TensorFlow Evolution 🧠

Progressive journey through TensorFlow implementations, from foundational concepts to advanced architectures. Features custom training pipelines, optimized GPU implementations, and production-ready models.

Features • Installation • Quick Start • Documentation • Contributing

📑 Table of Contents

• Features
• Project Structure
• Prerequisites
• Installation
• Quick Start
• Documentation
  • Models
  • GPU Optimization
  • Benchmarks
• Contributing
• Versioning
• Authors
• Citation
• License
• Acknowledgments

✨ Features

• Custom training loops and optimizations
• GPU-accelerated model training
• Production deployment patterns
• CUDA optimizations for large-scale training
• Comprehensive documentation and examples

📁 Project Structure

graph TD
    A[tensorflow-evolution] --> B[notebooks]
    A --> C[src]
    A --> D[tests]
    A --> E[docs]
    A --> F[apps]
    C --> G[models]
    C --> H[data]
    C --> I[utils]
    C --> J[cuda]
    G --> K[basic]
    G --> L[advanced]
    J --> M[kernels]
    J --> N[optimizations]

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Click to expand full directory structure

tensorflow-evolution/
├── notebooks/          # Jupyter notebooks for experiments
│   ├── basic/         # Foundational concepts
│   └── advanced/      # Advanced implementations
├── src/               # Source code
│   ├── models/        # Model implementations
│   ├── data/         # Data processing utilities
│   ├── utils/        # Helper functions
│   └── cuda/         # CUDA optimizations
├── tests/             # Unit tests
├── docs/              # Documentation
├── apps/              # Demo applications
└── README.md          # Main documentation

🔧 Prerequisites

• Python 3.8+
• CUDA 11.8+ (for GPU support)
• NVIDIA GPU with compute capability 6.0+

📦 Installation

# Clone the repository
git clone https://github.com/BjornMelin/tensorflow-evolution.git
cd tensorflow-evolution

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
.\venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

🚀 Quick Start

from src.models import BasicCNN
from src.utils import DataLoader

# Initialize model
model = BasicCNN()

# Train model
model.train(data_loader, epochs=10)

📚 Documentation

Models

Model	Description	Performance	CUDA Support
BasicCNN	Foundational CNN architecture	94% accuracy on MNIST	✓
TransformerBlock	Custom transformer implementation	BERT-comparable	✓
AdvancedGAN	Generative adversarial network	FID: 18.3	✓

GPU Optimization

Comprehensive CUDA optimizations including:

• Custom compute kernels
• Memory-efficient training
• Multi-GPU data parallelism
• Gradient accumulation

Benchmarks

Performance metrics across different hardware configurations:

Model Size	GPU Memory	Training Time	Inference Time
Small (10M)	4GB	2.3 hrs	5ms
Medium (50M)	8GB	5.7 hrs	15ms
Large (100M)	16GB	12.4 hrs	35ms

🤝 Contributing

We welcome contributions! Please see our:

• Contributing Guidelines
• Code of Conduct
• Development Guide

📌 Versioning

We use SemVer for versioning. For available versions, see the tags on this repository.

✍️ Authors

Bjorn Melin

• GitHub: @BjornMelin
• LinkedIn: Bjorn Melin

📝 Citation

@misc{melin2024tensorflowevolution,
  author = {Melin, Bjorn},
  title = {TensorFlow Evolution: Progressive Deep Learning Implementations},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/BjornMelin/tensorflow-evolution}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• TensorFlow team for their excellent documentation
• NVIDIA for CUDA toolkit and documentation
• Open source community for valuable feedback

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Made with 🧠 and ❤️ by Bjorn Melin