Date:Jan 17, 2017

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:

  • Bug fix: Add dilation to object dict and assign defaults to dil_w = dil_h = 1 [#335, #336]
  • Bug fix: Prevent GPU backend from ignoring non-zero slope in Rectlinclip and change default slope to 0
  • Bug fix: Nesterov momentum was updating velocities incorrectly
  • See more in the change log.

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!