Lightweight PyTorch and CUDA Learning Resources | Generated by AI
If tinygrad feels overwhelming with its code volume, there are several lighter, more focused alternatives for diving into PyTorch-style frameworks or CUDA-based projects. These emphasize educational simplicity, often with just a few hundred lines of code, to help you grok the internals like autograd, tensors, or GPU kernels without the full framework bloat. I’ll break them down by focus area.
Minimal PyTorch-Like Implementations (for Learning DL Framework Basics)
These are tiny re-implementations that mimic PyTorch’s core mechanics (e.g., tensors, backprop) but strip everything else away.
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Micrograd: A super-minimal autograd engine (under 200 lines) that powers neural nets from scratch. It’s perfect for understanding PyTorch’s backward pass and gradients. Andrej Karpathy’s accompanying video tutorial walks through it step-by-step, building up to a simple MLP. Start here if you want the essence of PyTorch’s dynamic computation graph.
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minGPT: A clean, interpretable re-implementation of GPT in ~300 lines of PyTorch code. It covers tokenization, transformer layers, and training/inference loops. Great for seeing how PyTorch glues together without extras—ideal if you’re into generative models.
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Mamba Minimal: A one-file PyTorch impl of the Mamba state-space model. It’s tiny (~100 lines for the core) and matches the official output, helping you learn selective scan ops and sequence modeling internals.
Tiny TensorFlow-Like Options
Fewer pure “tiny” TensorFlow clones exist, but these scratch the surface:
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Mini TensorFlow from Scratch: A from-scratch build of a basic TensorFlow-like library focusing on differentiable graphs and ops. It’s a short tutorial-style project (Python-only) that explains tensor ops and backprop without GPU complexity—good for contrasting with PyTorch’s eager mode.
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Tract: A no-frills, self-contained TensorFlow/ONNX inference engine in Rust (but with Python bindings). It’s tiny and focuses on runtime execution, useful for learning how TF models run under the hood without training overhead.
General CUDA Projects/Tutorials (for GPU-Focused Learning)
If you want to zoom in on CUDA kernels alongside a PyTorch vibe, these guide you through custom ops or full frameworks with GPU support:
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PyTorch from Scratch with CUDA: A hands-on project to recreate PyTorch’s core (tensors, autograd, optimizers) in C++/CUDA/Python. It includes GPU acceleration and ends with a working neural net—excellent for bridging high-level PyTorch to low-level CUDA without drowning in code.
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Writing CUDA Kernels for PyTorch: A beginner-friendly guide to crafting custom CUDA extensions in PyTorch. It starts with basics (matrix mul on GPU) and scales to real ops, with code snippets you can tweak. Pair it with PyTorch’s official extension docs for quick wins.
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Implementing PyTorch CUDA Operators Tutorial: Step-by-step on writing and integrating CUDA kernels into PyTorch (e.g., custom convolutions). It’s tutorial-driven, like explaining to a friend, and assumes basic C++—focuses on the “how” of GPU acceleration in DL frameworks.
Start with micrograd or minGPT for quick momentum—they’re the most digestible. If CUDA is the goal, jump to the from-scratch PyTorch project. All these are on GitHub, so clone and experiment.
Micrograd
minGPT
Mamba Minimal
Mini TensorFlow from Scratch
Tract
PyTorch from Scratch with CUDA
Writing CUDA Kernels for PyTorch
Implementing PyTorch CUDA Operators