MegEngine

Why we included this project

MegEngine covers the whole model lifecycle in one codebase, from training to deployment, which saves teams the usual chore of moving a model through separate tooling for each stage. Its most distinctive feature is a memory planner that can cut GPU usage to about a third when enabled, a real help on shared or modest hardware. The same trained model runs on x86, Arm, CUDA, and ROCm across desktop, mobile, and embedded targets, and quantization and dynamic shapes are handled inside the framework rather than through conversion steps. If you are comfortable with PyTorch and want something built more around efficiency and low-memory inference, MegEngine is a solid alternative to try.