This repository provides a curated collection of high-quality, concise PyTorch examples across various domains like computer vision, natural language processing, and reinforcement learning. It serves as a valuable resource for developers and researchers seeking to understand and implement diverse deep learning models and techniques using PyTorch.

How It Works

The examples are designed to be self-contained and have minimal dependencies, allowing users to easily run and adapt them. They showcase fundamental PyTorch concepts and advanced architectures, demonstrating practical applications of deep learning algorithms. The repository aims to offer substantially different examples, covering a broad spectrum of use cases.

Quick Start & Requirements

• Install: Clone the repository and install PyTorch: pip install torch torchvision torchaudio.
• Prerequisites: Python 3.x. Specific examples may require additional libraries like torchtext, numpy, matplotlib, gym, etc., which can typically be installed via pip.
• Resources: Varies by example; some are lightweight (MNIST), while others (ImageNet, Transformers) may require significant GPU resources and time for training.
• Links:
  • Examples: https://pytorch.org/examples/
  • Contribution Guide: https://github.com/pytorch/examples/blob/main/CONTRIBUTING.md

Highlighted Details

• Covers a wide range of applications: Image Classification (MNIST, ImageNet), Language Modeling (RNN, Transformer), GANs, VAEs, Reinforcement Learning (CartPole), Neural Style Transfer, Distributed Training, and C++ Frontend.
• Includes examples for advanced topics like Imagenet training with popular networks, Superresolution, and Forward-Forward algorithms.
• Showcases practical implementations of Natural Language Inference and Time sequence prediction.
• Features examples for distributed PyTorch using Data Parallel and RPC.

Maintenance & Community

This repository is officially maintained by the PyTorch team. For general Q&A and support, users can engage with the PyTorch community on https://discuss.pytorch.org/.

Licensing & Compatibility

The repository's code is typically licensed under a permissive license (e.g., BSD-style), allowing for broad use in research and commercial applications. However, users should always verify the specific license of individual example files.

Limitations & Caveats

While curated, some examples might require substantial computational resources (GPU, memory) and time for training or fine-tuning. The focus is on demonstrating core concepts, and production-ready deployment might require additional engineering.