MegEngine is a free deep learning framework for developing and deploying models, with automatic differentiation and local training. It uses one model for training and inference, including quantization and dynamic shapes. The project says enabling DTR can reduce GPU memory use to one-third of the original. Inference support spans x86, Arm, CUDA, and ROCm, while GPU use requires compatible device drivers. Python packages are listed for 64-bit Linux and Windows, macOS 10.14 or later, and Android 7 or later; macOS and Android installations are CPU-only. The installation guide lists Python 3.6–3.9. For deployment, MegEngine Lite provides C/C++, Rust, and Python runtimes. Related tools include MgeConvert for converting between MegEngine and third-party model formats, MegFile for S3, HTTP, and local file interfaces, and MegFlow for streaming computation in AI applications. The project presents tutorials for beginners and advanced developers, and lists GitHub issues, a forum, a QQ group, and email for support.
Who it is for
MegEngine suits developers building models locally and carrying them through deployment. Its tutorials address beginners and advanced developers, while GPU users need compatible device drivers.
What is good
- One model serves training and inference.
- Inference support covers x86, Arm, CUDA, and ROCm.
- Lite offers C/C++, Rust, and Python runtimes.
- Related tools cover file access and model conversion.
- Support includes a forum and GitHub issues.
What to know first
- Python guide lists versions 3.6–3.9.
- macOS and Android package installations are CPU-only.
- GPU use requires compatible device drivers.
Verdict
MegEngine combines model development and deployment with related conversion, file, and streaming tools. Check its listed Python versions and platform limits, especially the CPU-only macOS and Android packages.
MegEngine plans and pricing
All plansCompared on deep learning software
- Free plan
- Yes
- Training mode
- local
- Deployment targets
- multiple
- GPU acceleration
- Yes
- Distributed training
- Yes
- Supported languages
- Python, C++
- Model formats
- MegEngine .mge/traced module, Caffe, ONNX, TFLite