- Release
- Date
neon_ is Intel Nervana 's reference deep learning framework committed to best performance on all hardware. Designed for ease-of-use and extensibility.
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 and Intel CPU MKLML 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:
- Enabled pip install through pypi
- Updated MKLML to version 20171007 with up to 3X performance increase
- Updated resnet model to optimize performance with MKLML 20171007
- Updated Alexnet weight file and fixed bug for deep dream
- Fixed faster-rcnn inference model loading issue
- Added data_loading time measurement and enabled GAN networks benchmarking
- Updated Aeon version to 1.2.0
- Enabled neon build with mklEngine on Windows systems
- See more in the change log.
We use neon internally at Intel Nervana to solve our customers' problems in many domains. Consider joining us. We are hiring across several roles. Apply here!
installation.rst overview.rst running_models.rst
tutorials.rst model_zoo.rst backends.rst
loading_data.rst datasets.rst layers.rst layer_containers.rst activations.rst costs.rst initializers.rst optimizers.rst learning_schedules.rst models.rst callbacks.rst
faq.rst
developer_guide.rst design.rst ml_operational_layer.rst
resources.rst
api.rst
previous_versions.rst