HIPS/autograd

Health v2 · maintenance only

Adoption not part of the score

7523 stars · 940 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Autograd is a Python library that efficiently computes derivatives of native Python and NumPy code, supporting reverse-mode (backpropagation) and forward-mode automatic differentiation. It handles a large subset of Python features including loops, ifs, recursion, and closures, and supports higher-order derivatives.

Use cases

• compute gradients of numpy functions for gradient-based optimization
• take derivatives of derivatives of a function
• backpropagate through a neural network implemented in numpy
• differentiate scipy functions
• compute elementwise derivatives of vectorized functions
• verify gradients against finite differences

When to choose

• you need gradients of plain Python/NumPy code without rewriting it in a deep learning framework
• you want a lightweight, minimal autodiff library for research or teaching
• you need higher-order derivatives or mixed forward/reverse-mode differentiation

When to avoid

• you need GPU acceleration or large-scale deep learning training - use JAX or PyTorch instead
• you need a full deep learning framework with optimized kernels and distributed training
• your code relies on operations outside the supported NumPy/SciPy subset

Facets

library · maturity stable

machine-learning math machine-learning data-science python automatic-differentiation numpy backpropagation gradients optimization scipy algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="HIPS/autograd")