# neon¶

Release: 1.4.0+bc196cb April 29, 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 recent releases:

• Winograd algorithm for faster convolutions (up to 2x)
• Kepler GPU support
• Greatly expanded model zoo now featuring deep residual nets for image classification, fast-RCNN for object localization, C3D video action recognition.
• 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!

Note

This release includes 3x3 convolution kernels based on the the Winograd minimal filtering algorithm for up to a 2x algorithmic performance gain. Winograd is enabled by default, but can be controlled with the backend setting enable_winograd. When Winograd is enabled, the first time users run a network on the GPU, an autotuning process will compute the fastest kernel parameters. The results are cached in ~/nervana/cache folder for reuse.

The backend can also be configured to prefer 2x2 compared to 4x4 transform sizes through the enable_winograd option (0 = disabled (direct convolution), 2 = prefer 2x2, 4 = prefer 4x4).