dmlc/minpy
NumPy interface with mixed backend execution observed · 2026-08-28
Health v2 · maintenance only
10/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases archived no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3954
- days_rel: n/a
- days_push: 3117
- n_releases_24m: 0
Adoption not part of the score
1094 stars · 104 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
MinPy is a Python library providing a pure NumPy interface backed by MXNet, automatically dispatching operators to GPU when available and falling back to NumPy on CPU. It includes automatic gradient generation via Autograd and seamless MXNet symbol integration.
Use cases
- run numpy code on gpu transparently
- prototype deep learning algorithms with numpy syntax
- get automatic gradients for numpy-like code
- mix mxnet symbolic ops with imperative numpy code
- graceful cpu fallback for missing gpu operators
When to choose
- you are maintaining legacy code already written against minpy
- you want a historical example of mixed imperative/symbolic execution design
When to avoid
- starting any new deep learning project - use MXNet Gluon, PyTorch, or JAX instead
- you need maintained software with recent releases
- you need production-grade performance or support
Facets
library · maturity abandoned
machine-learning deep-learning gpu-computing math deep-learning machine-learning gpu-computing python numpy mxnet autograd deprecated mixed-execution gpu
2 sources
- readme: https://github.com/dmlc/minpy · fetched 2026-08-28 · 1fd6e47a2677
- registry_pypi: https://pypi.org/pypi/minpy/json · fetched 2026-08-29 · e50af739e9f9
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| dmlc/minpy | main | 10 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem