Metadata-Version: 2.4
Name: ungyoseries
Version: 26.6.13
Summary: Hyper-Performance Environment Setup for AI & Hardware Accelerating. Thank you for searching ungyoseries.
Author: 최운교
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Microsoft :: Windows
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: fasthardware>=2.2.2
Requires-Dist: numpy>=1.20.0
Requires-Dist: aiohttp>=3.8.0
Requires-Dist: requests>=2.25.0
Requires-Dist: openvino>=2023.0.0
Requires-Dist: opencv-python>=4.5.0
Dynamic: author
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: requires-dist
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Markdown
# ⚡ fasthardware (ungyoseries Acceleration Engine)

`fasthardware` is the core low-level acceleration engine of the **ungyoseries** framework. It provides hyper-performance runtime optimizations, hardware-native boosting (ULTIMATE mode), asynchronous non-blocking video streaming, and automated memory sweep mechanics designed for mission-critical AI inference pipelines.

---

## 🚀 Key Features

* **Ultimate Hardware Boosting (`speedup`):** Direct optimization of OS thread schedulers, memory allocation sub-systems, and compiler behaviors tailored for heavy mathematical computations and deep learning.
* **Zero-Latency Streaming (`FastVideoStream`):** Eliminates OpenCV blocking bottlenecks by running frame acquisition on a dedicated hardware-isolated thread using atomic GIL-safe variable swapping.
* **Automated Workspace Purging (`manual_sweep`):** Proactively triggers process working-set trimming and forces garbage collection to maintain a zero-memory-leak state during prolonged loops.
* **Seamless Integration:** Fully encapsulated within the `ungyoseries` unified architecture. Accessible seamlessly via a single master import.

---

## 🛠️ Architecture Overview

```text
ungyoseries (Master Package)
└── init() / @boost()
└── fasthardware (Core Engine)
    ├── speedup()          # OS & Thread Level Boosting
    ├── manual_sweep()     # Hardware-level Memory Trimming
    └── FastVideoStream()  # Non-blocking GIL-isolated Stream Engine
```
💻 Technical Specification & Usage
1. Initializing Ultra-Performance Mode
Invoke init(mode="ULTIMATE") at the very entry point of your application to maximize CPU/GPU thread scheduling priorities and lock down low-latency execution paths.

```Python
import ungyoseries

# Initialize Hyper-Performance Environment immediately at boot
ungyoseries.init(mode="ULTIMATE")
```
2. Zero-Lag High-Speed Streaming
Replace the standard cv2.VideoCapture blocking loop with the asynchronous hardware stream engine to achieve maximum, consistent loop FPS.

```Python
# Starts thread-isolated background frame swapper
vs = ungyoseries.fasthardware.FastVideoStream(src=0).start()

while True:
    # Instantly read the latest frame without any blocking delays
    frame = vs.read()
```
    
    # Your heavy AI inference / Processing Logic goes here...
3. Automated Memory Trimming via Decorator
Utilize the @ungyoseries.boost() decorator on your main pipeline function to manage GC states during the hot loop and execute an automatic manual_sweep() when the pipeline finishes or encounters an unhandled exception.

```Python
@ungyoseries.boost()
def main_pipeline():
    vs = ungyoseries.fasthardware.FastVideoStream(src=0).start()
    try:
        while True:
            image = vs.read()
            # Loop execution...
    finally:
        vs.stop()
```
📦 Requirements & Installation
fasthardware is automatically configured and linked as an editable module inside your python virtual environment via setuptools.

Dependencies
numpy >= 1.20.0

aiohttp >= 3.8.0

requests >= 2.25.0

openvino >= 2023.0.0

opencv-python >= 4.5.0

Local Development Setup
To register and lock the package framework to your local virtual environment:

```PowerShell
pip install -e .
```
🛡️ License
Developped and engineered exclusively by ungyo. Unauthorized distribution or commercial reuse of the core acceleration components is strictly restricted.
