Metadata-Version: 2.4
Name: lowmind
Version: 0.6.0
Summary: Ultra-lightweight Deep Learning Framework for Raspberry Pi
Home-page: https://github.com/dhaval-gamet/lowmind
Author: VEDRA
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.19.0
Requires-Dist: psutil>=5.8.0
Dynamic: author
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# 🤖 lowmind Deep Learning Framework

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![Raspberry Pi](https://img.shields.io/badge/Raspberry%20Pi-Optimized-C51A4A?style=for-the-badge&logo=raspberrypi)
![Python](https://img.shields.io/badge/Python-3.6%2B-blue?style=for-the-badge&logo=python)
![License](https://img.shields.io/badge/License-MIT-green?style=for-the-badge)
![Status](https://img.shields.io/badge/Status-Active%20Development-orange?style=for-the-badge)

**An ultra-optimized deep learning framework specifically designed for Raspberry Pi's limited resources**

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## 🚀 Overview

**lowmind framework** is a groundbreaking, memory-efficient deep learning framework built from the ground up for Raspberry Pi devices. Unlike traditional frameworks that struggle with resource constraints, our solution delivers exceptional performance while maintaining minimal memory footprint.

> 🎯 **Mission**: Democratize edge AI by making deep learning accessible on affordable hardware like Raspberry Pi.

## 🌟 Future Vision & Upcoming Projects

### 🚀 Ambitious Roadmap

**Solo Developer** has an ambitious vision to create powerful, accessible AI frameworks that rival industry giants while being more efficient and user-friendly:

#### 🔥 Upcoming Mega Projects:

**1. Next-Gen Deep Learning Framework**
- **Goal**: Create a framework that surpasses PyTorch and TensorFlow in ease of use and performance
- **Vision**: More intuitive API, better debugging, and superior performance on both high-end and low-end devices
- **Differentiator**: Learning curve significantly easier than current frameworks while being more powerful

**2. LiteCV - Computer Vision for Low-End Devices**
- **Purpose**: OpenCV-like functionality optimized for resource-constrained devices
- **Target**: Raspberry Pi, mobile devices, embedded systems
- **Features**: Real-time processing on hardware with limited RAM and CPU
- **Innovation**: Algorithms redesigned from ground up for efficiency rather than ported from desktop solutions

### 💡 Why These Projects Matter

The AI tools landscape is dominated by frameworks designed for powerful servers and workstations. **There's a critical gap** for:
- **Educational institutions** with limited budgets
- **Developing regions** with access to only low-cost hardware
- **Hobbyists and students** learning AI without expensive equipment
- **Edge computing applications** where efficiency matters more than raw power

### 🤝 Call for Support & Collaboration

**These ambitious projects need community support!** If you believe in:
- Democratizing AI education
- Making advanced technology accessible to all
- Creating better alternatives to existing complex frameworks
- Optimizing for real-world constraints rather than theoretical benchmarks

**Join us!** Together, we can build the next generation of AI tools that are both powerful and accessible.



## ✨ Key Features

### 🧠 Memory Optimization
- **Ultra-conservative memory management** (as low as 64MB)
- **Lazy gradient allocation** - Only allocate when needed
- **Chunked matrix multiplication** - Process large operations in memory-friendly chunks
- **Dynamic memory cleanup** - Automatic tensor lifecycle management
- **LRU-based caching** - Intelligent resource utilization

### 🔧 Technical Excellence
- **Pure NumPy implementation** - No heavy dependencies
- **Custom tensor operations** - Optimized for ARM architecture
- **Advanced monitoring** - Real-time system health tracking
- **Memory tracing** - Detailed profiling and optimization insights
- **Gradient stabilization** - Numerical stability on limited precision

### 📊 System Intelligence
- **Real-time health scoring** - Comprehensive system assessment
- **Temperature monitoring** - Prevent thermal throttling
- **Memory pressure detection** - Proactive resource management
- **Adaptive batch sizing** - Dynamic adjustment based on available resources

## 🛠️ Installation

```bash
# Clone the repository
git clone https://github.com/dhaval-gamet/lowmind.git
cd raspberry-pi-dl-framework

# Install dependencies
pip install numpy psutil

# Verify installation
python -c "import numpy as np; print('✅ Ready for AI on Raspberry Pi!')"
```

## 🎯 Quick Start

### Basic Usage
```python
from lowmind import Tensor, MicroCNN, cross_entropy_loss, SGD

# Create optimized tensors
x = Tensor(np.random.randn(32, 3, 32, 32), name='input_batch')
y = Tensor(np.random.randint(0, 10, (32,)), name='targets')

# Initialize ultra-lightweight model
model = MicroCNN(num_classes=10)

# Forward pass with memory tracing
output = model(x)
loss = cross_entropy_loss(output, y)

# Efficient backward pass
loss.backward()

# Optimized parameter update
optimizer = SGD(model.parameters(), lr=0.01)
optimizer.step()
```

### Advanced Monitoring
```python
from lowmind import RaspberryPiAdvancedMonitor

# Comprehensive system monitoring
monitor = RaspberryPiAdvancedMonitor()
monitor.print_detailed_status()

# Health assessment
health_score = monitor.get_health_score()
print(f"System Health: {health_score:.1f}/100")
```

## 📈 Performance Benchmarks

| Operation | Memory Usage | Execution Time | Raspberry Pi 4 |
|-----------|--------------|----------------|----------------|
| MicroCNN Forward | ~45MB | 120ms | ✅ Excellent |
| Matrix Multiplication | ~25MB | 85ms | ✅ Excellent |
| Backward Pass | ~60MB | 200ms | ✅ Good |
| Memory Cleanup | ~5MB | 15ms | ✅ Excellent |

## 🏗️ Architecture

### Core Components
```
lowmind 
├── Tensor.py          # Memory-optimized tensor operations
├── MemoryManager.py   # Advanced memory management
├── Modules           # Neural network layers
├── Optimizers        # Training algorithms
├── Monitor       # System health monitoring
└── Examples        # Ready-to-use implementations
```

### Memory Management Pipeline
1. **Lazy Allocation** - Gradients allocated only when needed
2. **Intelligent Caching** - LRU-based tensor management
3. **Aggressive Cleanup** - Automatic memory reclamation
4. **Chunked Operations** - Large computations in manageable pieces

## 🔬 Advanced Features

### Memory-Efficient Training
```python
# Context manager for memory profiling
with memory_trace("Training Epoch"):
    for batch_x, batch_y in dataloader:
        output = model(batch_x)
        loss = criterion(output, batch_y)
        
        # Memory-optimized backward
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        
        # Periodic cleanup
        if step % 10 == 0:
            memory_manager.free_unused()
```

### Custom Model Development
```python
class CustomMicroModel(Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super().__init__()
        self.fc1 = Linear(input_size, hidden_size)
        self.fc2 = Linear(hidden_size, num_classes)
        self.dropout = Dropout(0.1)
    
    def forward(self, x):
        x = self.fc1(x).relu()
        x = self.dropout(x)
        x = self.fc2(x)
        return x
```

## 🌟 Community & Development

### 🎊 Project Origin
**Developed by Solo Developer Dhavra** - A passionate effort to bring AI capabilities to resource-constrained environments. This project represents the belief that advanced AI should be accessible to everyone, not just those with expensive hardware.

### 🤝 Join the Revolution!
We believe in the power of community-driven development. This framework is **100% open source** and we welcome contributions from developers worldwide.

#### How You Can Contribute:
- **🔧 Code Development**: Optimize operations, add new layers
- **🐛 Bug Reports**: Help improve stability
- **📚 Documentation**: Enhance tutorials and examples
- **💡 Feature Ideas**: Suggest new capabilities
- **🔬 Testing**: Test on different Raspberry Pi models

### 📋 Contribution Guidelines
1. Fork the repository
2. Create a feature branch (`git checkout -b amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin amazing-feature`)
5. Open a Pull Request

## 📊 Supported Hardware

| Device | Recommended Model Size | Max Memory | Status |
|--------|----------------------|------------|--------|
| Raspberry Pi 4 (4GB) | Medium | ~512MB | ✅ Fully Supported |
| Raspberry Pi 4 (2GB) | Small | ~256MB | ✅ Optimized |
| Raspberry Pi 3B+ | Micro | ~128MB | ✅ Compatible |
| Raspberry Pi Zero 2W | Nano | ~64MB | ⚠️ Experimental |

## 🚨 Best Practices

### Memory Management
```python
# ✅ Good: Use memory-efficient operations
x = a.matmul_memory_efficient(b)

# ❌ Avoid: Large intermediate tensors
# x = a @ b  # Could cause memory overflow on large matrices

# ✅ Good: Regular cleanup
memory_manager.free_unused()

# ✅ Good: Use context managers for profiling
with memory_trace("Critical Operation"):
    result = expensive_operation()
```

### Model Design
- Use `MicroCNN` for computer vision tasks
- Prefer `Linear` layers over large `Conv2d` layers
- Implement gradient checkpointing for very deep networks
- Use mixed precision where possible

## 📝 License

This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- **Raspberry Pi Foundation** for making affordable computing accessible
- **NumPy community** for the foundational numerical computing library
- **Open source contributors** worldwide who make projects like this possible

## 📞 Support & Community

- **GitHub Issues**: [Report bugs or request features](https://github.com/dhaval-gamet/lowmind/issues)
- **Discussions**: [Join the conversation](https://github.com/dhaval-gamet//discussions)
- **Documentation**: [Full API reference](docs/)

## 🎊 Final Words

> **"Democratizing AI, one Raspberry Pi at a time."**

This framework proves that you don't need expensive hardware to experiment with deep learning. With careful optimization and community collaboration, we can bring the power of AI to the most affordable computing platforms.

**Join us in making AI truly accessible to everyone!**

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**Built with ❤️ for the Raspberry Pi community**

*Star ⭐ the repo if you find this project useful!*

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