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

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License

Apache License V2.0

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Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

C++

Related Categories

C++ Deep Learning Frameworks

Registered

2025-08-18