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neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu backend is selected by default, so the above command is equivalent to if a compatible GPU resource is found on the system. The Intel Math Kernel Library takes advantages of the parallelization and vectorization capabilities of Intel Xeon and Xeon Phi systems. When hyperthreading is enabled on the system, we recommend the following KMP_AFFINITY setting to make sure parallel threads are 1:1 mapped to the available physical cores.
Features
- Tutorials and iPython notebooks to get users started with using neon for deep learning
- Support for commonly used layers: convolution, RNN, LSTM, GRU, BatchNorm, and more
- Model Zoo contains pre-trained weights and example scripts for start-of-the-art models
- VGG, Reinforcement learning, Deep Residual Networks, Image Captioning, Sentiment analysis, and more
- Swappable hardware backends
- Write code once and then deploy on CPUs, GPUs, or Nervana hardware
Project Samples
