joblib中的batch_size和pre_dispatch到底意味着什么

Question

来自此处的文档https://pythonhosted.org/joblib/parallel.html#parallel-reference-documentation我不清楚batch_size和pre_dispatch究竟是什么意思。

让我们考虑使用'multiprocessing'后端，2个作业（2个进程）并且我们有10个任务要计算的情况。

我认为：

batch_size - 一次控制pickle任务的数量，所以如果你设置batch_size = 5 - joblib将pickle并立即向每个进程发送5个任务，并且在到达之后它们将按顺序一个接一个地解决。 使用batch_size=1 ，当且仅当该进程完成上一个任务时，joblib将一次pickle并发送一个任务。

显示我的意思：

def solve_one_task(task):
    # Solves one task at a time
    ....
    return result

def solve_list(list_of_tasks):
    # Solves batch of tasks sequentially
    return [solve_one_task(task) for task in list_of_tasks]

所以这段代码：

Parallel(n_jobs=2, backend = 'multiprocessing', batch_size=5)(
        delayed(solve_one_task)(task) for task in tasks)

等于此代码（在性能方面）：

slices = [(0,5)(5,10)]
Parallel(n_jobs=2, backend = 'multiprocessing', batch_size=1)(
        delayed(solve_list)(tasks[slice[0]:slice[1]]) for slice in slices)

我对吗？ 那pre_dispatch意味着什么？

Answer 1

事实证明，我是对的，并且两段代码在性能方面非常相似，因此batch_size工作方式与我在Question中的预期相同。 pre_dispatch（作为文档状态）控制任务队列中实例化任务的数量。

from sklearn.externals.joblib import Parallel, delayed
from time import sleep, time

def solve_one_task(task):
    # Solves one task at a time
    print("%d. Task #%d is being solved"%(time(), task))
    sleep(5)
    return task

def task_gen(max_task):
    current_task = 0
    while current_task < max_task:
        print("%d. Task #%d was dispatched"%(time(), current_task))
        yield current_task
        current_task += 1

Parallel(n_jobs=2, backend = 'multiprocessing', batch_size=1, pre_dispatch=3)(
        delayed(solve_one_task)(task) for task in task_gen(10))

输出：

1450105367. Task #0 was dispatched
1450105367. Task #1 was dispatched
1450105367. Task #2 was dispatched
1450105367. Task #0 is being solved
1450105367. Task #1 is being solved
1450105372. Task #2 is being solved
1450105372. Task #3 was dispatched
1450105372. Task #4 was dispatched
1450105372. Task #3 is being solved
1450105377. Task #4 is being solved
1450105377. Task #5 was dispatched
1450105377. Task #5 is being solved
1450105377. Task #6 was dispatched
1450105382. Task #7 was dispatched
1450105382. Task #6 is being solved
1450105382. Task #7 is being solved
1450105382. Task #8 was dispatched
1450105387. Task #9 was dispatched
1450105387. Task #8 is being solved
1450105387. Task #9 is being solved
Out[1]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]