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:
- Update Data Loader to aeon github.com/NervanaSyst…
- Add Neural Machine Translation model
- Remove Fast RCNN model (use Faster RCNN model instead)
- Remove music_genres example
- Fix super blocking for small N with 1D conv
- Fix update-direct conv kernel for small N
- Add gradient clipping to Adam optimizer
- Documentation updates and bug fixes
- 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!
