# MegEngine/MegEngine

MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架

Repository: https://github.com/MegEngine/MegEngine
Canonical: https://ross.abutalabs.com/products/megengine
Homepage: https://megengine.org.cn/
Language: C++
License: Apache-2.0
License Family: permissive
Topics: deep-learning, machine-learning, megengine, tensor, autograd, python, gpu, numpy
Last push: 2024-10-24T10:10:58+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2359, "days_push": 678, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4808, forks 546 (observed 2026-08-28T04:09:00.252202+00:00)

## What it is
MegEngine is a fast, scalable deep learning framework with automatic differentiation, developed in C++ with Python bindings. It unifies training and inference in a single model, supports quantization and dynamic shapes, and deploys efficiently across x86, Arm, CUDA, and ROCm platforms.

## Use cases
- train neural networks with autograd in Python
- deploy deep learning models on mobile and embedded devices
- reduce GPU memory usage during training with DTR
- quantize models for faster inference
- run inference on CPU, CUDA, or ROCm hardware
- build models with a NumPy-like tensor API

## When to choose
- you need a single framework for both training and inference
- you target diverse platforms including mobile, TEE, and embedded devices
- GPU memory is a bottleneck and you need DTR-style optimization
- you want quantization-aware training with a unified model

## When to avoid
- you need the broadest ecosystem of pretrained models and community tools (PyTorch/TensorFlow)
- you require the latest Python versions (MegEngine supports Python 3.6-3.9)
- you need frequent releases and very active development

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning, llm-training
- domain: deep-learning, machine-learning, gpu-computing
- platform: windows, python, cpp, cross-platform
- tags: autograd, tensor, training-and-inference, quantization, dtr-memory-optimization, numpy-like-api, linux, macos, android, ios, gpu

## Member repositories
- MegEngine/MegEngine (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:00.252202+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:18:36.083122+00:00, confidence not recorded.
  - readme: https://github.com/MegEngine/MegEngine (fetched 2026-08-28T04:09:00.252202+00:00, sha 8c3e87ed0097)
- Data as of 2026-08-30T08:39:29.467469+00:00.