dask delayed

Delayed Best Practices — Dask documentation

How to parallelize Python code with Dask Delayed (YouTube)

Dask Delayed — How to Parallelize Your Python Code With Ease | by Dario Radečić | Towards Data Science

With list comprehension

code:python

@delayed

def function():

....

compute(*list)

With Progress bar

code:python

from dask.distributed import progress,Client

from dask.diagnostics import ProgressBar

client=Client()

client

@delayed

def func(n):

...

results=[]

for n in range(100):

results.append(func(n))

futures = client.compute(results)

progress(futures)

code:python

Tips

When we used dask.delayed without having a distributed scheduler, we are relying on a single-machine scheduler, and dask will use the threadpool executor, which by default will use the resources available on your machine.

You can check this by doing:

code:python

import os

os.cpu_count()