workercontext 0.0.2

A small package that provides a context to spin up multiple workers to execute a function

Multi-Worker Context

This is a small project that create a context in python which can spin up multiple workers to do a task.

The only requirement is that a the function you want to give multiple workers to has only one argument that you would like to batch on. It is up to you to ensure that creating independant batches for multiple workers makes sense with this argument.

This makes refactoring code for multiprocessing a lot easier and should provide quick performance wins for time consuming functions with no interdependencies between elements.

Setup

pip install workercontext

Example

By default the work distribution will occur on the first parameter to the function.

Take the following function:

from typing import List

def my_func(arr: List[int]) -> List[int]:
    return [el*2 for el in arr]

This can be simply split across multiple workers as follows:

from workercontext import workercontext

arr = list(range(100))

with workercontext(my_func, n_processes=8) as f:
    res = f(arr)
print(res)

res will be a list of lists of ints in this case, if you would like to reduce across all workers then you can pass a reduction:

from workercontext import workercontext
from workercontext.reductions import flatten_reduction

arr = list(range(100))

with workercontext(my_func, n_processes=8, reduction=flatten_reduction) as f:
    res = f(arr)
print(res)

res will now a list of int.

If you wanted to combine multiple reductions then you can use the reduction composition class

from workercontext import workercontext
from workercontext.reductions import ReductionComposition, flatten_reduction, average_reduction

reductions = ReductionComposition([flatten_reduction, average_reduction])

with workercontext(my_func, n_processes=8, reduction=reductions) as f:
    res = f(arr)
print(res)

This makes res be a single float.

Using other parameters

You can batch work on other parameters by specifying them in the constructor.

from workercontext import workercontext
from workercontext.reductions import flatten_reduction

def my_func(l1: List[int], l2: List[int]) -> int:
    for i in range(len(l2)):
        for el1 in l1:
            l2[i] += el1
    return l2

arr1 = list(range(100))
arr2 = list(range(100))

with workercontext(my_func, batched_arg='l2', n_processes=8, reduction=flatten_reduction) as f:
    res = f(arr1, arr2)
print(res)

Documentation

workercontext

parameters

• function (Callable): The function to create the context for.
• n_processes (int): The number of processes to spawn.
• batched_arg (str, optional): The argument to batch on, if None the the first arg is used. Defaults to None.
• verbose (bool, optional): Whether or not to print information about the processing. Defaults to False.
• reduction (Callable[[List[Any]], Any], optional): A reduction function to be applied across the outputs of the pool. Defaults to None.

Supported Reductions

• flatten_reduction
• histogram_reduction
• product_reduction
• string concatenation_reduction
• bitwise and_reduction
• bitwise or_reduction
• min_reduction
• max_reduction

Testing

pytest

Formatting

black .

How it works

TL;DR it does a bunch of introspection.

1. The args to your function are introspected.
2. The self arg is remove if you passed it a method from a class.
3. If no batched arg was specified then the first one is selected.
4. All args are converted into kwargs using the introspected arg names and the *args provided.
5. The size of the batched arg is calculated and the chunk sizes are derived.
6. The arg is batched and batches of kwargs are created.
7. A pool is created with a partial for a wrapper function that allows for the batching to occur on the kwargs. The last parameter to the wrapper is a callback to your function.
8. A reduction is applied (if specified)
9. Results are returned.
10. When you leave the context the pools are joined and closed.