Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
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Latest commit 0d643dd Feb 21, 2017 @jennifermyers jennifermyers Update for v1.8.2

README.md

neon

neon is Intel Nervana's reference deep learning framework committed to best performance on all hardware. Designed for ease-of-use and extensibility.

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)

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

See the new features in our latest release.

Quick Install

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 make python3.

    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

Documentation

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

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 post a message to the neon-users Google group

License

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