@Marvin using a DaskTaskRunner and given a python list of elements, how can I gather the results from calling a task on each element of the list using

.submit()

in an asynchronous manner?

To gather results from calling a task on each element of a list using

.submit()

in an asynchronous manner with a

DaskTaskRunner

, you can follow the pattern outlined in the code example below. Make sure that for each element in the list, you submit the task and then gather all the task futures. After that, you can wait for all the task runs to complete asynchronously. Here's an example code that demonstrates how you could achieve this, though please adapt the details (like the task being run) to your specific needs:

python
from prefect import flow, task
from prefect.task_runners import DaskTaskRunner

@task
def process_element(element):
    # replace this with the actual processing logic
    return f"Processed: {element}"

@flow(task_runner=DaskTaskRunner())
def process_elements_flow(elements):
    futures = [process_element.submit(element) for element in elements]
    results = [future.result() for future in futures]  # wait for results
    return results

if __name__ == "__main__":
    elements = ['element1', 'element2', 'element3']
    result = process_elements_flow(elements)
    print(result)

In this example,

process_element

is a task that will be applied to each element. When

.submit()

is called on a task, it returns a

Future

object immediately, without waiting for the task to complete; this allows the flow to continue running other tasks or logic asynchronously. After submission, the flow compiles a list of futures. Then, it waits for the tasks to finish and retrieves the results using the

result()

method on each future. Note that

.map()

may also be relevant if you want to execute a task over a list of inputs in parallel; however, the

.map()

method is blocking until all mapped task runs are complete. Remember that using

DaskTaskRunner

requires Dask to be installed and an accessible Dask cluster. For more specific examples and information about Prefect and the

DaskTaskRunner

, you can refer to the Prefect documentation.

@Marvin can you explain all the elements of the Prefect architecture?

The Prefect architecture is designed to be modular and flexible in order to cater to a variety of use cases and infrastructure requirements. Here are some key elements of the Prefect architecture outlined in the documents: 1. APIs for Code and UI Interfaces: Prefect automatically transforms every object into discrete APIs, which are accessible by users. This allows for the creation of deployment templates to submit work via an API or the ability to build highly custom workflows. 2. Prefect UI: This user interface enables teams to track and observe their workflows, submit templated workflows with parameters, and monitor their progress. 3. Workers and Work Pools: Prefect's architecture allows defining custom infrastructure patterns through workers and work pools. This facilitates operational control, such as: - Creating opinionated infrastructure patterns. - Tightly controlling infrastructure usage with role-based access control (RBAC) and deployments. - Dynamically assigning infrastructure to switch between cloud providers and services with ease. 4. Hybrid Model: Prefect operates under a hybrid model where execution and orchestration are separate. This ensures that data and code can remain on-premises while still leveraging Prefect Cloud to isolate and scale the infrastructure supporting workflow execution. 5. Deployment Creators: Prefect provides clear interfaces to create guardrails for different developer personas, such as deployment creators, which can utilize work pools. These components are part of a platform approach that allows data engineers, data scientists, and others to build and orchestrate workflows effectively while meeting specific infrastructure requirements. The modular and adaptable nature of Prefect’s architecture equips platform teams to develop custom solutions that align with their organizational needs. For more in-depth information on Prefect's architecture, you can review the introductory blog post here and explore further documentation and FAQs on their website.

@Marvin can you generate an image of the Prefect architecture?

I'm not able to directly create images, but I can guide you to Prefect's documentation and resources that might provide diagrams or visual representations of its architecture. Generally, an architecture diagram for Prefect would illustrate the relationship between key components such as the Prefect server (or Prefect Cloud), the Prefect UI, the API, agents (now replaced by workers), flows, and tasks. To find an official diagram or image of Prefect's architecture, you would typically need to check Prefect's documentation or blog posts that discuss the architecture. If Prefect has publicly shared images of their architecture, they would likely be available in those places. Since I cannot create images, I recommend visiting the Prefect documentation website or contacting Prefect's support directly for the most accurate and up-to-date visual representations of their architecture. Here is the link to Prefect's documentation where you can explore more: Prefect Docs.