for Deep Learning
MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity.
What's New
- MXNet on Mobile Device
- Distributed Training
- Guide to Creating New Operators (Layers)
- Amalgamation and Go Binding for Predictors
- Training Deep Net on 14 Million Images on A Single Machine
- MXNet.jl Julia binding initial release
Contents
- Documentation and Tutorials
- Open Source Design Notes
- Code Examples
- Pretrained Models
- Contribute to MXNet
- Frequent Asked Questions
Features
- Open sourced design note on useful insights that can re-used by general DL projects.
- Flexible configuration, for arbitrary computation graph.
- Mix and Maximize good flavours of programmingto maximize flexibility and efficiency.
- Lightweight, memory efficient and portable to smart devices.
- Scales up to multi GPUs and distributed setting with auto parallelism.
- Support python, R, C++, Julia,
- Cloud friendly, and directly compatible with S3, HDFS, AZure
Bug Reporting
- For reporting bugs please use the mxnet/issues page.
License
© Contributors, 2015. Licensed under an Apache-2.0 license.
History
MXNet is initiated and designed in collaboration by authors from cxxnet, minerva and purine2. The project reflects what we have learnt from the past projects. It combines important flavour of the existing projects, being efficient, flexible and memory efficient.