Installation

Windows (Recommended)

The setup script handles everything automatically — Python, virtual environment, and all dependencies.

git clone https://github.com/chainchopper/NPU-STACK.git
cd NPU-STACK
setup.bat

This will:

• Download portable Python 3.11 if no compatible Python is found
• Create an isolated .venv virtual environment
• Install all backend + frontend dependencies
• Generate a .env configuration file
• Create launcher scripts: run-backend.bat, run-frontend.bat, run-all.bat

Manual Setup

pip install -r backend/requirements.txt
cd frontend && npm install
cp .env.example .env

Quick Start

After installation, launch both backend and frontend:

run-all.bat

This starts:

• Backend — FastAPI on http://localhost:8000
• Frontend — Vite dev server on http://localhost:5173
• OpenAI API — Available at http://localhost:8000/v1
• Swagger Docs — Interactive at http://localhost:8000/api/docs

Configuration

Edit .env in the project root to customize settings:

Model Serving

NPU-STACK includes an OpenAI-compatible API server, acting as a local alternative to LM Studio. Any tool or SDK that works with the OpenAI API works with NPU-STACK.

Supported Endpoints

Usage

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="any")
response = client.chat.completions.create(
    model="my-model",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

Models are auto-loaded on first request. You can also pre-load models via the /v1/models/load endpoint or the Serving page in the UI.

Compatibility

Tested and compatible with: OpenAI Python/JS SDK, LangChain, LlamaIndex, Open WebUI, Chatbot UI, Vercel AI SDK, cURL, Postman.

Playground

The Playground provides an interactive UI for testing models without code:

• Image Classification — Upload an image, get top-K predictions with confidence scores
• Object Detection — YOLO/SSD-style bounding box detection
• Text Generation — Prompt a language model and see streamed output
• Image Generation — Text-to-image with Stable Diffusion (ONNX)

HuggingFace Hub

Browse, search, and download models directly from HuggingFace:

• Search by query with task filtering (text-generation, image-classification, etc.)
• View model details — downloads, likes, tags, file list
• One-click download — auto-detects model format (ONNX, PyTorch, safetensors)
• Downloaded models are automatically registered in the local model registry

Set HUGGINGFACE_TOKEN in .env to access gated/private models.

Training

Train models from scratch with built-in architectures:

• Select architecture (ResNet, MobileNet, custom) and dataset
• Configure hyperparameters: epochs, batch size, learning rate, optimizer
• Real-time metrics via WebSocket — loss, accuracy, learning rate curves
• Trained models are saved to the model registry

Fine-Tuning

Parameter-efficient fine-tuning using LoRA and QLoRA:

• Select a base model from the registry + a dataset from the Dataset Manager
• Configure LoRA parameters: rank (r), alpha, dropout, target modules
• Background training with real-time step/epoch/loss tracking
• Fine-tuned adapters are saved alongside the base model
• Requires: pip install peft datasets

Conversion & Quantization

Convert models between formats and apply quantization:

• PyTorch → ONNX — Export with configurable input shapes
• ONNX → OpenVINO IR — Optimized for Intel hardware
• INT8 / INT4 Quantization — Via NNCF for smaller, faster models
• Converted models are registered with original as parent

Benchmark

Benchmark inference performance across hardware:

• Measure latency (p50, p95, p99) and throughput (inferences/sec)
• Compare CPU vs GPU vs NPU side-by-side
• Compare quantization levels (FP32 vs INT8 vs INT4)
• System info: detailed hardware detection with capabilities

Dataset Manager

Manage training and evaluation datasets:

• Upload — Drag and drop files; zip archives are auto-extracted
• Scan — Point to a local datasets/ folder and auto-detect contents
• Auto-detect — Image datasets, CSVs, JSON/JSONL, Parquet, plain text
• Delete — Remove local datasets from the UI

Hardware Support

NPU-STACK auto-detects and supports all major AI accelerators:

Docker

Run with Docker Compose for a containerized deployment:

docker compose up --build

Services:

• backend — FastAPI + all ML dependencies on port 8000
• frontend — Nginx-served React build on port 3000

Contributing

We welcome contributions! Here's the workflow:

git clone https://github.com/chainchopper/NPU-STACK.git
cd NPU-STACK
git checkout dev
# make your changes
git push origin dev
# then open a Pull Request on GitHub

All PRs should target the dev branch. See the dev branch for the latest development code.