@Marvin I am using prefect 3.0.8. Show me examples of complex flows with multiple parallel tasks running.

To create complex flows with multiple parallel tasks in Prefect 3.0.8, you can use task runners and concurrency limits. Here are some useful resources and concepts: 1. Parallel Execution with Task Runners: Prefect allows you to optimize parallel execution by using different task runners, such as Dask. You can group tasks into nested flows that run concurrently. This can be especially helpful for tasks that are computationally intensive or require separate resources. 2. Asynchronous Execution: Prefect supports asynchronous functions, allowing your flows to run as coroutines. This means tasks can be awaited or run concurrently using standard async Python practices. This is particularly useful for IO-bound tasks. 3. Task Run Concurrency Limits: To manage the number of tasks running in parallel, Prefect offers task run concurrency limits through tagging. You can set a specific concurrency limit for tasks using tags to prevent exceeding resource limits, like database connections. You can dive into these concepts and see code examples by visiting the Prefect documentation on writing flows and using task runners. Another relevant section is about task run limits, which explains how to control task execution using concurrency limits. By understanding these concepts, you'll be well-equipped to construct robust and efficient workflows in Prefect. It's like setting up an elaborate Rube Goldberg machine where everything runs flawlessly without a hitch—unless you throw a spanner in the works, of course.

Show me examples of using prefect with dask

To run parallel tasks using Dask in Prefect 3.0.8, you can use the Prefect-Dask integration which makes it quite straightforward. Below are the steps and code example to help you set it up: 1. Install Prefect-Dask: Ensure you have the

prefect-dask

installed. You can do this by running:

pip install prefect-dask

2. Import DaskTaskRunner: In your flow, import the

DaskTaskRunner

. 3. Define and Run the Flow: Utilize the

DaskTaskRunner

in your flow's decorator to enable parallel execution. Here's a simple example demonstrating these steps:

python
from prefect import task, flow
from prefect_dask import DaskTaskRunner

@task
def download_image(image_url):
    # simulate downloading an image
    print(f"Downloading image from {image_url}")
    return image_url

@flow(task_runner=DaskTaskRunner())
def download_images_flow(image_urls):
    results = []
    for url in image_urls:
        results.append(download_image.submit(url))
    return results

if __name__ == "__main__":
    image_urls = [
        "<http://example.com/image1.jpg>",
        "<http://example.com/image2.jpg>",
        "<http://example.com/image3.jpg>"
    ]
    download_images_flow(image_urls)

In this example, each call to

download_image.submit(url)

will be submitted to Dask's task runner, allowing the tasks to run in parallel. For more detailed information, you can visit the Prefect-Dask integration documentation which covers concepts like parallel execution and client management using Dask with Prefect. This allows you to not only run tasks in parallel but also to distribute them over multiple machines if necessary. Remember, using Dask can help you transform your simple flow into a powerhouse of parallel execution, much like turning a single CPU into an army of tiny task-solving robots!