"""
This module provides utility functions for parallel processing, such as:
- multiprocessing(): Execute a function in parallel using multiprocessing
- multithreading(): Execute a function in parallel using multithreading
I highly encourage you to read the function docstrings to understand when to use each method.
.. image:: https://raw.githubusercontent.com/Stoupy51/stouputils/refs/heads/main/assets/parallel_module.gif
:alt: stouputils parallel examples
"""
# Imports
import time
import multiprocessing as mp
from collections.abc import Callable, Iterator
from concurrent.futures import ThreadPoolExecutor
from multiprocessing import Pool, cpu_count
from typing import Any, TypeVar
from tqdm.auto import tqdm
from tqdm.contrib.concurrent import process_map # type: ignore
from .decorators import LogLevels, handle_error
from .print import MAGENTA, RESET
# Small test functions for doctests
def doctest_square(x: int) -> int:
return x * x
def doctest_slow(x: int) -> int:
time.sleep(0.1)
return x
# Constants
CPU_COUNT: int = cpu_count()
BAR_FORMAT: str = "{l_bar}{bar}" + MAGENTA + "| {n_fmt}/{total_fmt} [{rate_fmt}{postfix}, {elapsed}<{remaining}]" + RESET
T = TypeVar("T")
R = TypeVar("R")
# Private function to use starmap
def __starmap(args: tuple[Callable[[T], R], list[T]]) -> list[R]:
func, arguments = args
return func(*arguments) # type: ignore
# Private function to apply delay before calling the target function
def __delayed_call(args: tuple[Callable[[T], R], float, T]) -> R:
func, delay, arg = args
time.sleep(delay)
return func(arg)
[docs]
def __handle_parameters(
func: Callable[[T], R],
args: list[T],
use_starmap: bool,
delay_first_calls: float,
max_workers: int,
desc: str,
color: str
) -> tuple[str, Callable[[T], R], list[T]]:
r""" Private function to handle the parameters for multiprocessing or multithreading functions
Args:
func (Callable): Function to execute
args (list): List of arguments to pass to the function
use_starmap (bool): Whether to use starmap or not (Defaults to False):
True means the function will be called like func(\*args[i]) instead of func(args[i])
delay_first_calls (int): Apply i*delay_first_calls seconds delay to the first "max_workers" calls.
For instance, the first process will be delayed by 0 seconds, the second by 1 second, etc. (Defaults to 0):
This can be useful to avoid functions being called in the same second.
max_workers (int): Number of workers to use (Defaults to CPU_COUNT)
desc (str): Description of the function execution displayed in the progress bar
color (str): Color of the progress bar
Returns:
tuple[str, Callable[[T], R], list[T]]: Tuple containing the description, function, and arguments
"""
desc = color + desc
# If use_starmap is True, we use the __starmap function
if use_starmap:
args = [(func, arg) for arg in args] # type: ignore
func = __starmap # type: ignore
# Prepare delayed function calls if delay_first_calls is set
if delay_first_calls > 0:
args = [
(func, i * delay_first_calls if i < max_workers else 0, arg) # type: ignore
for i, arg in enumerate(args)
]
func = __delayed_call # type: ignore
return desc, func, args
[docs]
@handle_error(error_log=LogLevels.ERROR_TRACEBACK)
def multiprocessing(
func: Callable[[T], R],
args: list[T],
use_starmap: bool = False,
chunksize: int = 1,
desc: str = "",
max_workers: int = CPU_COUNT,
delay_first_calls: float = 0,
color: str = MAGENTA,
bar_format: str = BAR_FORMAT,
ascii: bool = False,
) -> list[R]:
r""" Method to execute a function in parallel using multiprocessing, you should use it:
- For CPU-bound operations where the GIL (Global Interpreter Lock) is a bottleneck.
- When the task can be divided into smaller, independent sub-tasks that can be executed concurrently.
- For computationally intensive tasks like scientific simulations, data analysis, or machine learning workloads.
Args:
func (Callable): Function to execute
args (list): List of arguments to pass to the function
use_starmap (bool): Whether to use starmap or not (Defaults to False):
True means the function will be called like func(\*args[i]) instead of func(args[i])
chunksize (int): Number of arguments to process at a time
(Defaults to 1 for proper progress bar display)
desc (str): Description displayed in the progress bar
(if not provided no progress bar will be displayed)
max_workers (int): Number of workers to use (Defaults to CPU_COUNT)
delay_first_calls (float): Apply i*delay_first_calls seconds delay to the first "max_workers" calls.
For instance, the first process will be delayed by 0 seconds, the second by 1 second, etc.
(Defaults to 0): This can be useful to avoid functions being called in the same second.
color (str): Color of the progress bar (Defaults to MAGENTA)
bar_format (str): Format of the progress bar (Defaults to BAR_FORMAT)
ascii (bool): Whether to use ASCII or Unicode characters for the progress bar
Returns:
list[object]: Results of the function execution
Examples:
.. code-block:: python
> multiprocessing(doctest_square, args=[1, 2, 3])
[1, 4, 9]
> multiprocessing(int.__mul__, [(1,2), (3,4), (5,6)], use_starmap=True)
[2, 12, 30]
> # Will process in parallel with progress bar
> multiprocessing(doctest_slow, range(10), desc="Processing")
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
> # Will process in parallel with progress bar and delay the first threads
> multiprocessing(
. doctest_slow,
. range(10),
. desc="Processing with delay",
. max_workers=2,
. delay_first_calls=0.6
. )
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
"""
# Handle parameters
verbose: bool = desc != ""
desc, func, args = __handle_parameters(func, args, use_starmap, delay_first_calls, max_workers, desc, color)
if bar_format == BAR_FORMAT:
bar_format = bar_format.replace(MAGENTA, color)
# Do multiprocessing only if there is more than 1 argument and more than 1 CPU
if max_workers > 1 and len(args) > 1:
def process() -> list[Any]:
if verbose:
return list(process_map(
func, args, max_workers=max_workers, chunksize=chunksize, desc=desc, bar_format=bar_format, ascii=ascii
)) # type: ignore
else:
with Pool(max_workers) as pool:
return list(pool.map(func, args, chunksize=chunksize)) # type: ignore
try:
return process()
except RuntimeError as e:
if "SemLock created in a fork context is being shared with a process in a spawn context" in str(e):
# Try with alternate start method
old_method: str | None = mp.get_start_method(allow_none=True)
new_method: str = "spawn" if old_method in (None, "fork") else "fork"
mp.set_start_method(new_method, force=True)
try:
return process()
finally:
mp.set_start_method(old_method, force=True)
else: # Re-raise if it's not the SemLock error
raise
# Single process execution
else:
if verbose:
return [func(arg) for arg in tqdm(args, total=len(args), desc=desc, bar_format=bar_format, ascii=ascii)]
else:
return [func(arg) for arg in args]
[docs]
@handle_error(error_log=LogLevels.ERROR_TRACEBACK)
def multithreading(
func: Callable[[T], R],
args: list[T],
use_starmap: bool = False,
desc: str = "",
max_workers: int = CPU_COUNT,
delay_first_calls: float = 0,
color: str = MAGENTA,
bar_format: str = BAR_FORMAT,
ascii: bool = False,
) -> list[R]:
r""" Method to execute a function in parallel using multithreading, you should use it:
- For I/O-bound operations where the GIL is not a bottleneck, such as network requests or disk operations.
- When the task involves waiting for external resources, such as network responses or user input.
- For operations that involve a lot of waiting, such as GUI event handling or handling user input.
Args:
func (Callable): Function to execute
args (list): List of arguments to pass to the function
use_starmap (bool): Whether to use starmap or not (Defaults to False):
True means the function will be called like func(\*args[i]) instead of func(args[i])
desc (str): Description displayed in the progress bar
(if not provided no progress bar will be displayed)
max_workers (int): Number of workers to use (Defaults to CPU_COUNT)
delay_first_calls (float): Apply i*delay_first_calls seconds delay to the first "max_workers" calls.
For instance with value to 1, the first thread will be delayed by 0 seconds, the second by 1 second, etc.
(Defaults to 0): This can be useful to avoid functions being called in the same second.
color (str): Color of the progress bar (Defaults to MAGENTA)
bar_format (str): Format of the progress bar (Defaults to BAR_FORMAT)
ascii (bool): Whether to use ASCII or Unicode characters for the progress bar
Returns:
list[object]: Results of the function execution
Examples:
.. code-block:: python
> multithreading(doctest_square, args=[1, 2, 3])
[1, 4, 9]
> multithreading(int.__mul__, [(1,2), (3,4), (5,6)], use_starmap=True)
[2, 12, 30]
> # Will process in parallel with progress bar
> multithreading(doctest_slow, range(10), desc="Threading")
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
> # Will process in parallel with progress bar and delay the first threads
> multithreading(
. doctest_slow,
. range(10),
. desc="Threading with delay",
. max_workers=2,
. delay_first_calls=0.6
. )
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
"""
# Handle parameters
verbose: bool = desc != ""
desc, func, args = __handle_parameters(func, args, use_starmap, delay_first_calls, max_workers, desc, color)
if bar_format == BAR_FORMAT:
bar_format = bar_format.replace(MAGENTA, color)
# Do multithreading only if there is more than 1 argument and more than 1 CPU
if max_workers > 1 and len(args) > 1:
if verbose:
with ThreadPoolExecutor(max_workers) as executor:
return list(tqdm(executor.map(func, args), total=len(args), desc=desc, bar_format=bar_format, ascii=ascii))
else:
with ThreadPoolExecutor(max_workers) as executor:
return list(executor.map(func, args))
# Single process execution
else:
if verbose:
return [func(arg) for arg in tqdm(args, total=len(args), desc=desc, bar_format=bar_format, ascii=ascii)]
else:
return [func(arg) for arg in args]
[docs]
def colored_for_loop(
iterable: list[T],
desc: str = "Processing",
color: str = MAGENTA,
bar_format: str = BAR_FORMAT,
ascii: bool = False,
**kwargs: Any
) -> Iterator[T]:
""" Function to iterate over a list with a colored TQDM progress bar like the other functions in this module.
Args:
iterable (list): List to iterate over
desc (str): Description of the function execution displayed in the progress bar
color (str): Color of the progress bar (Defaults to MAGENTA)
bar_format (str): Format of the progress bar (Defaults to BAR_FORMAT)
ascii (bool): Whether to use ASCII or Unicode characters for the progress bar (Defaults to False)
verbose (int): Level of verbosity, decrease by 1 for each depth (Defaults to 1)
**kwargs: Additional arguments to pass to the TQDM progress bar
Yields:
T: Each item of the iterable
Examples:
>>> for i in colored_for_loop(range(10), desc="Time sleeping loop"):
... time.sleep(0.01)
>>> # Time sleeping loop: 100%|██████████████████| 10/10 [ 95.72it/s, 00:00<00:00]
"""
if bar_format == BAR_FORMAT:
bar_format = bar_format.replace(MAGENTA, color)
desc = color + desc
yield from tqdm(iterable, desc=desc, bar_format=bar_format, ascii=ascii, **kwargs)
