MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of symbolic programming and imperative programming to maximize efficiency and productivity. In its core, 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. The library is portable and lightweight, and it scales to multiple GPUs and multiple machines.
MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning system, and interesting insights of DL systems for hackers.
- MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost
- Tutorial for NVidia GTC 2016
- Embedding Torch layers and functions in MXNet
- MXNet.js: Javascript Package for Deep Learning in Browser (without server)
- Design Note: Design Efficient Deep Learning Data Loading Module
- 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