https://github.com/bjornmelin/ai-system-design
🎨 Large-scale AI system architectures and implementations. Features distributed training systems, multi-GPU pipelines, and efficient resource management. 🏗️
https://github.com/bjornmelin/ai-system-design
architecture cuda distributed-systems engineering gpu-computing production scalability system-design
Last synced: 3 months ago
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🎨 Large-scale AI system architectures and implementations. Features distributed training systems, multi-GPU pipelines, and efficient resource management. 🏗️
- Host: GitHub
- URL: https://github.com/bjornmelin/ai-system-design
- Owner: BjornMelin
- License: mit
- Created: 2025-01-24T14:52:09.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-24T14:59:22.000Z (9 months ago)
- Last Synced: 2025-03-28T04:18:40.045Z (6 months ago)
- Topics: architecture, cuda, distributed-systems, engineering, gpu-computing, production, scalability, system-design
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AI System Design 🎨
[](https://www.python.org/downloads/)
[](https://kubernetes.io/)
[](https://developer.nvidia.com/cuda-toolkit)
[](https://www.docker.com/)
[](LICENSE)
> Large-scale AI system architectures and implementations. Features distributed training systems, multi-GPU pipelines, and efficient resource management.
[Features](#features) • [Installation](#installation) • [Quick Start](#quick-start) • [Documentation](#documentation) • [Contributing](#contributing)
## 📑 Table of Contents
- [Features](#features)
- [Project Structure](#project-structure)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Documentation](#documentation)
- [System Components](#system-components)
- [Performance](#performance)
- [Case Studies](#case-studies)
- [Contributing](#contributing)
- [Versioning](#versioning)
- [Authors](#authors)
- [Citation](#citation)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## ✨ Features
- Scalable AI system architectures
- Multi-GPU training pipelines
- Distributed inference systems
- Resource optimization
- Production case studies
## 📁 Project Structure
```mermaid
graph TD
A[ai-system-design] --> B[architectures]
A --> C[implementation]
A --> D[case-studies]
A --> E[deployment]
B --> F[distributed]
B --> G[scalable]
C --> H[training]
C --> I[inference]
D --> J[recommender]
D --> K[search]
E --> L[kubernetes]
E --> M[monitoring]
```
Click to expand full directory structure
```plaintext
ai-system-design/
├── architectures/ # System architecture designs
│ ├── distributed/ # Distributed systems
│ └── scalable/ # Scalability patterns
├── implementation/ # Reference implementations
│ ├── training/ # Training systems
│ └── inference/ # Inference systems
├── case-studies/ # Real-world examples
├── deployment/ # Deployment configs
├── tests/ # Unit tests
└── README.md # Documentation
```
## 🔧 Prerequisites
- Python 3.8+
- CUDA 11.8+
- Docker 24.0+
- Kubernetes 1.24+
- NVIDIA GPU (Compute Capability 6.0+)
## 📦 Installation
```bash
# Clone repository
git clone https://github.com/BjornMelin/ai-system-design.git
cd ai-system-design
# Create environment
python -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
```
## 🚀 Quick Start
```python
from ai_system import architecture, deployment
# Initialize distributed system
system = architecture.DistributedAISystem(
training_nodes=4,
inference_nodes=8,
gpu_per_node=4
)
# Deploy system
deployment = deployment.KubernetesDeployment(
system=system,
monitoring=True,
autoscaling=True
)
# Launch services
deployment.deploy()
```
## 📚 Documentation
### System Components
| Component | Purpose | Scale | Complexity |
|-----------|---------|-------|------------|
| Training Pipeline | Distributed Training | Very High | High |
| Inference System | Serving Models | High | Medium |
| Resource Manager | GPU Allocation | High | Medium |
### Performance
| Metric | Target | Achieved | Scale |
|--------|--------|----------|-------|
| Training Throughput | 10k samples/sec | 12k samples/sec | 32 GPUs |
| Inference Latency | 50ms | 35ms | 1M QPS |
| Resource Utilization | 85% | 88% | 100 nodes |
### Case Studies
| System | Scale | Challenge | Solution |
|--------|-------|-----------|----------|
| Recommender | 1B users | Real-time updates | Distributed KV store |
| Search Engine | 10M QPS | Low latency | GPU acceleration |
| LLM Training | 1T params | Memory efficiency | Model parallelism |
## 🤝 Contributing
- [Contributing Guidelines](CONTRIBUTING.md)
- [Code of Conduct](CODE_OF_CONDUCT.md)
- [Development Guide](DEVELOPMENT.md)
## 📌 Versioning
We use [SemVer](http://semver.org/) for versioning. For available versions, see the [tags on this repository](https://github.com/BjornMelin/ai-system-design/tags).
## ✍️ Authors
**Bjorn Melin**
- GitHub: [@BjornMelin](https://github.com/BjornMelin)
- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)
## 📝 Citation
```bibtex
@misc{melin2024aisystemdesign,
author = {Melin, Bjorn},
title = {AI System Design: Large-Scale Machine Learning Architectures},
year = {2024},
publisher = {GitHub},
url = {https://github.com/BjornMelin/ai-system-design}
}
```
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- Cloud providers for infrastructure insights
- Open source distributed systems community
- ML infrastructure teams sharing knowledge
---
Made with 🎨 and ❤️ by Bjorn Melin