MegEngine is a deep learning framework designed for both training and inference, targeting developers and researchers who need a fast, scalable, and user-friendly solution. It offers a unified model for both stages, simplifying deployment and enabling features like quantization and dynamic shape processing with a single model.

How It Works

MegEngine utilizes a Pushdown memory planner and a DTR (Dynamic Tensor Runtime) algorithm to significantly reduce GPU memory usage, potentially to one-third of the original. This approach allows for efficient inference across diverse hardware platforms including x86, Arm, CUDA, and RoCM, supporting operating systems like Linux, Windows, iOS, and Android.

Quick Start & Requirements

• Installation: python3 -m pip install megengine -f https://megengine.org.cn/whl/mge.html
• Prerequisites: Python 3.6-3.9. Supports Linux-64bit, Windows-64bit, and macOS (CPU-Only) 10.14+. Windows users can use WSL or a native Windows distribution.
• Resources: Pre-built binaries via pip. Building from source requires CMake.
• Documentation: MegEngine Documentation (Chinese)

Highlighted Details

• Unified framework for training and inference.
• Low memory usage via DTR algorithm and Pushdown memory planner.
• Efficient inference across x86, Arm, CUDA, RoCM, and various OS (Linux, Windows, iOS, Android).
• Supports quantization, dynamic shapes, and automatic differentiation.

Maintenance & Community

• Community guidelines follow the Contributor Covenant.
• Requires signing a Contributor License Agreement (CLA) for contributions.
• Contact: GitHub Issues, Email: megengine-support@megvii.com, Forum: discuss.megengine.org.cn, QQ Group: 1029741705.

Licensing & Compatibility

• Licensed under the Apache License, Version 2.0.
• Permissive license suitable for commercial use and closed-source linking.

Limitations & Caveats

The README specifies Python 3.6-3.9 support, which may be outdated. macOS support is CPU-only. While inference is supported on many platforms, training capabilities might be more restricted.