@Marvin what is the ideal way in such a scenario. I have a masterflow that executes multiple subflows, how can I execute subflows in parallel

To execute subflows in parallel within a master flow in Prefect 3.x, you can use the

prefect.futures.as_completed

function or the

prefect.utilities.asyncutils.gather

function to manage parallel execution. Here's a basic approach using Prefect's API: 1. Define Subflows: Define your subflows as separate flow functions using the

@flow

decorator. 2. Execute Subflows in Parallel: In your master flow, use

asyncio.gather

to run subflows concurrently. Prefect's async utilities can help manage this. Here's a simplified example:

python
from prefect import flow
import asyncio

@flow
async def subflow_1():
    # Subflow logic here
    pass

@flow
async def subflow_2():
    # Subflow logic here
    pass

@flow
async def master_flow():
    # Run subflows in parallel
    await asyncio.gather(
        subflow_1(), 
        subflow_2()
    )

if __name__ == "__main__":
    master_flow()

References: - Prefect Async Utilities - Prefect Futures This approach presumes you're comfortable with asynchronous programming in Python. If not, you might want to get familiar with

asyncio

basics.

@Marvin can I also use multiprocessing library for introducing real parallelism

Using the

multiprocessing

library with Prefect is not directly supported for executing flows in parallel. Prefect primarily leverages asynchronous execution and concurrency through its task runners and async utilities. The

multiprocessing

library creates separate processes, which may not integrate well with Prefect's flow orchestration and state management. For real parallelism, Prefect recommends using task runners like Dask, which are designed for distributed computing and can manage parallel execution more effectively within the Prefect ecosystem. You can create a nested flow using a task runner optimized for parallel execution, such as Dask, by specifying it in your flow configuration. References: - Prefect Task Runners - Global Concurrency Limits If you're committed to using

multiprocessing

, you might need to handle the process management outside of Prefect's flow logic and integrate Prefect for orchestration and state tracking. However, this approach might complicate the flow design and is not the typical use case for Prefect.

@Marvin so If my subflow 1 and subflow 2 are both computation heavy I still only can run them on the single node?

Yes, if you're using Prefect's default execution model, the subflows will run on a single node. Prefect's default setup is designed for orchestration rather than distributed computation. However, you can achieve distributed execution by integrating with task runners like Dask or Ray, which allow you to distribute tasks across multiple nodes. Here's a brief overview of how you might set this up: 1. Dask Task Runner: Prefect can integrate with Dask to distribute tasks across a Dask cluster. This allows you to leverage multiple nodes for computation-heavy tasks. 2. Ray Task Runner: Similarly, Prefect can integrate with Ray, which is another framework for distributed computing. To use these task runners, you'd specify them in your flow configuration. Here's a basic example using Dask:

python
from prefect import flow
from prefect_dask import DaskTaskRunner

@flow(task_runner=DaskTaskRunner(cluster_kwargs={"n_workers": 4}))
def master_flow():
    # Call subflows here
    pass

This setup requires that you have a Dask cluster available. Prefect will submit tasks to the cluster, allowing them to run across multiple nodes. References: - Dask Integration with Prefect - Ray Integration with Prefect These integrations allow you to scale your computations horizontally, distributing tasks across multiple machines or nodes.

@Marvin but the dasktaskrunners only help me for parallel task execution not parallel flow execution?

You're correct that the

DaskTaskRunner

is primarily used for parallel task execution within a flow, not for executing multiple flows in parallel. Prefect's task runners, like Dask and Ray, are designed to handle parallelism at the task level, distributing tasks across a cluster to improve performance for computation-heavy operations. For executing multiple subflows in parallel, you would typically manage this by invoking the subflows asynchronously within your master flow. Prefect does not natively support running multiple flows in parallel across different nodes using Dask directly as it does with tasks. However, you can still achieve parallel execution of subflows by using Python's

asyncio.gather

or similar async constructs to run them concurrently, assuming they are not dependent on each other. Here's a refined example using async execution:

python
from prefect import flow
import asyncio

@flow
async def subflow_1():
    # Computation-heavy logic here
    pass

@flow
async def subflow_2():
    # Computation-heavy logic here
    pass

@flow
async def master_flow():
    # Run subflows in parallel
    await asyncio.gather(
        subflow_1(), 
        subflow_2()
    )

if __name__ == "__main__":
    master_flow()

This approach allows you to execute subflows concurrently on the same node. If you need to distribute these across multiple nodes, you would typically orchestrate this at a higher level, potentially using a distributed task queue or another orchestration tool to manage multiple Prefect flow runs.

@Marvin but within my subflow I could add new tasks that I distribute over my nodes?

Yes, within each subflow, you can use Prefect's task runners like

DaskTaskRunner

to distribute tasks across nodes in a Dask cluster. This allows you to take advantage of distributed computing for the tasks within a subflow, even if the subflows themselves are managed on a single node. Here's how you might set this up: 1. Define Tasks: Define tasks that are computation-heavy and can be parallelized. 2. Use DaskTaskRunner: Within your subflow, specify the

DaskTaskRunner

to handle these tasks. Here's a basic example:

python
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def heavy_computation(x):
    # Some computation-heavy logic
    return x * x

@flow(task_runner=DaskTaskRunner(cluster_kwargs={"n_workers": 4}))
def subflow_1():
    results = []
    for i in range(10):
        result = heavy_computation.submit(i)
        results.append(result)
    # Gather results
    return [r.result() for r in results]

@flow
async def master_flow():
    # Run subflows, potentially in parallel
    await subflow_1()

if __name__ == "__main__":
    master_flow()

In this setup: -

DaskTaskRunner

is used within

subflow_1

to distribute the

heavy_computation

tasks across a Dask cluster with 4 workers. - You can run multiple subflows in parallel using

asyncio

as discussed before, and within each subflow, tasks can be distributed across nodes using Dask. References: - Dask Integration with Prefect - Prefect Task Runners