tiny_pytorch

Unravel the magic of modern deep learning by building a PyTorch-like framework from the ground up.
ML
DL
Python
C++
CUDA
Author

Imad Dabbura

Tiny-PyTorch – Unraveling Deep Learning’s Core

As an AI scientist, I firmly believe that a profound understanding of complex systems and algorithms stems from building them from the ground up. This conviction is the driving force behind Tiny-PyTorch, an educational deep learning framework crafted entirely in Python. It’s my personal quest to demystify the “magic” of modern deep learning by offering a transparent, from-scratch implementation of the essential components found in frameworks like PyTorch.

Tiny-PyTorch strips away high-level abstractions, allowing you to see the core logic of deep learning principles, connect theoretical concepts to their concrete computational realizations, and ultimately become a more insightful researcher by understanding the underlying mechanics and inherent trade-offs of the tools that underpin our field.

Key Highlights:

  • From Scratch, By Design: Every fundamental piece, from the Tensor object and its dynamic computation graph to the reverse-mode automatic differentiation (autograd) engine, is meticulously built from first principles. This provides an unparalleled view into the mathematical operations and computational flow that drive deep learning.
  • Modular Architecture: The project is structured to mirror major deep learning libraries, progressing from low-level NDArray operations and pluggable hardware backends (NumPy, custom CPU, and CUDA) to higher-level APIs like nn.Module and standard optimizers (SGD, Adam). This layered approach illuminates the architectural considerations of such frameworks.
  • Educational Foundation: It’s designed as a practical learning tool to bridge the gap between abstract mathematical concepts and their concrete code implementation, fostering a deeper understanding of how deep learning truly functions at its core.
  • Core Features Implemented: Includes Tensor with autograd, an extensible nn.Module system, standard optimizers, and a pluggable backend system for hardware acceleration, all constructed to expose their foundational elements.

Installation:

You can install Tiny-PyTorch directly from PyPI:

pip install tiny-pytorch

Resources: