Introduction to the Neon Project
The Neon project, once maintained by Intel, has been a deep learning framework known for delivering top-notch performance across various hardware setups. While Intel has ceased its official support and maintenance, the framework still holds significant value for users interested in deep learning and provides opportunities for those who wish to continue its development independently.
Framework Overview
Neon, found on GitHub, was designed with a focus on ease of use and adaptability. It's noteworthy for:
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Comprehensive Tutorials: To help users get started, Neon offers detailed tutorials and iPython notebooks, making it accessible for beginners in deep learning.
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Support for Popular Layer Types: Neon includes support for layers such as convolution, recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), batch normalization (BatchNorm), among others.
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Swappable Hardware Backends: One of Neon's distinguishing features is its ability to execute on different hardware backends like CPUs, GPUs, and specific Nervana devices without needing to change the code.
Performance Features
Neon claims to deliver some of the fastest performance metrics available in deep learning, even outperforming cuDNNv4 by doubling its speed. Specific performance examples include:
- Processing a macrobatch of 3072 images in 2.5 seconds on a Titan X using the AlexNet model.
- Training the VGG model using 16-bit floating points on a single Titan X can take approximately 10 days.
These optimizations make Neon a competitive choice for rapid prototyping and testing of machine learning models.
Model Zoo
Neon's Model Zoo is a repository filled with pre-trained models and example scripts, featuring state-of-the-art models like VGG for image classification, deep reinforcement learning models, image captioning frameworks, and tools for sentiment analysis. This resource is particularly useful for users who want to explore or use advanced models without training from scratch.
Installation and Use
Neon can be installed on various operating systems, with instructions available for local installations and dependencies. Alternatively, it can be installed using the PyPI package manager. It's important to note that the Aeon data pipeline needs a separate installation.
Example Execution and Backend Selection
For running examples, Neon supports script-based execution with options to select different backends directly from the command line. Users can choose between GPU optimization, MKL-optimized CPU, or non-optimized CPU backends for running their models.
Recommended Settings
To maximize performance on Intel architectures, specific settings for the Intel Math Kernel Library (MKL) are recommended, especially when working with systems supporting hyperthreading.
Comprehensive Documentation
Users are supported by extensive documentation, covering everything from introductory tutorials and workflow overviews, to detailed API explanations. This documentation is a critical resource for both beginners and experienced users alike, aiding them in utilizing the full power of the Neon framework.
Support and Community Engagement
The Neon project encourages community engagement through submitting issues or contributing via pull requests on their GitHub repository. For broader discussions, users can join the Neon-users Google group.
Licensing
Neon is open-source, released under the Apache 2.0 License, allowing users the freedom to modify and distribute their adaptations of the project, fostering an environment of open collaboration and innovation.
Since Intel no longer maintains the Neon project, users are encouraged to fork the project for personal needs or community development, continuing the legacy of high-performance deep learning.