moethread 1.4.2

Python wrapper for ThreadPoolExecutor to easily multithread resource bound tasks

Moethread

Table of Contents

• Overview
• Library Installalion
• Library Usage

Overview

Moethread is a python wrapper for the ThreadPoolExecutor library to easily multithread resource bound tasks. The library offers a decorator style of parallelizing function calls. NOTE, this only works for resource bound (API calls, network requests, disk read/write operations, etc) operations. If your task is CPU intensive, then this library may not offer much benefit and you're better off exploring other options such as multiporcessing.

Library Installalion

To install the library simply run the following command in a cmd, shell or whatever...

# Windows
pip install moethread

# Linux
pip3 install moethread

Library usage?

To start, you need to import the library

from moethread import parallel_call

If you need to read results back from the parallelized function, then you have to define the internal variables/objects globally where you can access them outside of that function. The function to parallelize will accept arguments and keyword arguments. Arguments are primitives/constants/variables that you'd like to pass through to your function. If you'd like to have counters inside the parallelized function, then define those globally as shown in the following code snippet.

global counter
counter = 0

As for the data which needs to be parallelized, this needs to be specified in the keywords argument. The keyword data is reserved for the input data. The input data is a dictionary collection of whatever needs to run in parallel.

For example if you have a dataset of images and you would like to read those images in parallel and those images have labels, then you have to create a dictionary of image paths and their corrosponding labels. You have to make sure that the two lists are aligned.

image_paths  = ["image_0.jpg", "image_1.jpg", ...] 	# some dummy paths
image_labels = [0, 1, ...] 		                # some dummy labels
assert len(image_paths) == len(image_labels)

# It's your responsiblity to ensure that elements align, e.g. image_labels[0] is the label for image_paths[0]
data = {"image_path": image_paths, "image_label": image_labels}

The next step is write the building block of your function. You will add the decorator @parallel_call on top of the function and assign *args and **kwargs as your function parameters. Inside the function, you will read the data dictionary which contains the path to image and its corrosponding label.

@parallel_call # decorator
def function_to_parallelize(*args, **kwargs):
	# Define globals...
	global counter
	# Read data in...
	image_path  = kwargs.get('data').get('image_path')
	image_label = kwargs.get('data').get('image_label')
	# Read image
	image = cv2.imread(image_path)
	if image_label == 1:
		counter += 1 # assume images with label == 1 are valid images
	## Do whatever you like to do below...

Lastly, you will just call the function and specify the number of threads. If you set threads = -1, then the libary will figure out the suitable number of threads for the task.

function_to_parallelize(data=data, threads=-1) # automatically assigns the needed number of threads...

Putting it all together.

from moethread import parallel_call

image_paths  = ["image_0.jpg", "image_1.jpg", ...] 	# some paths
image_labels = [0, 1, ...] 		                # some dummy labels
assert len(image_paths) == len(image_labels)

# It's your responsiblity to ensure that elements align, e.g. image_labels[0] is the label for image_paths[0]
data = {"image_path": image_paths, "image_label": image_labels}
global counter
counter = 0

@parallel_call # decorator
def function_to_parallelize(*args, **kwargs):
	# Define globals...
	global counter
	# Read data in...
	image_path  = kwargs.get('data').get('image_path')
	image_label = kwargs.get('data').get('image_label')
	# Read image
	image = cv2.imread(image_path)
	if image_label == 1:
		counter += 1 # assume images with label == 1 are valid images
	## Do whatever you like to do below...

function_to_parallelize(data=data, threads=-1) # Automatically assigns the needed number of threads...

Another example, Pull-request processing.

This examples shows how to read github pull requests and parse body content and return a list of github users who produced failed pull-requests.

from moethread import parallel_call

global invalid_pulls
github_users  = []
invalid_pulls = 0
github_token = ghx_test124
etag   = None
params = {'state': 'open'}
pulls  = list(self._iter(int(-1), url, repo.pulls.ShortPullRequest, params, etag))
@parallel_call
def process_pulls(*args, **kwargs):
    global invalid_pulls
    pull = kwargs.get('data').get('pulls')
    response = self._get(f'{url}/{pull.number}/reviews', auth=('', github_token))
    if response.ok:
        reviews = json.loads(response.text)
        for review in reviews:
            body = review.get('body', '').lower()
            err = "failure"
            if err in body:
                res = self._get(pull.user.url, auth=('', github_token))
                if res.ok:
                    github_user = json.loads(res.text)
                    github_users.append(github_user.get('login', ''))
                invalid_pulls += 1
                break
    elif response.status_code != 404:
        pass
process_pulls(data={"pulls": pulls}, threads=-1)

Ready to go functions

The library is packed with some ready to go functions that can be used to perform several operations using parallel_call without having to write code. All you have to do is to call those functions.

• mtdo()
• mtdo_from_json()
• mtdo_from_csv()

def mtdo(....)
	"""
	Performs a multithreaded data operation.

	Args:
		src_dir (str): source directory containing data to copy.
		dst_dir (str): destination directory to copy data to.
		op (str): operation type [cp: copy, mv: move, rm: delete, ren: rename].
		file_type (str, optional): type of data to copy, e.g '*.json' - copies json files only. Defaults to all data types '*.*'.
		sep_folder (str, optional): separation folder where right side directory structure is appended to destination directory,
									e.g. app/data/src/files, sep_folder='data', dest path -> os.path.join(dest_dir, 'src/files'). Defaults to ''.
		overwrite (bool, optional): whether to overwrite data in destination or skip already copied data on later trials. Defaults to False.
		prefix (str): prefix for image renaming, e.g prefix=data and image_name=im.jpg --> data_im.jpg
		threads (int, optional): number of threads to launch. Defaults to 8.
		**kwargs: Extra keywords such as (chunk_size: split data into equal sized chunks, verbose: supress moethread stdout), defaults to (chunk_size=5000, verbose=True)
	"""

def mtdo_from_json(....)
	"""Performs a multithreaded data operation for paths in json file.

	Args:
		file_path (str): input json file containing paths
		data_key (str): dictionary key holding file paths
		label_key (str): (optional) dictionary key holding labels for folders name to copy/move data to (classifying copied/moved data based on labels)
		op (str): operation type [cp: copy, mv: move].
		threads (int, optional): number of threads to launch. Defaults to 8.
		**kwargs: Extra keywords such as (chunk_size: split data into equal sized chunks, verbose: supress moethread stdout), defaults to (chunk_size=5000, verbose=True)
	"""

def mtdo_from_csv(....)
	"""Performs a multithreaded data operation for paths in csv file.

	Args:
		file_path (str): input json file containing paths
		data_key (str): dictionary key holding file paths
		label_key (str): (optional) dictionary key holding labels for folders name to copy/move data to (classifying copied/moved data based on labels)
		op (str): operation type [cp: copy, mv: move].
		threads (int, optional): number of threads to launch. Defaults to 8.
		**kwargs: Extra keywords such as (chunk_size: split data into equal sized chunks, verbose: supress moethread stdout), defaults to (chunk_size=5000, verbose=True)
	"""

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Author: Hamdan, Muhammad (@mhamdan91 - آ©)