@Marvin is there a benefit to using dasktaskrunner v concurrenttaskrunner if I don't have a separate remote dask cluster?

When dealing with task runners in Prefect, it's essential to know the distinction between concurrency and parallelism: 1. Concurrency refers to doing more than one thing simultaneously, but not necessarily at the exact same time. This is typically achieved via multi-threading or other techniques that allow tasks to share time slices. 2. Parallelism involves performing multiple tasks simultaneously at the exact same time, often leveraging multiple processors/cores. ### ConcurrentTaskRunner - Uses a thread pool maintained by

anyio

. - Suitable for tasks that benefit from concurrency but don't necessarily need to execute in parallel. - Efficient for I/O-bound tasks where tasks often wait on I/O operations. ### DaskTaskRunner - Designed for parallel task execution using

dask.distributed

. - Creates a temporary

distributed.LocalCluster

by default for parallel task execution. - Beneficial if your tasks are CPU-bound and can run simultaneously on different processors/cores. - Can be configured to use an external Dask cluster if one is available. ### Usage Without a Remote Dask Cluster - Even without a remote Dask cluster,

DaskTaskRunner

can still utilize parallelism by creating a local Dask cluster. This is useful for parallel execution on a single machine. - If you don't have a specific need for parallel execution or a Dask cluster,

ConcurrentTaskRunner

might be simpler and sufficient for most use cases. ### Relevant Documentation - Prefect - Task Runners Overview - Prefect - Dask and Ray Task Runners Guide - Prefect Dask Task Runners For further insights or discussion, you might want to dive into this GitHub issue, which mentions considerations for configuring task runners for different environments such as development and production. In conclusion, the choice between

ConcurrentTaskRunner

and

DaskTaskRunner

boils down to: - ConcurrentTaskRunner: Simplicity and sufficient for I/O-bound tasks. - DaskTaskRunner: Required for CPU-bound tasks needing true parallelism, even on a local machine.

@Marvin can I set the max number of threads for the concurrenttaskrunner?

Yes, you can set the maximum number of threads for the

ConcurrentTaskRunner

in Prefect. This can be done by specifying the

max_workers

parameter when initializing the

ConcurrentTaskRunner

. Here's an example:

python
from prefect import flow
from prefect.task_runners import ConcurrentTaskRunner

@flow(task_runner=ConcurrentTaskRunner(max_workers=10))
def example_flow():
    # Your flow logic here
    pass

if __name__ == "__main__":
    example_flow()

This configuration limits the

ConcurrentTaskRunner

to use a maximum of 10 threads. ### Relevant Sources: - Prefect Task Runners Documentation - Add Capacity Limiter to ConcurrentTaskRunner Pull Request With this setup, you can control the concurrency based on your application's requirements and the resources available, preventing excessive thread spawning which could otherwise lead to resource contention and performance issues.