dingke/neon

Python based Deep Learning Framework by Nervana™

neon

neon is Nervana's Python based Deep Learning framework and achieves the fastest performance on many common deep neural networks such as AlexNet, VGG and GoogLeNet. We have designed it with the following functionality in mind:

• Support for commonly used models and examples: convnets, MLPs, RNNs, LSTMs, autoencoders. You can find many pre-trained implementations of these in our model zoo
• Tight integration with nervanagpu kernels for fp16 and fp32 (benchmarks) on Maxwell GPUs
  • 3s/macrobatch (3072 images) on AlexNet on Titan X (Full run on 1 GPU ~ 32 hrs)
  • Fast image captioning model (~200x faster than CPU based NeuralTalk)
• Basic automatic differentiation support
• Framework for visualization
• Swappable hardware backends: write code once and then deploy on CPUs, GPUs, or Nervana hardware

New features in latest release.

We use neon internally at Nervana to solve our customers' problems across many domains. We are hiring across several roles. Apply here!

Getting started

Basic information to get started is below. Please consult the full documentation for more information.

Installation

• Local install and dependencies

Quick Install

On a Mac OSX or Linux machine, enter the following to download and install neon, and use it to train your first multi-layer perceptron or convolutional neural networks below.

git clone https://github.com/NervanaSystems/neon.git
cd neon
make
. .venv/bin/activate
neon examples/mnist_mlp.yaml
# alternatively, use a script:
python examples/mnist_mlp.py

Code organization

backends    --- implementation of different hardware backends
layers      --- layer code
models      --- model code
optimizers  --- learning rules
transforms  --- activation & cost functions

Documentation

The complete documentation for neon is available here. Some useful starting points are:

• Using neon
• API
• Developing for neon

Support

For any bugs or feature requests please:

1. Search the open and closed issues list to see if we're already working on what you have uncovered.
2. Check that your issue/request hasn't already been addressed in our Frequently Asked Questions (FAQ) or neon-users Google group.
3. File a new issue or submit a new pull request if you have some code you'd like to contribute

For other questions and discussions please:

1. Post a message to the neon-users Google group

Machine learning OPerations (MOP) Layer

The MOP is an abstraction layer for Nervana's system software and hardware which includes the Nervana Engine, a custom distributed processor for deep learning.

The MOP consists of linear algebra and other operations required by deep learning. Some MOP operations are currently exposed in neon, while others, such as distributed primitives, will be exposed in later versions as well as in other forthcoming Nervana libraries.

Defining models in a MOP-compliant manner guarantees they will run on all provided backends. It also provides a way for existing Deep Learning frameworks such as theano, torch, and caffe to interface with the Nervana Engine.

License

We are releasing neon under an open source Apache 2.0 License. We welcome you to contact us with your use cases.