multi-threading-processing

What problem does it solve?

Parallel and distributed task execution in Python can be error-prone and hard to scale; this guide helps you implement safe multi-threading and multi-processing workflows with the speedy_utils library.

Core Features & Use Cases

• Provides clear guidance on when to use threading vs processing, backends, and batching strategies.
• Demonstrates practical patterns for IO-bound tasks, CPU-bound computations, and data processing workflows.
• Includes best practices for error handling, progress tracking, and resource cleanup.

Quick Start

Run a simple parallel task using multi_thread or multi_process to process a list of items in batches.