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<@ULVA73B9P> prefect 2 limit concurrent task submi...
# ask-marvin
m
@Marvin prefect 2 limit concurrent task submission to 4
m
To limit concurrent task submissions to 4 in Prefect 2, you can use Prefect's built-in task concurrency limits, which are configured using task tags. Here's a step-by-step approach: 1. Use Task Tags: Assign a tag to tasks you wish to limit concurrently. You can specify a concurrency limit for this tag. 2. Set Concurrency Limit: Use the concurrency limit setting for your task's tag to restrict the number of concurrent executions. In your case, you would set the limit to 4. Here's an example demonstrating how to use tags for managing concurrency:
Copy code
python
from prefect import flow, task, concurrency

# Define a task with a tag for concurrency limiting
@task(tags=["my-task-tag"])
def my_task(i):
    return i

@flow
def my_flow():
    for i in range(10):
        my_task.submit(i)

# Set the concurrency limit at runtime
concurrency.create_concurrency_limit("my-task-tag", 4)

if __name__ == "__main__":
    my_flow()
In this code, the 
concurrency.create_concurrency_limit("my-task-tag", 4)
sets the limit for tasks with the tag 
"my-task-tag"
to a maximum of 4 concurrent executions. For more information on this feature, you can refer to the Prefect 2.x documentation on task concurrency limits and global concurrency limits.
m
@Marvin wouldn't this submit all the tasks at once?
m
In Prefect 2.x, using 
task.submit()
within a flow often queues all tasks simultaneously regardless of concurrency limits, which handle task execution rather than submission. To throttle submission directly, you can use a rate limit function. Here's an approach using rate limiting to control the submission process, thereby preventing too many tasks from being submitted at once:
Copy code
python
from prefect import flow, task
from prefect.concurrency.sync import rate_limit

@task
def my_task(i):
    # Task logic here
    print(f"Processing task {i}")

@flow
def my_flow():
    for i in range(10):
        # Apply rate limiting for task submission
        rate_limit("task_submission_limiter", 4)  # Limit to 4 concurrent submissions
        my_task.submit(i)

if __name__ == "__main__":
    my_flow()
In this code, each task submission is controlled by the 
rate_limit
function, which ensures that tasks are submitted at a controlled rate, effectively acting as a bottleneck and aligning submission with your desired concurrency limit. For more information about this feature, you can check the Prefect 2.x documentation on global concurrency limits.
m
@Marvin i have an array of s3 objects strings and I want to submit a task per string. But I want to limit tasks to only submit 4 at a time. If a task is complete I want to submit another task
m
To achieve this, you can use Python's 
asyncio
and Prefect's task concurrency handling to manage task submissions while controlling the number of concurrent tasks. Here's how you can set it up: 1. Use 
asyncio
to manage the concurrency of task submissions. 2. Utilize a semaphore to limit the number of concurrently running tasks. 3. As a task completes, submit another from the queue. Below is an example implementation:
Copy code
python
import asyncio
from prefect import flow, task
from prefect.concurrency.asyncio import rate_limit

# Define a task that processes each S3 object
@task
async def process_s3_object(s3_object):
    # Simulate task processing
    print(f"Processing {s3_object}")
    await asyncio.sleep(1)  # Simulate work by sleeping

# Define the main flow that submits tasks
@flow
async def my_s3_flow(s3_objects):
    semaphore = asyncio.Semaphore(4)  # Limit to 4 concurrent tasks

    async def submit_task(s3_obj):
        async with semaphore:
            await process_s3_object(s3_obj)

    tasks = [submit_task(obj) for obj in s3_objects]
    await asyncio.gather(*tasks)

if __name__ == "__main__":
    # Example list of S3 object strings
    s3_objects = [f"<s3://bucket/object_{i}>" for i in range(10)]
    asyncio.run(my_s3_flow(s3_objects))
### Key Points: - Async Semaphore: This limits the number of tasks that can run concurrently to four. - Async/Await: Using 
asyncio
allows you to efficiently handle asynchronous tasks. - Dynamic Submission: If a task finishes, another is automatically started because it's queued by 
asyncio.gather
. This approach will effectively manage the submission and execution of tasks, maintaining only 4 concurrent tasks at any time, and once a slot is freed, another task from the queue is started.
m
@Marvin will this use the task runner I specify?
m
In Prefect, when you specify a task runner, it is important to ensure that the runner is correctly applied to your tasks and flows. In the async example provided above, task and flow runners must be configured explicitly to use a non-default runner if needed. To ensure your tasks use the specified task runner, you can configure the runner in your flow definition or in the configuration when setting up your environment. Here's how you can do it with a specific task runner, for example, the `DaskTaskRunner`:
Copy code
python
import asyncio
from prefect import flow, task
from prefect.concurrency.asyncio import rate_limit
from prefect.task_runners import DaskTaskRunner

# Define a task that processes each S3 object
@task
async def process_s3_object(s3_object):
    # Simulate task processing
    print(f"Processing {s3_object}")
    await asyncio.sleep(1)  # Simulate work by sleeping

# Define the main flow that submits tasks
@flow(task_runner=DaskTaskRunner())
async def my_s3_flow(s3_objects):
    semaphore = asyncio.Semaphore(4)  # Limit to 4 concurrent tasks

    async def submit_task(s3_obj):
        async with semaphore:
            await process_s3_object(s3_obj)

    tasks = [submit_task(obj) for obj in s3_objects]
    await asyncio.gather(*tasks)

if __name__ == "__main__":
    # Example list of S3 object strings
    s3_objects = [f"<s3://bucket/object_{i}>" for i in range(10)]
    asyncio.run(my_s3_flow(s3_objects))
### Important Points: - Configuring the Task Runner: The 
task_runner
parameter in the 
@flow
decorator is used to specify runners like 
DaskTaskRunner
or any other runner you use. - Runner Compatibility: Ensure that the runner you choose is compatible with asynchronous workflows, especially when using 
asyncio
. Refer to the Prefect documentation on task runners for more details on how to configure and use different task runners.
m
@Marvin prefect 2 dasktaskrunner spin up x workers given runtime arg
m
To configure the 
DaskTaskRunner
in Prefect 2.x to spin up a specific number of workers based on a runtime argument, you can leverage the 
cluster_kwargs
settings for a 
DaskTaskRunner
instance. These allow you to specify the number of worker processes and threads for each worker at runtime. Here's an example of how this can be done:
Copy code
python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

@task
def my_task(data):
    print(f"Processing {data}")
    # Simulate some work
    return data * 2

@flow(task_runner=DaskTaskRunner(
    cluster_kwargs={"n_workers": 4, "threads_per_worker": 1})
)
def my_flow(data):
    results = my_task.map(data)
    return results

if __name__ == "__main__":
    # Pass your data and runtime configurations here
    data = [1, 2, 3, 4, 5]
    my_flow(data)
### Key Details: - 
n_workers
: This parameter specifies the number of processes in the Dask cluster. In this example, we set it to 4, so 4 worker processes will be created. - 
threads_per_worker
: This specifies the number of threads per worker process. You can adjust this to balance between CPU-bound and I/O-bound workloads. You can adjust the 
n_workers
and 
threads_per_worker
values dynamically by passing them when creating the 
DaskTaskRunner
. This example hard-codes these values, but you can modify the flow or task runner creation to accept these parameters from a configuration file or command-line arguments to make it more flexible. For further customization and details, you can refer to the Prefect 2.x documentation on task runners.
m
@Marvin I meant given a flow arg num workers can I spin up a dask cluster based on that arg
m
To dynamically configure the number of workers in a 
DaskTaskRunner
based on a flow argument, you can pass the configuration through 
cluster_kwargs
when instantiating the task runner within the flow. Here's how you can manage this: 1. Accept the number of workers as an argument to your flow. 2. Use this argument to set 
n_workers
in the 
cluster_kwargs
when creating the 
DaskTaskRunner
. Here's an example implementation:
Copy code
python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

@task
def my_task(data):
    print(f"Processing {data}")
    return data * 2  # Simulate some work

@flow
def my_flow(data, num_workers):
    # Create the DaskTaskRunner with dynamic worker count
    task_runner = DaskTaskRunner(cluster_kwargs={"n_workers": num_workers, "threads_per_worker": 1})
    
    # Set the task runner for the flow
    with task_runner:
        results = my_task.map(data)
        return results

if __name__ == "__main__":
    # Example data
    data = [1, 2, 3, 4, 5]
    # User-defined number of workers
    num_workers = 4
    my_flow(data, num_workers)
### Key Points: - Dynamic 
n_workers
: The 
num_workers
argument is passed when initiating the flow and directly used to set the 
n_workers
parameter in 
cluster_kwargs
. - Flexibility: This approach allows you to adjust the number of workers as needed per flow execution, making it adaptable to different workloads. For further details or customization, you can refer to Prefect's task runner documentation. This page offers insights into running and configuring Dask clusters with Prefect.
m
@Marvin can I use adapt kwargs with dask task runner
m
Yes, you can use 
adapt_kwargs
with the 
DaskTaskRunner
in Prefect 2.x. This allows for adaptive scaling of your Dask cluster, adjusting the number of workers based on the workload. Here's how you can configure it: ### Adaptive Scaling with 
adapt_kwargs
Adaptive scaling allows Dask to dynamically adjust the number of workers based on the tasks' requirements. You specify a minimum and maximum number of workers, and the cluster adjusts itself within these limits. Here's how you can use 
adapt_kwargs
to configure adaptive scaling:
Copy code
python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

@task
def my_task(data):
    print(f"Processing {data}")
    return data ** 2  # Simulate some work

@flow
def my_flow(data):
    # Setup a DaskTaskRunner with adaptive scaling
    task_runner = DaskTaskRunner(
        cluster_kwargs={"n_workers": 1},  # Can set an initial number of workers
        adapt_kwargs={"minimum": 1, "maximum": 10}  # Adaptive scaling range
    )
    
    with task_runner:
        results = my_task.map(data)
        return results

if __name__ == "__main__":
    # Example data
    data = list(range(10))
    my_flow(data)
### Key Points: - 
minimum
: The minimum number of workers your cluster can scale down to. - 
maximum
: The maximum number of workers your cluster can scale up to. - Dynamic Management: Dask will manage resources efficiently, scaling up when more workers are needed, and scaling down when they are not. By using 
adapt_kwargs
, you can ensure your Dask cluster uses resources efficiently, providing flexibility for variable workloads. For more details, check the Prefect 2.x documentation on task runners.
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