hannes-brt/hebel

Health v2 · maintenance only

Adoption not part of the score

1169 stars · 116 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Hebel is a GPU-accelerated deep learning library in Python built on PyCUDA, implementing feed-forward neural networks for classification and regression with SGD, momentum, dropout, and regularization. The author no longer actively develops it and recommends Chainer instead.

Use cases

• train feed-forward neural networks on GPU with CUDA
• classify MNIST digits with a shallow neural net
• run regression with deep learning in Python
• experiment with dropout and Nesterov momentum SGD
• train multi-task neural network models

When to choose

• you need a lightweight CUDA-based neural network library with YAML-driven training configs
• you are maintaining legacy code already built on Hebel

When to avoid

• you need an actively maintained deep learning framework
• you need convolutional networks, autoencoders, or modern architectures
• you lack an NVIDIA GPU with CUDA and PyCUDA

Facets

library · maturity abandoned

deep-learning machine-learning gpu-computing deep-learning machine-learning gpu-computing python windows cuda pycuda neural-networks feedforward-networks sgd dropout linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="hannes-brt/hebel")