You can scale out workloads to many containers using the .map() method. You might use this for parallelizing computational-heavy tasks, such as batch inference or data processing jobs.

from beam import function


@function(cpu=0.1)
def square(i: int):
    return i**2


def main():
    numbers = list(range(10))
    squared = []

    # Run a remote container for every item in list
    for result in square.map(numbers):
        print(result)
        squared.append(result)


if __name__ == "__main__":
    main()

When we run this Python module, 10 containers will be spawned to run the workload:

$ python math-app.py

=> Building image
=> Using cached image
=> Syncing files

=> Running function: <map-example:square>
=> Running function: <map-example:square>
=> Running function: <map-example:square>
=> Running function: <map-example:square>
=> Running function: <map-example:square>
=> Running function: <map-example:square>
=> Running function: <map-example:square>
=> Running function: <map-example:square>
=> Running function: <map-example:square>
=> Running function: <map-example:square>

=> Function complete <a6a1c063-b0d7-4c62-b6b1-a7940b19fde9>
=> Function complete <531e1f2c-a4f2-4edf-9cb9-6240df959815>
=> Function complete <bc421f5a-e09b-42d4-8035-d3d13ca5c238>
=> Function complete <2a3dde03-20df-4805-8fb0-ec9743f2bde3>
=> Function complete <59b64517-7b4a-4260-8c65-d0fbb9b98a76>
=> Function complete <f0ab7790-e2fb-441f-8278-74856719a457>
=> Function complete <1256a9ac-c035-412a-ac65-c94248f1ce99>
=> Function complete <476189dd-ba28-4646-9911-96ef8794cb58>
=> Function complete <04ef44cd-ff64-4ef2-a087-00c01ce5a2e4>
=> Function complete <104a602c-93a7-43d5-983c-071f64d91a2c>

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