- Tutorials and iPython notebooks to get users started with using neon for deep learning.
- Support for commonly used layers: convolution, RNN, LSTM, GRU, BatchNorm, and more.
- Model Zoo contains pre-trained weights and example scripts for start-of-the-art models, including: VGG, Reinforcement learning, Deep Residual Networks, Image Captioning, Sentiment analysis, and more.
- Swappable hardware backends: write code once and then deploy on CPUs, GPUs, or Nervana hardware
For fast iteration and model exploration, neon has the fastest performance among deep learning libraries (2x speed of cuDNNv4, see benchmarks).
- 2.5s/macrobatch (3072 images) on AlexNet on Titan X (Full run on 1 GPU ~ 26 hrs)
- Training VGG with 16-bit floating point on 1 Titan X takes ~10 days (original paper: 4 GPUs for 2-3 weeks)
See the new features in our latest release.
On a Mac OSX or Linux machine, enter the following to download and install
neon (conda users see the guide), and use it to train your first multi-layer perceptron. To force a python2 or python3 install, replace
make below with either
make python2 or
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
The complete documentation for neon is available here. Some useful starting points are:
- Tutorials for neon
- Overview of the neon workflow
- API documentation
- Resources for neon and deep learning
For any bugs or feature requests please:
- Search the open and closed issues list to see if we're already working on what you have uncovered.
- Check that your issue/request hasn't already been addressed in our Frequently Asked Questions (FAQ) or neon-users Google group.
- 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 post a message to the neon-users Google group