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
Foreign Function Interfaces (FFI)
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ctypes- A foreign function library for Python, Python Software Foundation, 2024 - The official reference for Python's standard library module for calling functions in shared libraries.- CFFI Documentation, Armin Rigo, Maciej Fijalkowski, 2024 - Official documentation for CFFI, an alternative foreign function interface for Python.
- Extending PyTorch with C++ and CUDA, PyTorch Authors, 2025 (PyTorch) - Provides guidance on building custom PyTorch extensions, which is the alternative to FFI discussed in the section.
- High Performance Python: Practical Performant Programming for Humans, Micha Gorelick, Ian Ozsvald, Mikael Vejdemo-Johansson, Jon Roes, Reuven M. Lerner, Tarek Ziadé, David Beazley, 2020 (O'Reilly Media) - Includes practical strategies for improving Python performance, including detailed explanations of
ctypesand CFFI.