Advanced PyTorch
Chapter 1: PyTorch Internals and Autograd
Tensor Implementation Details
Understanding the Computational Graph
Autograd Engine Mechanics
Custom Autograd Functions: Forward and Backward
Higher-Order Gradient Computation
Inspecting Gradients and Graph Visualization
Memory Management Considerations
Hands-on Practical: Building Custom Autograd Functions
Chapter 2: Advanced Neural Network Architectures
Implementing Transformers from Components
Advanced Attention Mechanisms
Graph Neural Networks with PyTorch Geometric
Normalizing Flows for Generative Modeling
Neural Ordinary Differential Equations
Meta-Learning Algorithms
Practice: Implementing a Custom GNN Layer
Chapter 3: Optimization Techniques and Training Strategies
Sophisticated Optimizers Overview
Advanced Learning Rate Scheduling
Regularization Methods
Gradient Clipping and Accumulation
Mixed-Precision Training with torch.cuda.amp
Strategies for Handling Large Datasets
Automated Hyperparameter Tuning
Hands-on Practical: Implementing Mixed-Precision Training
Chapter 4: Model Deployment and Performance Optimization
TorchScript Fundamentals: Tracing vs Scripting
Model Quantization Techniques
Model Pruning Strategies
Performance Analysis with PyTorch Profiler
Optimizing Kernels with External Libraries
Exporting Models to ONNX Format
Serving Models with TorchServe
Practice: Profiling and Quantizing a Model
Chapter 5: Distributed Training and Parallelism
Fundamental Concepts of Distributed Computing
Data Parallelism with DistributedDataParallel (DDP)
Tensor Model Parallelism
Pipeline Parallelism Implementation
Fully Sharded Data Parallelism (FSDP)
Using torch.distributed Primitives
Setting up Distributed Environments
Hands-on Practical: Setting up a DDP Training Script
Chapter 6: Custom Extensions and Interoperability
Building Custom C++ Extensions
Building Custom CUDA Extensions
Working with the ATen Library
Interfacing PyTorch with NumPy
Extending torch.nn with Custom Modules
Extending torch.optim with Custom Optimizers
Foreign Function Interfaces (FFI)
Practice: Building a Simple CUDA Extension
Pipeline Parallelism Implementation
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- GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism, Yanping Huang, Youlong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Thang Luong, Yonghui Wu, Zhifeng Chen, 2019 Advances in Neural Information Processing Systems (NeurIPS) (NeurIPS Foundation) DOI: 10.48550/arXiv.1905.01329 - Introduces the GPipe algorithm for pipeline parallelism, detailing the use of micro-batching to reduce idle time.
- Distributed communication package -
torch.distributed, PyTorch Contributors, 2025 (PyTorch) - Official documentation covering PyTorch's distributed communication primitives, fundamental for manual pipeline parallelism implementations. - DeepSpeed: Large-Scale Distributed Training with Pipeline Parallelism, Samyam Rajbhandari, Cong Li, Zhun Liu, Olatunji Ruwase, Justin K. Romberg, Yuxiong He, 2020 arXiv preprint arXiv:2006.02708 DOI: 10.48550/arXiv.2006.02708 - Presents DeepSpeed's pipeline parallelism capabilities, including various scheduling strategies for efficient training of large models.
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