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Part I: Getting Started with torch

Welcome to the foundational core of the PyTorch Builder’s Companion Book.

This part introduces the torch module—the backbone of the PyTorch ecosystem. Whether you're a beginner eager to understand tensors or an advanced developer optimizing GPU workloads, Part I grounds you in the essential concepts and setup steps required to use PyTorch effectively and confidently.

Chapter 1: What is torch and Why Does It Matter?

This chapter lays the philosophical and technical foundation for everything that follows.

  • torch is not just another module—it's the core engine powering every other component in PyTorch.
  • We explore its place in the PyTorch ecosystem:
  • torch (core tensor ops),
  • torch.nn (neural network abstractions),
  • torch.autograd (differentiation),
  • torchvision/torchaudio/torchtext (domain-specific wrappers).
  • The torch.Tensor is introduced as the primary building block—powerful, GPU-ready, and autograd-compatible.
  • Key takeaway: To truly master PyTorch, you need to understand torch—not just use it, but leverage it for custom layers, debugging, and innovation.

Chapter 2: Installation & Setup

Before building models, you need a proper PyTorch environment.

  • Walkthrough of the installation process using pip or conda with CPU or CUDA support.
  • Emphasis on using virtual environments to avoid polluting your global Python setup.
  • Quick verification steps:
  • Importing torch
  • Creating random tensors
  • Checking GPU availability
  • Intro to your first mini Tensor Playground—creating, operating, and moving tensors to CUDA if available.
  • Tips on CPU vs GPU behavior, plus troubleshooting advice (e.g., nvidia-smi, Python version compatibility).

By the end of this chapter, you’ll be running tensors on your device and preparing for deeper exploration.

Chapter 3: Tensor Fundamentals

This chapter goes deeper into the anatomy of tensors.

  • What is a tensor? From scalars (0D) to multi-dimensional batches of data (4D+).
  • Comparison with NumPy:
  • API similarity
  • GPU support
  • Autograd capability
  • Tensor interoperability with NumPy (shared memory warning!).
  • Introduction to device management:
  • Sending tensors to 'cpu' or 'cuda'
  • Choosing a device dynamically
  • Use-case comparisons (CPU vs GPU) and why your tensor placement directly impacts performance.

Mastering tensors means mastering data representation, device flow, and performance—skills critical for both beginners and pros.

Summary of Part I

Concept Key Insight
torch Module The core engine behind all PyTorch operations
torch.Tensor Foundation for all data, models, and gradients in PyTorch
Installation & Setup Prepare clean environments with optional CUDA support
Device Management Know how to work with CPU vs GPU to optimize performance
Tensor Playground Hands-on intro to tensor creation, manipulation, and hardware acceleration

Whether you’re debugging deep models or building your first neural net, Part I arms you with essential tools to begin your PyTorch journey on solid ground.

→ Next up: A deeper dive into the torch.Tensor API.