MPIRE, short for MultiProcessing Is Really Easy, is a Python package for multiprocessing.
MPIRE is faster in
most scenarios, packs more features, and is generally more user-friendly than the default multiprocessing package. It
combines the convenient map like functions of
multiprocessing.Pool with the benefits of using copy-on-write shared
multiprocessing.Process, together with easy-to-use worker state, worker insights, worker init and exit
functions, timeouts, and progress bar functionality.
Full documentation is available at https://sybrenjansen.github.io/mpire/.
- Faster execution than other multiprocessing libraries. See benchmarks.
- Intuitive, Pythonic syntax
- Multiprocessing with
- Easy use of copy-on-write shared objects with a pool of workers (copy-on-write is only available for start method
- Each worker can have its own state and with convenient worker init and exit functionality this state can be easily manipulated (e.g., to load a memory-intensive model only once for each worker without the need of sending it through a queue)
- Progress bar support using tqdm
- Progress dashboard support
- Worker insights to provide insight into your multiprocessing efficiency
- Graceful and user-friendly exception handling
- Timeouts, including for worker init and exit functions
- Automatic task chunking for all available map functions to speed up processing of small task queues (including numpy arrays)
- Adjustable maximum number of active tasks to avoid memory problems
- Automatic restarting of workers after a specified number of tasks to reduce memory footprint
- Nested pool of workers are allowed when setting the
- Child processes can be pinned to specific or a range of CPUs
- Optionally utilizes dill as serialization backend through multiprocess, enabling parallelizing more exotic objects, lambdas, and functions in iPython and Jupyter notebooks.
MPIRE is tested on Linux and Windows. For Windows and macOS users, there are a few minor known caveats, which are documented in the Troubleshooting chapter.
Through pip (PyPi):
pip install mpire
MPIRE is also available through conda-forge:
conda install -c conda-forge mpire
Suppose you have a time consuming function that receives some input and returns its results. Simple 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
import time from multiprocessing import Pool def time_consuming_function(x): time.sleep(1) # Simulate that this function takes long to complete return ... with Pool(processes=5) as pool: results = pool.map(time_consuming_function, range(10))
MPIRE can be used almost as a drop-in replacement to
multiprocessing. We use the
mpire.WorkerPool class and
call one of the available
from mpire import WorkerPool with WorkerPool(n_jobs=5) as pool: results = pool.map(time_consuming_function, range(10))
The differences in code are small: there's no need to learn a completely new multiprocessing syntax, if you're used to
multiprocessing. The additional available functionality, though, is what sets MPIRE apart.
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
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.
Note: Copy-on-write shared objects is only available for start method
threading the objects are shared
as-is. For other start methods the shared objects are copied once for each worker, which can still be better than once
If you have one or more objects that you want to share between all workers you can make use of the copy-on-write
shared_objects option of MPIRE. MPIRE will pass on these objects only once for each worker without
copying/serialization. Only when you alter the object in the worker function it will start copying it for that worker.
def time_consuming_function(some_object, x): time.sleep(1) # Simulate that this function takes long to complete return ... def main(): some_object = ... with WorkerPool(n_jobs=5, shared_objects=some_object) as pool: results = pool.map(time_consuming_function, range(10), progress_bar=True)
See shared_objects for more details.
Workers can be initialized using the
worker_init feature. Together with
worker_state you can load a model, or
set up a database connection, etc.:
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 feature 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 worker_exit
section for more information.
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 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
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.
Timeouts can be set separately for the target,
worker_exit functions. When a timeout has been
set and reached, it will throw a
def init(): ... def exit_(): ... # Will raise TimeoutError, provided that the target function takes longer # than half a second to complete with WorkerPool(n_jobs=5) as pool: pool.map(time_consuming_function, range(10), task_timeout=0.5) # Will raise TimeoutError, provided that the worker_init function takes longer # than 3 seconds to complete or the worker_exit function takes longer than # 150.5 seconds to complete with WorkerPool(n_jobs=5) as pool: pool.map(time_consuming_function, range(10), worker_init=init, worker_exit=exit_, worker_init_timeout=3.0, worker_exit_timeout=150.5)
threading as start method MPIRE won't be able to interrupt certain functions, like
See timeouts for more details.
MPIRE has been benchmarked on three different benchmarks: numerical computation, stateful computation, and expensive initialization. More details on these benchmarks can be found in this blog post. All code for these benchmarks can be found in this project.
In short, the main reasons why MPIRE is faster are:
forkis available we can make use of copy-on-write shared objects, which reduces the need to copy objects that need to be shared over child processes
- Workers can hold state over multiple tasks. Therefore you can choose to load a big file or send resources over only once per worker
- Automatic task chunking
The following graph shows the average normalized results of all three benchmarks. Results for individual benchmarks can be found in the blog post. The benchmarks were run on a Linux machine with 20 cores, with disabled hyperthreading and 200GB of RAM. For each task, experiments were run with different numbers of processes/workers and results were averaged over 5 runs.
See the full documentation at https://sybrenjansen.github.io/mpire/ for information on all the other features of MPIRE.
If you want to build the documentation yourself, please install the documentation dependencies by executing:
pip install mpire[docs]
pip install .[docs]
Documentation can then be build by using Python <= 3.9 and executing:
python setup.py build_docs
Documentation can also be build from the
docs folder directly. In that case
MPIRE should be installed and
available in your current working environment. Then execute: