# parallelism
Unlock advanced task scheduling in Python with `parallelism`.
Seamlessly coordinate parallel and concurrent execution, optimizing performance while ensuring code integrity.
Embrace expert scheduling techniques to elevate your programming to new heights of efficiency and responsiveness.
## Installation
You can install this package using pip:
```bash
pip install parallelism
```
## Basic Usage
Explore an effortless approach to task creation and management.
Follow these steps to get started:
1. Import the necessary classes and functions in your Python code.
```python
# Built-in modules
from multiprocessing import Process
from threading import Thread
# Third-party libraries
from parallelism import scheduled_task, task_scheduler
```
2. Define your task functions. These user-defined functions will be executed in parallel and concurrently.
```python
def func(*args, **kwargs):
if not args and not kwargs:
raise ValueError('Missing *args or **kwargs')
return args, kwargs
```
3. Create task instances using the `scheduled_task` function, specifying the execution unit (Process or Thread), task name, function, and provide any required positional or keyword arguments.
```python
task1 = scheduled_task(Process, 'task1', func, args=(1, 2, 3), continual=True)
task2 = scheduled_task(Process, 'task2', func, kwargs={'a': 10, 'b': 20}, continual=True)
task3 = scheduled_task(Thread, 'task3', func, continual=True)
```
4. Schedule tasks using the `task_scheduler` function, Specify the tasks to be executed along with the desired number of processes and threads.
```python
result = task_scheduler(tasks=(task1, task2, task3), processes=2, threads=4)
```
5. Access task execution details and results through the result object, providing insights into execution times, elapsed times, exceptions, and return values:
```python
>>> result.execution_time
{
'task1': datetime.datetime(%Y, %m, %d, %H, %M, %S, %f),
'task2': datetime.datetime(%Y, %m, %d, %H, %M, %S, %f),
'task3': datetime.datetime(%Y, %m, %d, %H, %M, %S, %f),
}
>>> result.elapsed_time
{
'task1': float(...),
'task2': float(...),
'task3': float(...),
}
>>> result.raise_exception
{
'task3': ValueError('Missing *args or **kwargs'),
}
>>> result.return_value
{
'task1': ((1, 2, 3), {}),
'task2': ((), {'a': 10, 'b': 20}),
}
```
For more comprehensive documentation and advanced usage, please refer to the full [API Documentation](https://parallelism.readthedocs.io/en/latest/index.html).
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"description": "# parallelism\r\n\r\nUnlock advanced task scheduling in Python with `parallelism`.\r\nSeamlessly coordinate parallel and concurrent execution, optimizing performance while ensuring code integrity.\r\nEmbrace expert scheduling techniques to elevate your programming to new heights of efficiency and responsiveness.\r\n\r\n## Installation\r\n\r\nYou can install this package using pip:\r\n\r\n```bash\r\npip install parallelism\r\n```\r\n\r\n## Basic Usage\r\n\r\nExplore an effortless approach to task creation and management.\r\nFollow these steps to get started:\r\n\r\n1. Import the necessary classes and functions in your Python code.\r\n\r\n ```python\r\n # Built-in modules\r\n from multiprocessing import Process\r\n from threading import Thread\r\n\r\n # Third-party libraries\r\n from parallelism import scheduled_task, task_scheduler\r\n ```\r\n\r\n2. Define your task functions. These user-defined functions will be executed in parallel and concurrently.\r\n\r\n```python\r\ndef func(*args, **kwargs):\r\n if not args and not kwargs:\r\n raise ValueError('Missing *args or **kwargs')\r\n return args, kwargs\r\n```\r\n\r\n3. Create task instances using the `scheduled_task` function, specifying the execution unit (Process or Thread), task name, function, and provide any required positional or keyword arguments.\r\n\r\n```python\r\ntask1 = scheduled_task(Process, 'task1', func, args=(1, 2, 3), continual=True)\r\ntask2 = scheduled_task(Process, 'task2', func, kwargs={'a': 10, 'b': 20}, continual=True)\r\ntask3 = scheduled_task(Thread, 'task3', func, continual=True)\r\n```\r\n\r\n4. Schedule tasks using the `task_scheduler` function, Specify the tasks to be executed along with the desired number of processes and threads.\r\n\r\n```python\r\nresult = task_scheduler(tasks=(task1, task2, task3), processes=2, threads=4)\r\n```\r\n\r\n5. Access task execution details and results through the result object, providing insights into execution times, elapsed times, exceptions, and return values:\r\n\r\n```python\r\n>>> result.execution_time\r\n{\r\n 'task1': datetime.datetime(%Y, %m, %d, %H, %M, %S, %f),\r\n 'task2': datetime.datetime(%Y, %m, %d, %H, %M, %S, %f),\r\n 'task3': datetime.datetime(%Y, %m, %d, %H, %M, %S, %f),\r\n}\r\n>>> result.elapsed_time\r\n{\r\n 'task1': float(...),\r\n 'task2': float(...),\r\n 'task3': float(...),\r\n}\r\n>>> result.raise_exception\r\n{\r\n 'task3': ValueError('Missing *args or **kwargs'),\r\n}\r\n>>> result.return_value\r\n{\r\n 'task1': ((1, 2, 3), {}),\r\n 'task2': ((), {'a': 10, 'b': 20}),\r\n}\r\n```\r\n\r\nFor more comprehensive documentation and advanced usage, please refer to the full [API Documentation](https://parallelism.readthedocs.io/en/latest/index.html).\r\n",
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