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Machine Learning Libraries

CAFFE

http://caffe.berkeleyvision.org/

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley.

Licensing Terms and Conditions

Caffe is released under the BSD 2-Clause license. License

Usage

List available versions

module avail caffe

## ML partition ONLY 
module load swbwl
module avail caffe

CNTK

https://www.microsoft.com/en-us/cognitive-toolkit/

The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

Licensing Terms and Conditions

CNTK has been available under an open-source license since April 2015. It is our hope that the community will take advantage of CNTK to share ideas more quickly through the exchange of open source working code.

Usage

https://docs.microsoft.com/en-us/cognitive-toolkit/

List available versions

## ML partition ONLY 
module load swbwl
module avail cntk

MXNET

Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.

MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

Licensing Terms and Conditions Licensed under an Apache-2.0 license. License

Usage

List available versions

module avail mxnet

## ML partition ONLY 
module load swbwl
module avail mxnet

TENSORFLOW

TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google’s Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Python API and C APIs as well as without API backwards compatibility guarantee like C++, Go, Java, JavaScript and Swift.

Licensing Terms and Conditions Apache License 2.0

Usage

List available versions

module avail tensorflow

## ML partition ONLY 
module load swbwl
module avail tensorflow