Maximize the absolute computing power of your Python process with a single line of code.
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fasthardware
Maximize the absolute computing power of your Python process with just a single line of code.
fasthardware is a lightweight, zero-configuration hardware acceleration injector designed for high-performance, real-time Python applications (e.g., YOLO object detection, MediaPipe pose estimation, OpenCV video processing pipelines, and AI inference loops).
By hijacking the OS scheduler, optimizing runtime memory management, and forcing C-level multi-threading, fasthardware eliminates micro-stuttering and boosts real-time processing speed (FPS) by up to 40%.
Key Features
- OS Priority Escalation: Automatically forces the host OS (Windows/Linux) to allocate maximum CPU scheduling priority to your Python process.
- Micro-Stuttering Elimination: Optimizes Python's Garbage Collection (GC) thresholds to prevent the dreaded "Stop-the-World" latency spikes during heavy loops.
- C-Level Multicore Mobilization: Injects global environment flags (
OMP,MKL,OPENBLAS,NUMEXPR) to force underlying C/C++ backed libraries (NumPy, OpenCV) to utilize every single logical core available. - Zero-Config Integration: No code rewrites. Just import it at the very top of your script, and you are good to go.
Installation
Install the package directly from PyPI:
pip install fasthardware
️ Quick Start Simply import fasthardware and ignite the supercharger engine before your main execution loop.
# 1. Import the engine at the absolute top of your script
from fasthardware import fasthardware
# 2. Unlock the full potential of your hardware
fasthardware.speedup()
import cv2
import numpy as np
# Your heavy real-time loop (YOLO, MediaPipe, OpenCV, etc.)
while True:
# Experience smoother, higher FPS performance!
pass
Manual Memory Sweeping (Optional)
For ultra-heavy asynchronous pipelines (e.g., blending AI inference with heavy API requests or Text-to-Speech), you can manually sweep the 0-generation memory cache without breaking your frame rate:
License This project is licensed under the MIT License - see the LICENSE file for details.
Developed with by Choi Woongyo
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