深度学习框架
深度学习框架
Deep Learning Frameworks
The NVIDIA Deep Learning SDK accelerates widely-used deep learning frameworks
such as Caffe, CNTK, TensorFlow, Theano and Torch as well as many other deep
learning applications. Choose a deep learning framework from the list below,
download the supported version of cuDNN and follow the instructions on the
framework page to get started.
Caffe is a deep learning framework made with expression, speed, and modularity
in mind. Caffe is developed by the Berkeley Vision and Learning Center (BVLC),
as well as community contributors and is popular for computer vision.
Caffe supports cuDNN v5 for GPU
acceleration.
Supported interfaces: C, C++, Python, MATLAB, Command line interface
Learning Resources
- Deep learning course: Getting Started with the Caffe Framework
- Blog: Deep Learning for Computer Vision with Caffe and cuDNN
The Computational Network Toolkit (CNTK) is a unified deep-learning toolkit
from Microsoft Research that makes it easy to train and combine popular model
types across multiple GPUs and servers. CNTK implements highly efficient CNN
and RNN training for speech, image and text data.
CNTK supports cuDNN v5.1 for GPU
acceleration.
Supported interfaces: C++, Command line interface
[Download CNTK](https://github.com/Microsoft/CNTK/wiki/Setup-CNTK-on-your-
machine) Download cuDNN
TensorFlow is a software library for numerical computation using data flow
graphs, developed by Google’s Machine Intelligence research organization.
TensorFlow supports cuDNN v5.1 for GPU
acceleration.
Supported interfaces: C++, Python
[Download TensorFlow](http://www.nvidia.com/object/gpu-accelerated-
applications-tensorflow-installation.html) Download
cuDNN
Theano is a math expression compiler that efficiently defines, optimizes, and
evaluates mathematical expressions involving multi-dimensional arrays.
Theano supports cuDNN v5 for GPU
acceleration.
Supported interfaces: Python
Learning resources
- Deep learning course: Getting Started with the Theano Framework
Torch
is a scientific computing framework that offers wide support for machine
learning algorithms.
Torch supports cuDNN v5 for GPU
acceleration.
Supported interfaces: C, C++, Lua
Learning resources
- Deep learning course: Getting Started with the Torch Framework
- Blog: Understanding Natural Language with Deep Neural Networks Using Torch
MXnet
is a deep learning framework designed for both efficiency and flexibility that
allows you to mix the flavors of symbolic programming and imperative
programming to maximize efficiency and productivity.
MXnet supports cuDNN v3 for GPU
acceleration.
Supported Interfaces: Python, R, C++, Julia
Chainer
is a deep learning framework that’s designed on the principle of define-by-
run. Unlike frameworks that use the define-and-run approach, Chainer lets you
modify networks during runtime, allowing you to use arbitrary control flow
statements.
Chainer supports cuDNN v4 for GPU
acceleration.
Supported Interfaces: Python
Download Chainer Download
cuDNN
Keras is a minimalist, highly modular neural
networks library, written in Python, and capable of running on top of either
TensorFlow or Theano. Keras was developed with a focus on enabling fast
experimentation.
cuDNN version depends on the version of TensorFlow and Theano installed with
Keras.
Supported Interfaces: Python
More frameworks
There are several other deep learning frameworks that leverage the Deep
Learning SDK, including BidMach,
Brainstorm,
Kaldi,
MatConvNet,
MaxDNN,
Deeplearning4j, Keras,
Lasagne(Theano),
Leaf, and more.
If you’re a framework developer and would like to see your framework listed
here, please get in touch with us at
deeplearning@nvidia.com
