Can I use async/await to accelerate init classes? - python-3.7

Question

I'm a python starter, and I'm trying to write some data analysis programs. the program is like below:
import asyncio
import time
class Test:
def __init__(self, task):
self.task = task
time.sleep(5) # here's some other jobs...
print(f'{self.task = }')
async def main():
result = []
tasks = ['task1', 'task2', 'task3', 'task4', 'task5', 'task6', 'task7', 'task8', 'task9']
print(f"started at {time.strftime('%X')}")
# I have a program structure like this, can I use async?
# how to start init tasks at almost the same time?
for task in tasks:
result.append(Test(task))
print(f"finished at {time.strftime('%X')}")
asyncio.run(main())
I've tried some other way like multiprocessing, it works, code like below:
...
def main():
result = []
tasks = ['task1', 'task2', 'task3', 'task4', 'task5', 'task6', 'task7', 'task8', 'task9']
print(f"started at {time.strftime('%X')}")
# I have a program structure like this, can I use async?
# how to start init tasks at the same time?
p = Pool()
result = p.map(operation, [(task,) for task in tasks])
print(f"finished at {time.strftime('%X')}")
...
but I still want to learn some 'modern way' to do this. I've found a module named 'ray', it's new.
But could async do this? I'm still wondering...
If someone can give me some advice, thanks a lot.

Answer 1

Your example code won't necessarily benefit from async IO, because __init__ is not "awaitable". You might be able to benefit from async if your code were structured differently and has an appropriate bottleneck. For example, if we had:
class Task:
def __init__(self):
<some not io bound stuff>
<some io bound task>
We could re-structure this to:
class Task:
def __init__(self):
<some not io bound stuff>
async def prime(self):
await <some io bound task>
Then in your main loop you can initialise the tasks as you're doing, then run the slow prime step in your event loop.
My advice here though would be to resist doing this unless you know you definitely have a problem. Coroutines can be quite fiddly, so you should only do this if you need to do it!