01 — Introduction to PyTorch
PyTorch is an open-source machine learning framework focused on flexible model development, strong GPU support, and increasingly powerful production tooling.
Read More →PyTorch Deep Dive Blog Series
A comprehensive exploration of PyTorch from its history and internals to modern compilation techniques and distributed training. Learn what makes PyTorch tick and how to leverage its full power for your machine learning projects.
Blog Posts
02 — The Evolution and History of PyTorch
PyTorch was introduced by Facebook AI Research (FAIR) in 2016 as a Python interface over Torch's tensor and neural-network capabilities.
Read More →03 — Internals: Autograd and Dispatch
Explore the core mechanisms that make PyTorch work: automatic differentiation and the dispatch system that handles different device types.
Read More →04 — Design Decisions
Understanding the key design decisions that shaped PyTorch's architecture and why they matter for developers and researchers.
Read More →05 — Programming Paradigm
PyTorch's programming paradigm emphasizes flexibility and ease of use while maintaining performance for production workloads.
Read More →06 — Core Features and Ecosystem
A comprehensive look at PyTorch's core features and the rich ecosystem of libraries and tools built around it.
Read More →07 — Dynamo and TorchInductor
Deep dive into TorchDynamo and TorchInductor, the key components of PyTorch's modern compilation pipeline.
Read More →08 — Compilation in PyTorch 2
How PyTorch 2.x revolutionized performance with torch.compile and the new compilation stack.
Read More →09 — IRs, DSLs, LLVM, and XLA
Understanding the intermediate representations, domain-specific languages, and compiler backends that power PyTorch.
Read More →10 — Distributed Training and Scalability
How PyTorch handles distributed training across multiple GPUs and nodes for large-scale model training.
Read More →11 — Tutorial: Building a Training Loop
Step-by-step guide to building a complete training loop in PyTorch, from data loading to model optimization.
Read More →12 — Alternatives and When to Use What
A practical comparison of PyTorch with other ML frameworks and guidance on when to choose each one.
Read More →