Metadata-Version: 1.1
Name: neon
Version: 1.5.4
Summary: Nervana's deep learning framework
Home-page: http://www.nervanasys.com
Author: Nervana Systems
Author-email: info@nervanasys.com
License: License :: OSI Approved :: Apache Software License
Description: # neon
        
        [neon](https://github.com/NervanaSystems/neon) is Nervana's Python based
        Deep Learning framework and achieves the [fastest performance](https://github.com/soumith/convnet-benchmarks) on modern deep neural networks such as AlexNet, VGG and GoogLeNet. Designed for ease-of-use and extensibility.
        
        * [Tutorials](http://neon.nervanasys.com/docs/latest/tutorials.html) and [iPython notebooks](https://github.com/NervanaSystems/meetup) to get users started with using neon for deep learning.
        * Support for commonly used layers: convolution, RNN, LSTM, GRU, BatchNorm, and more.
        * [Model Zoo](https://github.com/nervanazoo/NervanaModelZoo) contains pre-trained weights and example scripts for start-of-the-art models, including: [VGG](https://github.com/nervanazoo/NervanaModelZoo/tree/master/ImageClassification/ILSVRC2012/VGG), [Reinforcement learning](https://github.com/nervanazoo/NervanaModelZoo/tree/master/DeepReinforcement), [Deep Residual Networks](https://github.com/nervanazoo/NervanaModelZoo/tree/master/SceneClassification/DeepResNet), [Image Captioning](https://github.com/nervanazoo/NervanaModelZoo/tree/master/ImageCaptioning), [Sentiment analysis](https://github.com/nervanazoo/NervanaModelZoo/tree/master/NLP/SentimentClassification/IMDB), and [more](http://neon.nervanasys.com/docs/latest/model_zoo.html).
        * Swappable hardware backends: write code once and then deploy on CPUs, GPUs, or Nervana hardware
        
        For fast iteration and model exploration, neon has the fastest performance among deep learning libraries (2x speed of cuDNNv4, see [benchmarks](https://github.com/soumith/convnet-benchmarks)).
        * 2.5s/macrobatch (3072 images) on AlexNet on Titan X (Full run on 1 GPU ~ 26 hrs)
        * Training VGG with 16-bit floating point on 1 Titan X takes ~10 days (original paper: 4 GPUs for 2-3 weeks)
        
        We use neon internally at Nervana to solve our customers' problems across many
        [domains](http://www.nervanasys.com/solutions/). We are hiring across several
        roles. Apply [here](http://www.nervanasys.com/careers/)!
        
        See the [new features](https://github.com/NervanaSystems/neon/blob/master/ChangeLog) in our latest release.
        
        ## Quick Install
        
        * [Local install and dependencies](http://neon.nervanasys.com/docs/latest/installation.html)
        
        On a Mac OSX or Linux machine, enter the following to download and install
        neon (conda users see the [guide](http://neon.nervanasys.com/docs/latest/installation.html)), and use it to train your first multi-layer perceptron. To force a python2 or python3 install, replace `make` below with either `make python2` or `make python3`.
        
        ```bash
            git clone https://github.com/NervanaSystems/neon.git
            cd neon
            make
            . .venv/bin/activate
            neon examples/mnist_mlp.yaml
            # alternatively, use a script:
            python examples/mnist_mlp.py
        ```
        
        ## Documentation
        
        The complete documentation for neon is available
        [here](http://neon.nervanasys.com/docs/latest). Some useful starting points are:
        
        * [Tutorials](http://neon.nervanasys.com/docs/latest/tutorials.html) for neon
        * [Overview](http://neon.nervanasys.com/docs/latest/overview.html) of the neon workflow
        * [API](http://neon.nervanasys.com/docs/latest/api.html) documentation
        * [Resources](http://neon.nervanasys.com/docs/latest/resources.html) for neon and deep learning
        
        
        ## Support
        
        For any bugs or feature requests please:
        
        1. Search the open and closed
           [issues list](https://github.com/NervanaSystems/neon/issues) to see if we're
           already working on what you have uncovered.
        2. Check that your issue/request hasn't already been addressed in our
           [Frequently Asked Questions (FAQ)](http://neon.nervanasys.com/docs/latest/faq.html)
           or [neon-users](https://groups.google.com/forum/#!forum/neon-users) Google
           group.
        3. File a new [issue](https://github.com/NervanaSystems/neon/issues) or submit
           a new [pull request](https://github.com/NervanaSystems/neon/pulls) if you
           have some code you'd like to contribute
        
        For other questions and discussions please post a message to the
           [neon-users](https://groups.google.com/forum/?hl=en#!forum/neon-users)
           Google group
        
        ## License
        
        We are releasing [neon](https://github.com/NervanaSystems/neon) under an open source
        [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) License. We welcome you to [contact us](mailto:info@nervanasys.com) with your use cases.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Environment :: Console :: Curses
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: System :: Distributed Computing
