Apache MXNet is a scalable, efficient open-source deep learning framework—offering a flexible hybrid programming model (symbolic + imperative) and supporting a wide array of languages—designed for training and deploying neural networks across heterogeneous systems. Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scalable to many GPUs and machines. Apache MXNet is more than a deep learning project. It is a community on a mission of democratizing AI. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.
Features
- Hybrid front-end: switch between symbolic and imperative programming
- Automatic dynamic dependency scheduler for parallelism
- Graph optimization for memory efficiency and speed
- Broad language support: Python, C++, R, Julia, Java, JavaScript, Scala, Perl
- Portable across devices—from mobile to distributed GPU systems
- Active community, though archived and moved to read-only by November 2023