Date:June 30, 2016

neon is Nervana ’s Python-based deep learning library. It provides ease of use while delivering the highest performance.

Features include:

  • Support for commonly used models including convnets, RNNs, LSTMs, and autoencoders. You can find many pre-trained implementations of these in our model zoo
  • Tight integration with our state-of-the-art GPU kernel library
  • 3s/macrobatch (3072 images) on AlexNet on Titan X (Full run on 1 GPU ~ 32 hrs)
  • Basic automatic differentiation support
  • Framework for visualization
  • Swappable hardware backends: write code once and deploy on CPUs, GPUs, or Nervana hardware

New features in this release:

  • Python2/Python3 compatibility [#191]
  • Support for Pascal GPUs
  • Persistent RNN kernels [#262]
  • Dataloader enhancements (audio loader with examples)
  • HDF5 file data iterator
  • Convolution kernel improvements
  • Winograd kernel for fprop/bprop and 5x5 stride 1 filters
  • API documentation improvements [#234, #244, #263]
  • Cache directory cleanup
  • Reorganization of all unit tests
  • and many more.

We use neon internally at Nervana to solve our customers’ problems in many domains. Consider joining us. We are hiring across several roles. Apply here!