Deep Learning Evolution 🚀

Deep-Learning Evolution combines both TensorFlow and PyTorch projects into a single, cohesive repository. From foundational neural networks to custom CUDA kernels and distributed training systems, you’ll find memory‑efficient training techniques, advanced GPU optimizations, and production‑ready deployments for real‑world AI applications.

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📑 Table of Contents

• ✨ Features
• 📁 Project Structure
• 🔧 Prerequisites
• 📦 Installation
• 🚀 Quick Start
  • TensorFlow Examples
  • PyTorch Examples
• 📚 Documentation
  • Models
  • GPU Optimization
  • Benchmarks
• 🤝 Contributing
• 📌 Versioning
• ✍️ Author
• 📝 Citation
• 📄 License
• 🙏 Acknowledgments

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✨ Features

1. Comprehensive Framework Coverage

   • TensorFlow 2.14+ and PyTorch 2.2+ examples under one roof.

2. Custom Training Pipelines

   • Efficient data loading, augmentation, and custom training loops.

3. Advanced GPU & CUDA Optimizations

   • Memory‑efficient backprop, kernel fusion, and multi‑GPU data parallelism.

4. Distributed Training

   • PyTorch’s DistributedTrainer and TensorFlow’s MirroredStrategy for large‑scale HPC setups.

5. Production‑Ready Models

   • Demonstrations of how to package and deploy models with Docker, Streamlit, or other tools.

6. Comprehensive Documentation & Tests

   • Detailed notebooks, examples, and unit tests in both frameworks.

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📁 Project Structure

Depending on your final layout, you might organize your repo as follows:

graph TD
    A[deep-learning-evolution] --> B[tensorflow]
    A --> C[pytorch]
    A --> D[common]
    A --> E[benchmarks]
    A --> F[tests]
    B --> G[notebooks]
    B --> H[src]
    B --> I[apps]
    C --> J[examples]
    C --> K[src]
    C --> L[extensions]
    D --> M[utils]
    D --> N[datasets]
    D --> O[docs]

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Click to expand possible directory structure

deep-learning-evolution/
├── tensorflow/
│   ├── notebooks/           # Jupyter notebooks for TF experiments
│   ├── src/                 # TF source code (models, data, utils, cuda)
│   ├── apps/                # Demo or production apps (e.g., Streamlit)
│   └── ...
├── pytorch/
│   ├── examples/            # PyTorch example scripts
│   ├── src/                 # PyTorch source (models, training utils)
│   ├── extensions/          # Custom CUDA kernels/extensions
│   └── ...
├── common/
│   ├── utils/               # Shared utility scripts
│   ├── datasets/            # Shared data loaders or references
│   └── docs/                # Additional documentation
├── benchmarks/              # Performance comparisons across frameworks
├── tests/                   # Unit tests for TF and PT code
├── requirements.txt         # Dependencies
└── README.md                # Main documentation

Tip: You can keep separate requirements-tf.txt and requirements-pt.txt if you prefer splitting dependencies.

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🔧 Prerequisites

• Python 3.8+
• CUDA 11.8+
• TensorFlow 2.14+
• PyTorch 2.2+
• NVIDIA GPU with compute capability 6.0+
• (Optional) C++ compiler for building custom CUDA extensions (e.g., PyTorch’s extension modules)

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📦 Installation

1. Clone this repository:

   git clone https://github.com/BjornMelin/deep-learning-evolution.git
   cd deep-learning-evolution

2. Set up a virtual environment:

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

3. Install dependencies:

   # Single unified requirements:
   pip install -r requirements.txt
   
   # OR separate for each framework:
   pip install -r requirements-tf.txt
   pip install -r requirements-pt.txt

4. (Optional) Build custom CUDA extensions (for PyTorch examples):

   cd pytorch/extensions/cuda
   python setup.py install

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🚀 Quick Start

TensorFlow Examples

# TensorFlow: Basic CNN Example
from tensorflow.src.models import BasicCNN
from tensorflow.src.utils import DataLoader

data_loader = DataLoader('path/to/data')
model = BasicCNN()

model.train(data_loader, epochs=10)
model.evaluate(...)
model.save('saved_model')

PyTorch Examples

# PyTorch: Custom Transformer Example
from pytorch.src import models, training

model = models.TransformerWithCustomAttention(num_heads=8, d_model=512)
trainer = training.DistributedTrainer(model, gpu_list=[0,1])
trainer.train(dataset, epochs=10)

For more demos, see the notebooks in tensorflow/ or the examples folder in pytorch/.

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📚 Documentation

Models

Model	Framework	Description	Performance	Memory Efficiency
BasicCNN	TensorFlow	Foundational CNN architecture	94% on MNIST	High
TransformerBlock	TensorFlow	Custom transformer for NLP	BERT-comparable	Medium
AdvancedGAN	TensorFlow	Generative adversarial network	FID: 18.3	High
TransformerWithCustomAttention	PyTorch	Advanced Transformer w/ fused attention	95% on GLUE	High
ViT	PyTorch	Vision transformer with optimized attention	92% on ImageNet	Medium
CustomGAN	PyTorch	Memory‑efficient adversarial network	FID: 12.4	High

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GPU Optimization

• Custom CUDA kernels for specialized operations
• Memory‑efficient training (gradient checkpointing, fused ops)
• Multi‑GPU data parallelism (PyTorch DDP, TF MirroredStrategy)
• Distributed training for large‑scale HPC
• Kernel fusion techniques to minimize overhead

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Benchmarks

Model	Hardware	Framework	Training Time	Memory Usage	Throughput
Custom BERT	A100	PyTorch	4.2 hrs	16GB	1250 samples/sec
ViT	V100	PyTorch	6.8 hrs	24GB	850 samples/sec
ResNet-152	3090	PyTorch	2.5 hrs	12GB	2100 samples/sec
BasicCNN (small)	4GB GPU	TensorFlow	2.3 hrs	~2GB	900 samples/sec
TransformerBlock	8GB GPU	TensorFlow	5.7 hrs	~6GB	600 samples/sec

(Exact numbers are illustrative; refer to each framework’s benchmarks folder for detailed logs.)

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🤝 Contributing

We welcome all contributions, from bug fixes to new model architectures and GPU kernels.

• Contributing Guidelines
• Code of Conduct
• Development Guide

1. Fork this repository
2. Create a new branch
3. Commit your changes
4. Open a Pull Request

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📌 Versioning

We use SemVer for versioning. For available versions, check the tags.

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✍️ Author

Bjorn Melin

• GitHub: @BjornMelin
• LinkedIn: Bjorn Melin

Feel free to add additional contributors or maintainers here.

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📝 Citation

If you find this project useful, please cite:

@misc{melin2024deeplearningevolution,
  author = {Melin, Bjorn},
  title = {Deep Learning Evolution: Unified TensorFlow & PyTorch Implementations},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/BjornMelin/deep-learning-evolution}
}

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📄 License

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

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🙏 Acknowledgments

• TensorFlow team for extensive documentation and tooling.
• PyTorch contributors for ongoing innovation.
• NVIDIA for the CUDA toolkit.
• Open Source Community for valuable feedback and inspiration.

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Made with 🧠 and ⚡ by Bjorn Melin