Meta PyTorch Developer Handbook

The Meta PyTorch Developer Handbook serves as the comprehensive reference for developing, training, and deploying deep learning models using PyTorch—Meta’s open-source machine learning framework. It explains the core architecture, tensor operations, autograd system, and model optimization techniques, while guiding developers through distributed training, deployment, and integration with Meta’s AI tooling. Aimed at researchers and engineers, the handbook provides practical examples and best practices for building efficient, scalable AI systems with PyTorch.

Meta Platforms, Inc. — PyTorch Engineering and Developer Documentation Team

1. Introduction
• Overview of PyTorch
• Key Features and Design Philosophy
• Installation and Setup
• PyTorch Ecosystem and Tools

2. Core Concepts
• Tensors and Tensor Operations
• Autograd: Automatic Differentiation
• Computational Graphs
• Modules and Parameters

3. Building Models
• Creating Custom Neural Networks
• Using torch.nn and Sequential APIs
• Activation Functions and Loss Functions
• Model Initialization and Management

4. Training and Optimization
• Forward and Backward Propagation
• Optimizers and Learning Rate Schedules
• Gradient Clipping and Regularization
• Checkpointing and Model Saving

5. Data Handling
• torch.utils.data and DataLoaders
• Transformations and Augmentations
• Working with Custom Datasets
• Efficient Input Pipelines

6. Distributed and Scalable Training
• Data Parallel and Distributed Data Parallel (DDP)
• Model Parallelism
• Mixed Precision and AMP
• Multi-GPU and Multi-Node Training

7. Deployment and Inference
• TorchScript and Model Tracing
• PyTorch Mobile and Edge Deployment
• Integration with ONNX and TensorRT
• Serving Models with TorchServe

8. Performance and Debugging
• Profiling Tools and Optimization Techniques
• Debugging Common Issues
• Memory Management and GPU Utilization

9. Advanced Topics
• Custom CUDA Kernels and Extensions
• Graph Mode Execution and FX Tracing
• Quantization and Pruning
• Reinforcement Learning and PyTorch RL Libraries

10. Ecosystem and Libraries
• PyTorch Lightning, Accelerate, and Others
• TorchVision, TorchAudio, TorchText
• Meta AI Research Tools (e.g., FAIRSEQ, Detectron2)

11. Appendices
• API Reference
• Migration Guides (1.x to 2.x)
• Glossary and Additional Resources