Docs

Python API

Python Guide ›

Python Tutorials ›

Python API Reference ›

Python-first API

MXNet provides a comprehensive and flexible Python API to serve a broad community of developers with different levels of experience and wide ranging requirements. Current efforts are focused on the Gluon API. Gluon provides a clear, concise, and simple API for deep learning. It makes it easy to prototype, build, and train deep learning models without sacrificing training speed.

You can checkout the rich ecosystem built around Apache MXNet Gluon, including D2L.ai, GluonCV, GluonNLP and GluonTS.

While most of the usability improvement around training are focused on the python API, the performance of MXNet is accessible through a variety of different language bindings, checkout their respective API and guides below!

Other Bindings

Scala Guide ›

Scala Tutorials ›

Scala API Reference ›

Java Guide ›

Java Tutorials ›

Java API Reference ›

Clojure Guide ›

Clojure Tutorials ›

Clojure API Reference ›

C/C++ Guide ›

C/C++ Tutorials ›

C/C++ API Reference ›

Julia Guide ›

Julia Tutorials ›

Julia API Reference ›

R Guide ›

R Tutorials ›

R API Reference ›

Perl Guide ›

Perl Tutorials ›

Perl API Reference ›

MXNet Architecture

Building a high-performance deep learning library requires many systems-level design decisions. In this design note, we share the rationale for the specific choices made when designing _MXNet_. We imagine that these insights may be useful to both deep learning practitioners and builders of other deep learning systems.

Deep Learning System Design Concepts

The following pages address general design concepts for deep learning systems. Mainly, they focus on the following 3 areas: abstraction, optimization, and trade-offs between efficiency and flexibility. Additionally, we provide an overview of the complete MXNet system.

• Exception Handling in MXNet
• Efficient Data Loaders
• Dependency Engine
• Memory Consumption
• MXNet System Architecture
• Deep Learning Programming Paradigm

Developer Guide

• GitHub contribution and PR verification tips
• Debugging and performance optimization tips
• Examine forward results with hooks
• Exception handing and custom error types
• Profiling

FAQ

Deployment Environments

• How to convert a Caffe model to MXNet?
• How to run MXNet on AWS?
• How to do distributed training using MXNet on AWS?
• What are MXNet environment variables?
• How to use data from S3 for training?
• How to run MXNet securely?



Model

• How can I train using multiple GPUs with model parallelism?
• How do I run MXNet on Multiple CPU/GPUs with data parallelism?
• How do I visualize neural networks as computation graphs?



Speed

• How do I use mixed precision (float16) with MXNet or Gluon?
• How do I use gradient compression with distributed training?
• Can I use nnpack to improve the CPU performance of MXNet?
• What are the best setup and data-handling tips and tricks for improving speed?
• How can I create a .rec dataset ?



Security

• How to run MXNet securely?



Extend and Contribute to MXNet

• How do I implement operators in MXNet backend?
• How do I use MXNet built with Large Tensor Support
• How do I create new operators in MXNet with Python?
• Why was MXNet developed in the first place ?