A self-contained Python script (workflow.py) that serves as a complete reference for modern PyTorch development. It demonstrates foundational concepts and production-ready techniques for building robust deep learning models.

This repository enables developers to:

• Understand and manipulate PyTorch Tensors and Autograd.
• Build Feed-Forward, CNN, and RNN architectures.
• Train models with custom datasets and loaders.
• Apply transfer learning and deploy models with TorchScript.
• Optimize performance using AMP and gradient clipping.

🧠 Key Features

• Tensor Operations: Initialization and manipulation of PyTorch tensors.
• Autograd: Automatic differentiation and gradient computation.
• Model Architectures: Feed-Forward, CNN, RNN implementations.
• Data Pipeline: Custom Dataset and DataLoader.
• Training Utilities: Loss functions, optimizers, schedulers.
• Stability Enhancements: Gradient clipping and error handling.
• Transfer Learning: ResNet-18 with frozen weights and custom head.
• Model Persistence: Save/load model weights using state_dict.
• Deployment: TorchScript conversion for production use.
• Performance Optimization: AMP via torch.cuda.amp.GradScaler.

git clone https://github.com/luckyjoy/basic_pytorch_workflow.git cd basic_pytorch_workflow pip install -r requirements.txt

python workflow.py

1. Fork the repository
2. Create a new branch (feature/awesome-enhancement)
3. Commit your changes
4. Open a Pull Request

• Author: Bang Thien Nguyen - ontario1998@gmail.com

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