@Marvin how does the task method .map work in Prefect 2.13.8?

In Prefect 2.13.8, the

.map()

method is used to automatically create a task run for each element of its input data. This is particularly useful for parallelizing operations over a collection of inputs. Here's a basic usage example:

python
from prefect import flow, task

@task
def square_num(num):
    return num ** 2

@flow
def map_flow(nums):
    squared_nums = square_num.map(nums)
    print(squared_nums)

map_flow([1, 2, 3, 5, 8, 13])

In this example, the

square_num

task is applied to each element in the

nums

list. The

.map()

method handles the parallel execution of the task for each list item. Prefect also supports unmapped arguments, which allows you to pass static values that don't get mapped over. If your static argument is an iterable, you should wrap it with

unmapped()

to ensure it is treated as a static value. For more details, you can refer to the Prefect 2.x documentation.

@Marvin I am having a problem with prefect cloud, because I am trying to parallelize almost 400 tasks using map, but the Prefect Cloud server is responding with 'Too many requests' error. How can we fix that?

The "Too many requests" error you're encountering when trying to parallelize tasks with Prefect Cloud may be related to rate limits imposed by the infrastructure you're using, such as Docker's rate limits on container pulls. Here are a few strategies you might consider to mitigate this issue: 1. Rate Limiting: If you're hitting Docker's pull rate limits, consider authenticating with Docker Hub to increase your pull rate limits. You can follow Docker's guidelines on how to increase rate limits by authenticating: Increase Docker Rate Limits. 2. Batching Tasks: Instead of running all 400 tasks simultaneously, you might batch them into smaller groups to avoid overwhelming the server. This can be done by splitting your input data into chunks and using

.map()

on each chunk sequentially. 3. Dask Task Runner: Utilize Prefect's

DaskTaskRunner

to handle task parallelization more efficiently. This allows you to manage how tasks are distributed across workers and can help with scaling issues. Ensure your Dask cluster is properly configured to handle the load. 4. Prefect Cloud Tier: Verify your Prefect Cloud tier and consider upgrading if necessary. Higher tiers may offer more resources and higher rate limits. 5. Custom Infrastructure: If you're deploying on custom infrastructure, ensure that your setup can handle the number of concurrent requests without hitting any internal limits. For more insights, you might want to check related GitHub issues such as Issue #12003 and Issue #12877, where similar problems have been discussed.

@Marvin Please provide an example of how to use the DaskTaskRunner

To use the

DaskTaskRunner

in Prefect, you need to ensure that the

prefect-dask

collection is installed. The

DaskTaskRunner

allows you to run tasks in parallel using Dask's distributed scheduler. Here's an example of how to set it up: 1. Install Prefect Dask:

bash
   pip install prefect-dask

2. Define a Flow with DaskTaskRunner: You can either create a temporary Dask cluster for the duration of the flow run or connect to an existing Dask cluster by providing its address.

python
from prefect import flow
from prefect_dask.task_runners import DaskTaskRunner

@flow(task_runner=DaskTaskRunner(address="<http://my-dask-cluster>"))
def my_flow():
    # Define your tasks here
    pass

# Run the flow
my_flow()

In this example,

DaskTaskRunner

is configured to connect to an existing Dask cluster at

<http://my-dask-cluster>

. If you don't specify an address, Prefect will create a temporary Dask cluster for you. For further details, you can refer to the Prefect Dask documentation.