Date:Dec 28, 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:

  • Skip Thought Vectors ( example
  • Dilated convolution support
  • Nesterov Accelerated Gradient option to SGD optimizer
  • MultiMetric class to allow wrapping Metric classes
  • Support for serializing and deserializing encoder-decoder models
  • Allow specifying the number of time steps to evaluate during beam search
  • A new community-contributed Docker image
  • Improved error messages when a tensor is created with an invalid shape or reshaped to an incompatible size
  • Fix bugs in MultiCost support
  • Documentation fixes [#331]
  • See 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!