Getting started¶
Suppose you have a time consuming function that receives some input and returns its results. This could look like the following:
import time
def time_consuming_function(x):
time.sleep(1) # Simulate that this function takes long to complete
return ...
results = [time_consuming_function(x) for x in range(10)]
Running this function takes about 10 seconds to complete.
Functions like these are known as embarrassingly parallel problems, functions that require little to no effort to
turn into a parallel task. Parallelizing a simple function as this can be as easy as importing multiprocessing
and
using the multiprocessing.Pool
class:
from multiprocessing import Pool
with Pool(processes=5) as pool:
results = pool.map(time_consuming_function, range(10))
We configured to have 5 workers, so we can handle 5 tasks in parallel. As a result, this function will complete in about 2 seconds.
MPIRE can be used almost as a drop-in replacement to multiprocessing
. We use the mpire.WorkerPool
class and
call one of the available map
functions:
from mpire import WorkerPool
with WorkerPool(n_jobs=5) as pool:
results = pool.map(time_consuming_function, range(10))
Similarly, this will complete in about 2 seconds. The differences in code are small: there’s no need to learn a
completely new multiprocessing syntax, if you’re used to vanilla multiprocessing
. The additional available
functionality, though, is what sets MPIRE apart.
Progress bar¶
Suppose we want to know the status of the current task: how many tasks are completed, how long before the work is ready?
It’s as simple as setting the progress_bar
parameter to True
:
with WorkerPool(n_jobs=5) as pool:
results = pool.map(time_consuming_function, range(10), progress_bar=True)
And it will output a nicely formatted tqdm progress bar.
MPIRE also offers a dashboard, for which you need to install additional dependencies. See Dashboard for more information.
Worker initialization¶
Need to initialize each worker before starting the work? Have a look at the worker_state
and worker_init
functionality:
def init(worker_state):
# Load a big dataset or model and store it in a worker specific worker_state
worker_state['dataset'] = ...
worker_state['model'] = ...
def task(worker_state, idx):
# Let the model predict a specific instance of the dataset
return worker_state['model'].predict(worker_state['dataset'][idx])
with WorkerPool(n_jobs=5, use_worker_state=True) as pool:
results = pool.map(task, range(10), worker_init=init)
Similarly, you can use the worker_exit
parameter to let MPIRE call a function whenever a worker terminates. You can
even let this exit function return results, which can be obtained later on. See the Worker init and exit section for
more information.
Worker insights¶
When your multiprocessing setup isn’t performing as you want it to and you have no clue what’s causing it, there’s the worker insights functionality. This will give you some insight in your setup, but it will not profile the function you’re running (there are other libraries for that). Instead, it profiles the worker start up time, waiting time and working time. When worker init and exit functions are provided it will time those as well.
Perhaps you’re sending a lot of data over the task queue, which makes the waiting time go up. Whatever the case, you
can enable and grab the insights using the enable_insights
flag and mpire.WorkerPool.get_insights()
function,
respectively:
with WorkerPool(n_jobs=5, enable_insights=True) as pool:
results = pool.map(time_consuming_function, range(10))
insights = pool.get_insights()
See Worker insights for a more detailed example and expected output.