Metadata-Version: 2.4
Name: vaos-engine
Version: 0.1.0
Summary: Virtual Adaptive Offloading System (vAOS) for Extreme-Scale Deep Learning on Constrained GPUs
Home-page: https://github.com/yourusername/vaos-engine
Author: Your Name
Author-email: your.email@college.edu
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: torch>=2.0.0
Requires-Dist: numpy
Requires-Dist: psutil
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: seaborn
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Virtual Adaptive Offloading System (vAOS-Engine)

The **vAOS-Engine** is a lightweight, PyTorch-native runtime controller designed to break the GPU Memory Wall on consumer-grade hardware (e.g., 6GB VRAM laptops).

By virtualizing host CPU RAM as an OS-bypass `mmap` pool, dynamically scheduling asynchronous PCIe transfers, and utilizing late-stage register-level INT4 dequantization, vAOS allows you to train Massive Transformer models that natively trigger Out-Of-Memory (OOM) crashes.

## Quick Start
```python
import torch
from src.api import evaluate_system
from my_models import HeavyTransformer

model = HeavyTransformer()
evaluate_system(model, dummy_inputs, dummy_targets, criterion)
