Deep learning framework with configurable backends
Project description
neon is NErvana’s pythON based Deep Learning Framework! We have designed it with the following functionality in mind:
YAML for easy model specification (inspired by pylearn2)
Python for easily adding models and support for many data formats
Support for commonly used models: convnets, MLPs, RNNs, LSTMs, autoencoders, RBMs
Support for common learning rules, activation functions and cost functions
Comparing performance of alternate numeric representations with 32-bit floating point (fp32) for Deep Learning
Support for using spearmint for hyperparameter optimization
Swappable hardware backends: write code once and then deploy on CPUs, GPUs, or Nervana hardware
Features that are unique to neon include:
Tight integration with nervanagpu kernels for fp16 and fp32 (benchmarks) on Maxwell GPUs. These are the fastest implementations of the benchmark deep networks.
4.3s/macrobatch on AlexNet on Titan X (Full run on 1 GPU ~ 45 hrs)
Out of the box fp16 AlexNet model that has the same accuracy as fp32
Integration with our fork (cudanet) of Alex Krizhevsky’s cuda-convnet2 library for Kepler GPU support
Support for our distributed processor (Nervana Engine™) for deep learning.
We use neon internally at Nervana to solve our customers’ problems across many domains. We are hiring across several roles. Apply here!
Getting started
Basic information to get started is below. Please consult the full documentation for more information.
Installation
Cloud-based access (email us for an account)
Quick Install
On a Mac OSX or Linux box enter the following to download and install neon, and use it to train your first multi-layer perceptron or convolutional neural networks below.
git clone https://github.com/NervanaSystems/neon.git cd neon sudo make install
The above will install neon system-wide. If you don’t have sufficient privileges or would prefer an isolated installation, see our virtualenv based install.
There are several examples built-in to neon in the examples directory for a user to get started. The YAML format is plain-text and can be edited to change various aspects of the model. See the ANNOTATED_EXAMPLE.yaml for some of the definitions and possible choices.
Running a simple MNIST model (on CPU)
neon examples/mlp/mnist-small.yaml
Running an Alexnet model (on GPU)
In fp32:
# for nervangpu (requires Maxwell GPUs) neon --gpu nervanagpu examples/convnet/i1k-alexnet-fp32.yaml # for cudanet (works with Kepler or Maxwell GPUs) neon --gpu cudanet examples/convnet/i1k-alexnet-fp32.yaml
In fp16:
neon --gpu nervanagpu examples/convnet/i1k-alexnet-fp16.yaml
Code organization
backends --- implementation of different hardware backends datasets --- support for common datasets CIFAR-10, ImageNet, MNIST etc. diagnostics --- hooks to measure timing and numeric ranges hyperopt --- hooks for hyperparameter optimization layers --- layer code models --- model code optimizers --- learning rules transforms --- activation & cost functions metrics --- performance evaluation metrics
Documentation
The complete documentation for neon is available here. Some useful starting points are:
Issues
For any bugs or feature requests please:
Search the open and closed issues list to see if we’re already working on what you have uncovered.
Check that your issue/request has already been addressed in our Frequently Asked Questions (FAQ)
File a new issue or submit a new pull request if you have some code you’d like to contribute
Machine learning OPerations (MOP) Layer
The MOP is an abstraction layer for Nervana’s system software and hardware which includes the Nervana Engine, a custom distributed processor for deep learning.
The MOP consists of linear algebra and other operations required by deep learning. Some MOP operations are currently exposed in neon, while others, such as distributed primitives, will be exposed in later versions as well as in other forthcoming Nervana libraries.
Defining models in a MOP-compliant manner guarantees they will run on all provided backends. It also provides a way for existing Deep Learning frameworks such as theano, torch, and caffe to interface with the Nervana Engine.
Upcoming libraries
We have separate, upcoming efforts on the following fronts:
Distributed models
Automatic differentiation
Integration with Nervana Cloud™
License
We are releasing neon and nervanagpu under an open source Apache 2.0 License. We welcome you to contact us with your use cases.
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