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<@ULVA73B9P> Any idea why a function with subproce...
# ask-marvin
d
@Marvin Any idea why a function with subprocess.run that calls a binary works fine locally but when run in a docker container via prefect hangs for hours
m
It seems there aren't any directly related issues on the Prefect GitHub repository. However, a function using 
subprocess.run
that hangs in a Docker container but works fine locally could be caused by various issues, here are a few possibilities: 1. Docker Environment: The Docker container might lack some dependencies or environment settings required by the binary, resulting in it hanging when attempting to execute. 2. Buffering Issue: If the subprocess outputs a lot, it might be filling the buffer and causing a hang. This can sometimes be resolved by ensuring the output is captured and not allowed to fill the buffer. 3. Permissions: The Docker container may not have the correct permissions to execute the binary. 4. Path Issues: The binary’s path could be incorrect in the context of the Docker container, or it might not be included in the container's 
$PATH
. 5. Deadlock: The subprocess might be waiting on input or stuck in a deadlock state due to resource constraints in the Docker container. One approach to troubleshooting this could be to: 1. Ensure that the Docker container is set up similarly to the local environment regarding dependencies and configurations. 2. Run the subprocess with 
stdout
and 
stderr
piped to the console or log files to capture any possible outputs for debugging. 3. Check the permissions and paths inside the Docker container. 4. If possible, run the Docker container interactively and try to execute the binary manually to see if it provides any error messages. If these steps don't lead to a solution, you may consider posting the issue to the Prefect community or GitHub repository with details of the 
subprocess.run
command and the Docker container environment for more focused assistance.
d
@Marvin I have checked all of that and all seems correct. Sometimes it will eventually run after a few hours so not likely to be a permission or file access issue
m
Oh, the joys of intermittent issues – they're a delight, aren't they? Without context, diagnosing such a problem would be like searching for a black hole in a vast, empty space with a telescope lens smeared with jam – not impossible, but highly improbable. Could you provide me with some more details or context regarding the issue you're experiencing? It would help me a lot to have specifics like what software or process you're referring to, any error messages you're seeing, and what you've tried so far. This will enable me to do a more targeted search for documentation or similar issues that might help solve your problem.
d
@Marvin How can I lock a task and be thread safe so only one worker can run the task at a time
m
It appears you're interested in ensuring that a task within your workflow is run by only one worker at a time in a thread-safe manner. Prefect supports this concurrency control with global concurrency limits. To achieve this, you can use the concept of global concurrency limits in Prefect which allows you to specify the maximum number of task runs that can be in a running state at any one time across your entire infrastructure. Here’s how you can define a global concurrency limit: 1. Create a concurrency limit within the Prefect UI or via the CLI. 2. Assign the concurrency limit to a task by including the limit name in the decorator.
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python
from prefect import task

@task(name="Singleton task", concurrency_limit="single_task_limit")
def singleton_task():
    # Your task code here
In this pseudocode snippet, 
single_task_limit
would be the name of your previously created concurrency limit. When the limit is reached, additional task runs that are subject to the limit are queued until the running tasks complete, and available concurrency slots open up. For fine-grained control, you can configure slot decay using the 
slot_decay_per_second
parameter when defining or adjusting a concurrency limit, which is essentially the rate at which the concurrency slots become available again. For more detailed explanations and to adjust the configuration to fit your needs, check the Prefect documentation on Global Concurrency Limits. Note that it is crucial to keep monitoring your setup to make sure that it's functioning as expected under different load conditions. It's also good practice to watch out for open issues on the Prefect GitHub Issues page, like the one found here, as users might face similar challenges and their solutions or discussions could be helpful. Remember, it's not that I'm lazy, it's that I just don't care... about concurrency problems anymore, since Prefect has these features to handle them.
d
@Marvin where is the cpu max allocation per container managed?
m
The management of CPU maximum allocation per container, especially within the context of Prefect, typically occurs in the configuration of the container instances themselves, rather than within Prefect. This is often specified when defining your container execution environment, and the specifics can depend on the environment in question. For instance, if you are using Azure with Prefect, you would define the CPU resources it can use when configuring the container instances:
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python
from azure.containerinstance.models import ResourceRequests, ResourceRequirements

container_resource_requests = ResourceRequests(memory_in_gb=self.memory, cpu=self.cpu)
container_resource_requirements = ResourceRequirements(requests=container_resource_requests)
This example snippet is from the Prefect Azure integration documentation, which details how resources such as memory and CPU are configured for Azure Container Instances. While Prefect could be used to orchestrate jobs that run within containers, the actual resource limits, such as CPU and memory, are generally managed by the platform hosting the containers (e.g., Kubernetes, Docker, AWS ECS, Azure ACI). If you are using Kubernetes, for example, CPU and memory requests and limits are set in the container spec within a deployment configuration file. If you're using Docker, you might use the 
--cpus
flag when running a container to limit CPU usage. For more information on managed execution within Prefect and how resources are handled in that context, you can check the Managed Execution documentation, which discusses available resources under different tier accounts. Remember, managing resources for containers can be cheerlessly complicated, but it's an essential part of ensuring your workflows run efficiently. Just another day in the life of this vast universe of computing.
d
@Marvin I am running local docker containers using the Docker agent flow
m
To set CPU maximum allocation per container when using the Docker agent in Prefect, you typically need to pass resource limit arguments to Docker when a container is started by the agent. Since the results from our searches didn't show relevant information from Prefect Documentation within my scope, I will guide you based on general Docker practices. When using Docker containers, you can set resource limits directly with Docker using flags such as 
--cpus
when you run a container. Unfortunately, Prefect documentation isn't showing a direct way to pass these flags through the Docker agent. Prefect's Docker agent may not directly expose a configuration option to set resource limits on the containers it starts. In some systems, this can be manipulated through the agent's environment configuration or additional command flags. You might need to check the Prefect Docker agent's documentation directly or explore the source code (especially in the 
prefect_docker/worker.py
, as hinted by one of the search results) to understand how it starts containers and if it allows passing resource limit arguments. For example, if you were doing this outside of Prefect, you would start a container like this:
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bash
docker run -it --cpus="1.5" ubuntu
This sets a limit of 1.5 CPUs for the container. You may need to adjust your Docker agent startup script to include these kinds of flags or use a custom script that the agent calls which includes them. For a more accurate and specific answer, please refer to the latest Prefect documentation or contact the Prefect community for updates, since the mechanism for setting this up can change over time. As a general rule, ensure to monitor the behavior of your containers when setting limits to guarantee that the tasks have enough resources to operate effectively without causing resource starvation or affecting the performance of other containers. If only everything in the universe could be unlimited, but alas, such is the tyranny of reality.
d
@Marvin is there a way to read the name of the crashed task when triggering an automation to run a deployment
m
To read the name of the crashed task when triggering an automation to run a deployment in Prefect, you can make use of state change hooks which allow you to execute custom code in response to state changes. This would typically involve registering a state handler that triggers when the task enters a 'crashed' state, which you can then use to gather information about the task, like its name.
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python
from prefect import task, flow

# Example of a task that crashes
@task
def my_task_that_crashes():
    raise SystemExit("Crashing on purpose!")

# State handler function
def handle_state_change(task, old_state, new_state):
    if new_state.is_crashed():
        # Here, you can read the task name and perform your desired actions
        # Use `task.name` to access the name of the task that crashed
        print(f"Task {task.name} has crashed.")

@flow
def crashing_flow():
    my_task_that_crashes.add_state_handler(handle_state_change)
    my_task_that_crashes()

crashing_flow()
In this example, 
my_task_that_crashes
is a task that is deliberately designed to fail. With the 
handle_state_change
function, you can detect the state change to 'crashed' and retrieve the name of the crashed task. When setting up automations, you might need to configure your flow or task with an appropriate state handler function that sends the necessary information to your automation. This could mean sending an HTTP request, calling another function, or interacting with your deployment pipeline with the information about the crashed task. For more details on how to set up state change hooks and handle crashed task scenarios, please refer to the Prefect documentation on State Change Hooks. The specifics of integrating this with your automation system may vary depending on the tools and services you are using. Life, don't talk to me about life... but at least with Prefect, crashed tasks and automation have a systematic way of being handled. Now if only my diodes along my left side could get such attention.
d
@Marvin how to set a Global Concurrency Limits to 0 via python
m
Based on the information retrieved from the Prefect documentation on Global Concurrency Limits, there is mention of configuring a "slot decay" rate, but it does not address setting concurrency limits directly to 0. The concept of "slot decay per second" defines the rate at which concurrent slots become available again after being occupied. However, setting a global concurrency limit to 0 programmatically through Python is not commonly practical, as it would imply no tasks are allowed to run concurrently, effectively pausing execution. If you are looking to suspend task execution temporarily, you might want to disable or pause the relevant flows instead of setting a concurrency limit to 0. This would prevent tasks from running without altering concurrency settings, which normally are used to manage the load and not halt the workflow entirely. In the case that setting a global concurrency limit to 0 is indeed necessary for your use case (e.g., for administrative or maintenance reasons), it is likely that you would need to interact with the Prefect API to create or adjust concurrency limits. Such an operation might involve creating an API call to the Prefect backend to adjust the concurrency limits accordingly. However, specific Python code to perform this action isn't provided in the documentation, and direct manipulation of concurrency limits in this manner may be unsupported. For accurate functionality and if this operation is crucial, it would be best to consult the Prefect community or Prefect support directly, as the ability to set concurrency limits to 0 might involve advanced configurations or might be an operation distinct from typical use cases of Prefect.
d
@Marvin is there a way to add or delete concurrency limits via the python sdk?
m
Yes, there is a way to add or delete global concurrency limits via the Prefect Python SDK. The Prefect documentation on Global Concurrency Limits indicates that you can manage concurrency limits through your code. It is mentioned that you can create a limit with a certain number of slots, which are the maximum number of concurrent task runs that can be occupying the limit, and you can assign the limit to specific tasks. Slot decay per second is also adjustable, which controls the rate at which slots are released. However, the excerpts provided here don't include specific Python code for adding or deleting a concurrency limit directly. Based on standard practices with the Prefect SDK, to manage concurrency limits, you would interact with the Prefect client to create or delete concurrency limits. Since the exact code isn't provided in the documentation excerpts, here's a general idea of how you might approach it:
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python
from prefect import Client

client = Client()

# To create a global concurrency limit
client.create_concurrency_limit(name="my_limit", concurrency_limit=10)

# To delete a global concurrency limit
client.delete_concurrency_limit(name="my_limit")
Please note that the methods 
create_concurrency_limit
and 
delete_concurrency_limit
used in this example are illustrative and not necessarily part of the Prefect SDK. You will need to consult the Prefect SDK's client documentation or source code for the exact method names and usage. For specific functions and detailed examples of how to programmatically add or delete global concurrency limits, you should refer to the latest version of Prefect documentation or consider reaching out to their support or community for guidance. The Prefect community and forums are also good resources to find more specific answers if this functionality has been recently updated or changed.
d
@Marvin what is the expected pattern for a flow calling a task_runner=DaskTaskRunner flow. The former runs and the subsequent task sits in pending state for 15 mins
m
It seems you're experiencing a situation where a flow using the 
DaskTaskRunner
has subsequent tasks that sit in a pending state for an extended period. Based on the documentation and common patterns for using 
DaskTaskRunner
, here are some insights and troubleshooting steps. ### Step-by-Step Pattern for Using `DaskTaskRunner`: 1. Initialize the Dask Cluster: First, ensure that your Dask cluster is appropriately set up with the required resources and configurations. 2. Define and Configure the Flow: Use the 
DaskTaskRunner
in the flow definition. Ensure the tasks and flow are well-defined and are using the appropriate task runner. 3. Task and Flow Submission: Submit tasks within the flow using 
task.submit()
. Ensure that the tasks are correctly ordered and dependencies are appropriately managed. Here is a code example that demonstrates the usage pattern:
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python
from prefect import flow, task, get_run_logger
from prefect_dask import DaskTaskRunner
from dask.distributed import Client

# Initialize dask client
dask_client = Client(n_workers=4, threads_per_worker=2, memory_limit='2GB')

@task
def sample_task(task_name: str):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Running: {task_name}")

@flow(task_runner=DaskTaskRunner(address=dask_client.scheduler_address))
def dask_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting Dask flow")
    
    task_1 = sample_task.submit("task_1")
    task_2 = sample_task.submit("task_2", wait_for=[task_1])
    task_3 = sample_task.submit("task_3", wait_for=[task_2])
    
    <http://logger.info|logger.info>("Dask flow completed")

if __name__ == "__main__":
    dask_flow()
### Troubleshooting the Pending State Issue: 1. Resource Allocation: Ensure the Dask cluster has sufficient resources (CPU, memory). The pending state often indicates resource contention or insufficient resources available to execute the tasks. 2. Check Dask Scheduler and Workers: Monitor the Dask dashboard to ensure that the workers are correctly connected and are receiving tasks from the scheduler. 3. Dependencies and Task Order: Ensure tasks have the correct dependencies specified using the 
wait_for
parameter. This parameter should handle inter-task dependencies, ensuring each task starts only when the preceding ones are complete. 4. Worker Logs and Debug Information: Check worker logs for any errors or warnings that might indicate why tasks are sitting in a pending state. Issues such as memory exhaustion or task failures should be evident in the logs. 5. Task Submission Rate: Ensure that tasks are not being submitted at a rate faster than the workers can handle, leading to a backlog and extended pending states. By following this pattern and considering these troubleshooting steps, you should be able to diagnose and rectify the issue with tasks remaining in a pending state while using 
DaskTaskRunner
. For more details, you can refer to the Prefect documentation on DaskTaskRunner and the Dask documentation, which provide in-depth insights and examples. And remember, if the universe seems indifferent to your plight, at least you've got Prefect and Dask to manage the chaos, however whimsically.
d
@Marvin the question is about calling a generic flow which in turn calls another flow with task_runner=DaskTaskRunner which in turn calls a task
m
When dealing with nested flows where a generic flow calls another flow with 
DaskTaskRunner
, which subsequently calls a task, there can be complexities that lead to tasks pending or other issues. Here’s a concise guide on how to handle this: ### Expected Pattern for Nested Flows with DaskTaskRunner 1. Define the Inner Flow with 
DaskTaskRunner
: - Ensure the inner flow is using 
DaskTaskRunner
and that it is correctly configured. 2. Define the Outer Flow: - The outer flow should call the inner flow as part of its execution. 3. Submit Tasks Correctly: - Ensure tasks are submitted correctly and their dependencies are managed to prevent them from sitting in a pending state. Here's an example demonstrating this pattern:
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python
from prefect import flow, task, get_run_logger
from prefect_dask import DaskTaskRunner
from dask.distributed import Client

# Initialize dask client and configure the Dask cluster
dask_client = Client(n_workers=4, threads_per_worker=2, memory_limit='2GB')

@task
def sample_task(task_name: str):
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Running: {task_name}")

@flow(task_runner=DaskTaskRunner(address=dask_client.scheduler_address))
def inner_dask_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting Inner Dask Flow")

    task_1 = sample_task.with_options(name='task_1').submit("task_1")
    task_2 = sample_task.with_options(name='task_2').submit("task_2", wait_for=[task_1])
    task_3 = sample_task.with_options(name='task_3').submit("task_3", wait_for=[task_2])

    return task_3

@flow
def outer_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting Outer Flow")

    inner_flow_result = inner_dask_flow()
    # Process the result of the inner flow if needed
    <http://logger.info|logger.info>(f"Inner flow result: {inner_flow_result}")

if __name__ == "__main__":
    outer_flow()
### Troubleshooting Pending Tasks 1. Resource Adequacy: Ensure that the Dask cluster has sufficient resources to execute the tasks. 2. Correct Configuration: Verify the configuration of the 
DaskTaskRunner
to ensure it's connecting to the Dask cluster properly. 3. Monitor the Dask Cluster: Use the Dask dashboard to monitor the tasks' lifecycle and resource usage. 4. Logging and Debugging: Check logs for any errors or issues that may prevent tasks from executing. Ensure tasks are being enqueued correctly. 5. Timeouts and Delays: Adjust timeouts or anticipate potential delays in task scheduling. For more details on these configurations and handling such scenarios, refer to the Prefect DaskTaskRunner documentation and related GitHub issues, such as Flow cancellation which provides insights into task state handling and cancellation. Remember, even in a universe of constant flux, a structured approach to managing tasks and flows can mitigate these intermittent suspensions. Happy flow management!
d
@Marvin how to call a deployment from a flow?
m
To call a deployment from a flow, you can use the Prefect Python SDK to interact with the Prefect API and trigger the deployment programmatically. Here's how you can do it step-by-step: 1. Set Up Your Environment: Ensure you have the Prefect library installed and you are authenticated to your Prefect Cloud or Prefect Server instance. 2. Define Your Flow and Tasks: Define the main flow where you will be calling the deployment, and the deployment to be called. 3. Trigger the Deployment: Use the 
deployments.run_deployment
function from the Prefect Client to run the deployment from within your flow. Here's an example to demonstrate this:
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python
from prefect import flow, task, get_client, tags
from prefect.deployments import run_deployment

# Define a sample task to log some information
@task
def log_info(info: str):
    print(info)

# Define the deployment flow
@flow
def deployment_flow():
    log_info("This is the deployment flow running.")
    
# Define the main flow to trigger the deployment flow
@flow
def main_flow():
    log_info("Starting the main flow.")
    
    # Trigger the deployment as a subflow
    client = get_client()
    deployment_id = "your-deployment-id-here"  # Replace with your actual deployment ID
    client.run_deployment(deployment_id=deployment_id)
    
    log_info("Main flow has finished.")

# Entry point for running the main flow
if __name__ == "__main__":
    main_flow()
### Explanation: 1. Define Your Tasks and Flows: The 
log_info
task logs information. The 
deployment_flow
is a simple flow that runs as the deployment. The 
main_flow
calls this deployment using the 
run_deployment
function. 2. Get Client and Run Deployment: Within the 
main_flow
, we get a Prefect client instance and call the 
run_deployment
method with the deployment ID we want to trigger. 3. Replace `deployment_id`: Ensure to replace 
"your-deployment-id-here"
with the actual deployment ID you wish to call. ### Requirements: - Prefect Client Setup: Ensure that your environment is configured to interact with Prefect Cloud or Prefect Server. You might need to set up API keys and other credentials correctly. - Deployment ID: You will need the ID of the deployment you intend to trigger. You can find this in the Prefect Cloud or Prefect Server UI. ### References: - Prefect Deployment Documentation This pattern allows you to leverage Prefect's deployment capabilities programmatically within your flows, enabling modular and independent workflow components to be run as needed.
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