tqdm
and dask
are both amazing packages for iterations in python. While tqdm
implements the needed progress bar, dask
implements the multi-thread platform and they both can make iteration process less frustrating. Yet - I'm having troubles to combine them both together.
For example, the following code implements a delayed execution in dask
, with tqdm.trange
progress bar. The thing is that since the delayed
is performed quickly, the progress bar ends immediately, while the real computation-time effort is done during the compute
part.
from dask import delayed,compute
from tqdm import trange
from time import sleep
ct = time()
result= []
def fun(x):
sleep(x)
return x
for i in trange(10):
result.append(delayed(fun)(i))
print compute(result)
How can I attach the progress bar to the actual execution in compute
command?
4. Working with a while loop and unknown increments. Instead of using tqdm as a wrapper, we can create it outside the loop and update it inside the loop on each iteration. This makes tqdm more flexible for loops with unknown length or unknown increments.
The Dask delayed function decorates your functions so that they operate lazily. Rather than executing your function immediately, it will defer execution, placing the function and its arguments into a task graph. Wraps a function or object to produce a Delayed .
In addition, a huge benefit of using tqdm instead of a different method for showing a progress bar is that tqdm has little overhead, around 60 nanoseconds per iteration — meaning it should not affect performance much, compared to something like ProgressBar, which has an overhead of 800 nanoseconds per iteration.
tqdm can be used with zip if a total keyword argument is provided in the tqdm call. The issue is that tqdm needs to know the length of the iterable ahead of time. Because zip is meant to handle iterables with different lengths, it does not have as an attribute a single length of its arguments.
from dask.diagnostics import ProgressBar
with ProgressBar():
compute(result)
You can use this plugin architecture to get a signal at the end of every task. http://dask.pydata.org/en/latest/diagnostics.html
Here is an example of someone doing exactly this: https://github.com/tqdm/tqdm/issues/278
Based on :
Dask Integration
from tqdm.dask import TqdmCallback
with TqdmCallback(desc="compute"):
...
arr.compute()
# or use callback globally
cb = TqdmCallback(desc="global")
cb.register()
arr.compute()
Applied to the code in the question:
from dask import delayed,compute
from tqdm.auto import tqdm
# from tqdm import trange
from time import sleep
from tqdm.dask import TqdmCallback
# ct = time()
result= []
def fun(x):
sleep(x)
return x
for i in tqdm(range(10)):
result.append(delayed(fun)(i))
with TqdmCallback(desc="compute"):
print(compute(result))
screenshot of output in jupyter :
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