python - 如何在异步函数中并行化 for 循环并跟踪 for 循环执行状态？

Question

最近，我问了一个关于如何在部署的 API 中跟踪 for 循环进度的问题。这是link .
solution code对我有用的是，

from fastapi import FastAPI, UploadFile
from typing import List
import asyncio
import uuid


context = {'jobs': {}}

app = FastAPI()


async def do_work(job_key, files=None):
    iter_over = files if files else range(100)
    for file, file_number in enumerate(iter_over):
        jobs = context['jobs']
        job_info = jobs[job_key]
        job_info['iteration'] = file_number
        job_info['status'] = 'inprogress'
        await asyncio.sleep(1)
    jobs[job_key]['status'] = 'done'


@app.get('/')
async def get_testing():
    identifier = str(uuid.uuid4())
    context['jobs'][identifier] = {}
    asyncio.run_coroutine_threadsafe(do_work(identifier), loop=asyncio.get_running_loop())

    return {"identifier": identifier}


@app.get('/status/{identifier}')
async def status(identifier):
    return {
        "status": context['jobs'].get(identifier, 'job with that identifier is undefined'),
    }

这样，我可以在 do_work 中跟踪 for 循环的进度。通过调用 status method 使用标识符
现在，我正在寻找一种方法来并行化 do_work 中的 for 循环方法。
但是如果我使用 joblib那么我不知道如何跟踪正在处理的每个文件，迭代计数将毫无意义，因为所有文件都将并行处理。
注:我只是举了一个 joblib 的例子，因为我对其他库不是很熟悉。对文件的处理是基于 CPU 的繁重工作。我正在预处理文件并加载 4 个 tensorflow 模型并在文件上预测它并写入 sql 数据库。
如果有人知道我可以做到的任何方法，请分享并帮助我。

Answer 1

我不是 100% 确定我理解，这样的事情会起作用吗？

async def do_work(job_key, files=None):
    iter_over = files if files else range(100)
    jobs = context['jobs']
    job_info = jobs[job_key]
    job_info['iteration'] = 0

    async def do_work_inner(file):
        # do the work on the file here
        job_info['iteration'] += 1
        await asyncio.sleep(0.5)

    tasks = [do_work_inner(file) for file in iter_over]
    job_info['status'] = 'inprogress'
    await asyncio.gather(*tasks)
    jobs[job_key]['status'] = 'done'

这将并行运行文件上的所有工作*，请记住，在这种情况下，job_info['iteration'] 几乎没有意义，因为它们一起开始，它们将一起增加值。

这是异步并行的，这意味着它不是并行的，但事件循环会不断地从一个任务跳转到另一个任务。

请注意，这非常重要，您要对文件执行的实际工作类型是什么，如果它是与 CPU 相关的工作(计算、分析等)，而不是主要与 IO 相关的工作(如网络调用)，那么这就是错误的解决方案，应该稍微调整一下，如果是这样，请告诉我，我会尝试更新它。
编辑:cpu 相关工作的更新版本，进度显示文件已完成
这是一个比较完整的例子，只是没有实际的服务器

import time
import asyncio
import random
from concurrent.futures import ProcessPoolExecutor



jobs = {}
context = {}
executor = ProcessPoolExecutor()


def do_work_per_file(file, file_number):
    # CPU related work here, this method is not async
    # do the work on the file here
    print(f'Starting work on file {file_number}')
    time.sleep(random.randint(1,10) / 10)
    return file_number


async def do_work(job_key, files=None):
    iter_over = files if files else range(15)
    jobs = context['jobs']
    job_info = jobs[job_key]
    job_info['completed'] = 0

    loop = asyncio.get_running_loop()
    tasks = [loop.run_in_executor(executor,do_work_per_file, file, file_number) for file,file_number in enumerate(iter_over)]
    job_info['status'] = 'inprogress'
    for completed_job in asyncio.as_completed(tasks):
        print(f'Finished work on file {await completed_job}')
        job_info['completed'] += 1
        print('Current job status is ', job_info)
        

    jobs[job_key]['status'] = 'done'
    print('Current job status is ', job_info)

if __name__ == '__main__':
    context['jobs'] = jobs
    jobs['abc'] = {}
    asyncio.run(do_work('abc'))

输出是

Starting work on file 0
Starting work on file 1
Starting work on file 2
Starting work on file 3
Starting work on file 4
Starting work on file 5
Starting work on file 6
Starting work on file 7
Starting work on file 8
Starting work on file 9
Starting work on file 10
Starting work on file 11
Starting work on file 12
Starting work on file 13
Starting work on file 14
Finished work on file 1
Current job status is  {'completed': 1, 'status': 'inprogress'}
Finished work on file 7
Current job status is  {'completed': 2, 'status': 'inprogress'}
Finished work on file 9
Current job status is  {'completed': 3, 'status': 'inprogress'}
Finished work on file 12
Current job status is  {'completed': 4, 'status': 'inprogress'}
Finished work on file 11
Current job status is  {'completed': 5, 'status': 'inprogress'}
Finished work on file 13
Current job status is  {'completed': 6, 'status': 'inprogress'}
Finished work on file 4
Current job status is  {'completed': 7, 'status': 'inprogress'}
Finished work on file 14
Current job status is  {'completed': 8, 'status': 'inprogress'}
Finished work on file 0
Current job status is  {'completed': 9, 'status': 'inprogress'}
Finished work on file 6
Current job status is  {'completed': 10, 'status': 'inprogress'}
Finished work on file 2
Current job status is  {'completed': 11, 'status': 'inprogress'}
Finished work on file 3
Current job status is  {'completed': 12, 'status': 'inprogress'}
Finished work on file 8
Current job status is  {'completed': 13, 'status': 'inprogress'}
Finished work on file 5
Current job status is  {'completed': 14, 'status': 'inprogress'}
Finished work on file 10
Current job status is  {'completed': 15, 'status': 'inprogress'}
Current job status is  {'completed': 15, 'status': 'done'}

基本上改变的是现在你正在打开一个处理文件工作的新进程池，作为一个新进程也意味着 CPU 密集型工作不会阻塞你的事件循环并阻止你查询作业的状态。