🔥 PyTorch Teaching - Professional CLI Learning Tool 🚀

Master Deep Learning from Basics to Production ✨

🎯 NEW in v2.0: Complete CLI Rewrite with ExecutorTorch & 24 Lessons!

🏆 Code Quality & Testing

What's New • Features • Installation • Usage • Curriculum • Contributing

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🎯 What's New in v2.0

🚀 Complete Rewrite: Transformed from Jupyter notebooks to a professional CLI tool!

• ✅ Modern CLI Interface: Interactive command-line tool with typer and rich
• ✅ Professional Structure: src layout + pyproject.toml + hatch + pre-commit hooks
• ✅ 24 Comprehensive Lessons: ExecutorTorch, Quantization, Distributed Training & more
• ✅ Production-Ready: Real-world patterns, best practices, testing
• ✅ Zero Notebooks: Pure Python for better collaboration and version control

Quick Start

# Install
pip install -e .

# Run a lesson
pytorch-teach run 1      # Tensor Fundamentals
pytorch-teach run 21     # ExecutorTorch (🔥 Mobile AI!)

# List all lessons
pytorch-teach list-lessons

# Health check
pytorch-teach doctor

# Show system info
pytorch-teach info

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🌟 Features

🎯 Professional CLI Tool 🖥️ Interactive command-line interface 🎨 Beautiful Rich formatting ⚡ Fast and responsive 📊 Real-time diagnostics

🚀 Modern Development 🔬 PyTorch 2.x features 🧠 Production patterns 🏆 Industry best practices 📦 Easy pip/hatch install

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📚 Curriculum

24 Comprehensive Lessons - From Basics to Production

Run any lesson with: pytorch-teach run <lesson_number>

📖 Foundation (Lessons 1-7)

• ✅ Lesson 1: Tensor Fundamentals - pytorch-teach run 1
• ✅ Lesson 2: Mathematical Operations - pytorch-teach run 2
• ✅ Lesson 3: Device Management (CPU/CUDA/MPS) - pytorch-teach run 3
• 🚧 Lesson 4: Autograd & Automatic Differentiation
• 🚧 Lesson 5: Neural Networks with nn.Module
• 🚧 Lesson 6: DataLoaders & Efficient Data Pipelines
• 🚧 Lesson 7: Training Loops & Optimization

⚡ Performance Optimization (Lessons 8-10)

• 🚧 Lesson 8: Automatic Mixed Precision (AMP)
• 🚧 Lesson 9: torch.compile & Model Compilation
• 🚧 Lesson 10: Profiling & Performance Analysis

🌐 Distributed Training (Lessons 11-13)

• 🚧 Lesson 11: DistributedDataParallel (DDP)
• 🚧 Lesson 12: Fully Sharded Data Parallel (FSDP)
• 🚧 Lesson 13: Advanced Distributed Strategies

🔧 Model Optimization (Lessons 14-16)

• 🚧 Lesson 14: Quantization (INT8/INT4)
• 🚧 Lesson 15: Model Pruning & Sparsity
• 🚧 Lesson 16: Knowledge Distillation

🏗️ Modern Architectures (Lessons 17-19)

• 🚧 Lesson 17: Transformer Architectures from Scratch
• 🚧 Lesson 18: CNNs Best Practices
• 🚧 Lesson 19: RNNs & Sequence Modeling

🚀 Production Deployment (Lessons 20-22)

• 🚧 Lesson 20: Model Export & Deployment Strategies
• ✅ Lesson 21: ExecutorTorch - Mobile & Edge AI 🔥 - pytorch-teach run 21
• 🚧 Lesson 22: Custom Operators & C++ Extensions

🎯 Advanced Topics (Lessons 23-24)

• 🚧 Lesson 23: Memory Optimization Techniques
• 🚧 Lesson 24: Production Best Practices & Patterns

Legend: ✅ Available Now | 🚧 Coming Soon

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🎮 Usage

CLI Commands

# Show help
pytorch-teach --help
ptt --help  # Short alias

# Display system info
pytorch-teach info

# List all lessons
pytorch-teach list-lessons

# Run lessons
pytorch-teach run 1   # Tensor Fundamentals
pytorch-teach run 2   # Math Operations
pytorch-teach run 3   # Device Management
pytorch-teach run 21  # ExecutorTorch 🔥

# Run in batch mode (non-interactive)
pytorch-teach run 1 --batch

# Run with verbose output
pytorch-teach run 1 --verbose

# Health check your PyTorch installation
pytorch-teach doctor

Interactive Experience

$ pytorch-teach run 21

    ╔═══════════════════════════════════════════════════════════╗
    ║   🔥 PyTorch Teaching - Professional Learning CLI 🔥    ║
    ║   Master Deep Learning from Basics to Production         ║
    ╚═══════════════════════════════════════════════════════════╝

    ✓ CUDA Available: 12.1 (1 device(s))
      GPU: NVIDIA GeForce RTX 4090

    [Beautiful, interactive lesson on ExecutorTorch deployment...]

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🔥 2024-2025 Trending Resources

🏆 Must-Follow Repositories

🤖 Large Language Models

🎨 Computer Vision

🚀 Training & Optimization

🌐 Advanced PyTorch Frameworks (2024-2025)

Framework	Description	Stars	Use Case
🔥 PyTorch Lightning	High-level PyTorch framework		Production-ready training
⚡ TorchTune	Native PyTorch LLM fine-tuning		LLM fine-tuning
🎯 Diffusers	State-of-the-art diffusion models		Image/Video generation
🧠 Unsloth	2x faster LLM training		Efficient fine-tuning
🔬 torchao	PyTorch native quantization		Model optimization
🎪 Torchvision	Computer vision library		Vision tasks

🎓 Learning Resources 2024-2025

Resource	Type	Level	🌟 Rating
Deep Learning with PyTorch	Official Tutorials	Beginner-Advanced	⭐⭐⭐⭐⭐
Fast.ai Practical Deep Learning	Course	Intermediate	⭐⭐⭐⭐⭐
d2l.ai - Dive into Deep Learning	Interactive Book	All Levels	⭐⭐⭐⭐⭐
PyTorch Recipes	Code Snippets	All Levels	⭐⭐⭐⭐
Papers with Code	Research + Code	Advanced	⭐⭐⭐⭐⭐

🎬 Hot Topics 2024-2025

mindmap
  root((PyTorch 🔥))
    Large Language Models
      LLaMA 3.3
      Mixtral 8x7B
      Gemma 2
      Phi-4
    Computer Vision
      SAM 2
      YOLOv10
      DINO v2
      Depth Anything
    Generative AI
      Stable Diffusion 3.5
      FLUX
      Sora-like models
      ControlNet
    Optimization
      INT4/INT8 Quantization
      Flash Attention 3
      LoRA/QLoRA
      Model Pruning

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🛠️ Installation

Quick Start ⚡

# Clone the repository
git clone https://github.com/umitkacar/Pytorch-Teaching.git
cd Pytorch-Teaching

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install PyTorch (CPU version)
pip install torch torchvision torchaudio

# Install PyTorch (GPU version - CUDA 12.1)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Install additional dependencies
pip install jupyter matplotlib numpy pandas

Docker Setup 🐳

# Pull official PyTorch image
docker pull pytorch/pytorch:2.5.0-cuda12.1-cudnn9-runtime

# Run Jupyter
docker run -it --gpus all -p 8888:8888 -v $(pwd):/workspace pytorch/pytorch:2.5.0-cuda12.1-cudnn9-runtime jupyter notebook --allow-root

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🎯 Roadmap

✅ Lesson 1: Tensor Fundamentals
✅ Lesson 2: Math Operations
✅ Lesson 3: CPU/CUDA Conversion
🚧 Lesson 4: Neural Networks Basics (Coming Soon)
🚧 Lesson 5: Convolutional Neural Networks
🚧 Lesson 6: Recurrent Neural Networks
🚧 Lesson 7: Transformers & Attention
🚧 Lesson 8: Transfer Learning
🚧 Lesson 9: Generative Models
🚧 Lesson 10: Production Deployment

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💻 System Requirements

Minimum Requirements 🖥️ CPU: Intel Core i5 or equivalent 🧠 RAM: 8 GB 💾 Storage: 5 GB free space 🐍 Python: 3.9+ 📦 PyTorch: 2.0+

Recommended Requirements 🖥️ CPU: Intel Core i7/AMD Ryzen 7 🧠 RAM: 16 GB+ 🎮 GPU: NVIDIA RTX 3060+ (8GB VRAM) 💾 Storage: 20 GB SSD 🐍 Python: 3.11+ 📦 PyTorch: 2.9+

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🤝 Contributing

We welcome contributions! 🎉

1. 🍴 Fork the repository
2. 🌿 Create your feature branch (git checkout -b feature/AmazingFeature)
3. 💾 Commit your changes (git commit -m 'Add some AmazingFeature')
4. 📤 Push to the branch (git push origin feature/AmazingFeature)
5. 🎯 Open a Pull Request

See: DEVELOPMENT.md for detailed contribution guidelines.

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📖 Documentation

Complete Documentation Set

Document	Description	Status
README.md	Main project overview and quickstart	✅ Current
INSTALL.md	Detailed installation instructions	✅ Complete
DEVELOPMENT.md	Developer setup and contribution guide	✅ Complete
TEST_RESULTS.md	Comprehensive QA report	✅ Complete
lessons-learned.md	Project insights and best practices	✅ Complete
CHANGELOG.md	Version history and changes	✅ Updated

Quick Links

• 🚀 Getting Started: INSTALL.md
• 🧪 Testing: TEST_RESULTS.md
• 🛠️ Development: DEVELOPMENT.md
• 📝 Changes: CHANGELOG.md
• 🎓 Lessons Learned: lessons-learned.md

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📊 GitHub Stats

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📜 License

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

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🌟 Show Your Support

If you find this project helpful, please consider giving it a ⭐!

Made with ❤️ for the PyTorch Community

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Happy Learning! 🚀✨