PaddlePaddle provides a deep learning framework for training models across devices and platforms.
MegEngine
The MegEngine framework supports deep learning and automatic differentiation, according to its repository description.

About MegEngine
MegEngine is an open-source deep learning framework hosted on GitHub under the Apache License 2.0. Its repository describes it as a fast, scalable, and easy-to-use framework that supports automatic differentiation. It covers both training and inference, and the README states that a single model can be trained and then used for inference on different platforms.
Python packages install from pip wheels on Linux 64-bit, Windows 64-bit, macOS (CPU-only, 10.14+), and Android 7+ (CPU-only), with Python 3.6 to 3.9. Windows 10 users can install the Linux distribution through WSL or the Windows distribution directly. Other platforms are listed for inference only. The README also describes memory-reduction methods, including the DTR algorithm and a Pushdown memory planner, but these are the project's own claims and are not independently verified here.
Key features
- Unified framework for both model training and inference
- Quantization, dynamic shape handling, and image pre-processing within a single model
- Pushdown memory planner for inference with lower memory usage
- DTR algorithm for reducing GPU memory use during training
- Inference on x86, Arm, CUDA, and RoCM hardware
- Automatic differentiation support
- Covers both training and inference within one framework
- Lists inference support for x86, Arm, CUDA, and RoCM hardware
- Released under the permissive Apache License 2.0
- Pip installation is listed for Python 3.6 to 3.9 only
- Python installs on macOS and Android are CPU-only
- Memory reduction figures are claims made in the README and are not independently verified
MegEngine at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
MegEngine alternatives
Tools and sites you could use instead, ranked by how closely they match.
QKeras provides quantization tools for deep learning with TensorFlow Keras.
The linfa project provides a machine learning framework written in Rust.
FATE supports federated learning through an industrial grade framework.
MLX provides an array computing framework designed for Apple silicon devices.
Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.
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