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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

Advanced PyTorch

Prerequisites Intermediate PyTorch & DL concepts

Level:

Advanced

PyTorch Internals
Understand the underlying mechanics of PyTorch tensors and the autograd engine.
Custom Operations
Implement custom autograd functions and C++/CUDA extensions for specialized operations.
Advanced Architectures
Build complex models like Transformers, GNNs, and Normalizing Flows.
Optimization Techniques
Apply advanced optimizers, learning rate schedules, mixed-precision training, and regularization.
Distributed Training
Implement various parallel training strategies including DDP, Model Parallelism, and FSDP.
Model Deployment Optimization
Optimize models for inference using TorchScript, quantization, pruning, and profiling.

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