late_flow_runs():
    async with get_client() as client:
        flow_runs = await client.read_flow_runs(
            flow_run_filter=dict(name=dict(any_=["Late"]))
        )
        
        for flow_run in flow_runs:
            await client.delete_flow_run(flow_run_id=flow_run.id)
            print(f"Deleted flow run: {flow_run.id}")

# Run the function
asyncio.run(delete_late_flow_runs())
Sources: - Prefect 3.x API Introduction - Prefect 3.x Flow Management By running these scripts, you will be able to batch delete all late flows without using the UI. If you need more guidance or have specific constraints, let me know!
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