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TensorFlow

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  Analyzed about 9 hours ago

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy ... [More] computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. 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, but the system is general enough to be applicable in a wide variety of other domains as well. [Less]

3.83M lines of code

798 current contributors

about 22 hours since last commit

23 users on Open Hub

Very High Activity
5.0
 
0 Reviews
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Mostly written in C++
Licenses: apache_2
Tags c++ deep_learning deep_neural_networks flow_graphs GPU gpu_computing machine_learning mobile numerical_computing python

neurokernel

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  Analyzed about 9 hours ago

The Neurokernel Project aims to build an open software platform for the emulation of the entire brain of the fruit fly Drosophila melanogaster on multiple Graphics Processing Units (GPUs).

52.3K lines of code

0 current contributors

4 months since last commit

1 users on Open Hub

Very Low Activity
5.0
 
0 Reviews
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Mostly written in Python
Licenses: BSD-3-Clause
Tags CUDA GPU neuroscience python simulation

scikit-cuda

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  Analyzed about 10 hours ago

scikit-cuda provides Python interfaces to many of the functions in the CUDA device/runtime, CUBLAS, CUFFT, and CUSOLVER libraries distributed as part of NVIDIA's CUDA Programming Toolkit, as well as interfaces to select functions in the free and standard versions of the CULA Dense Toolkit. Both ... [More] low-level wrapper functions similar to their C counterparts and high-level functions comparable to those in NumPy and Scipy are provided. [Less]

20.6K lines of code

3 current contributors

6 months since last commit

1 users on Open Hub

Very Low Activity
0.0
 
0 Reviews
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Mostly written in Python
Licenses: BSD-3-Clause
Tags CUDA GPU numerical_computing nvidia python

CNTK

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  Analyzed about 19 hours ago

CNTK (Computational Network Toolkit) by Microsoft Research, 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 ... [More] operations upon their inputs. CNTK allows 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. [Less]

327K lines of code

26 current contributors

over 1 year since last commit

0 users on Open Hub

Very Low Activity
0.0
 
0 Reviews
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Mostly written in C++
Licenses: No declared licenses
Tags automatic_differentiation cnn convolutional_networks deep_learning deeplearning distributed_computing dnn error_backpropagation feedforward GPU GPU_cluster lstm 7 more...

Deep Scalable Sparse Tensor Network Engine (DSSTNE)

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  Analyzed about 24 hours ago

Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine DSSTNE (pronounced "Destiny") is a library for training and deploying deep neural networks using GPUs. It is build to solve deep learning problems at Amazon's scale. It is built for production deployment of real-world deep learning ... [More] applications, emphasizing speed and scale over experimental flexibility. Multi-GPU Scale: Training and prediction both scale out to use multiple GPUs, spreading out computation and storage in a model-parallel fashion for each layer. Large Layers: Model-parallel scaling enables larger networks than are possible with a single GPU. Sparse Data: DSSTNE is optimized for fast performance on sparse datasets. Custom GPU kernels perform sparse computation on the GPU, without filling in lots of zeroes. [Less]

41K lines of code

6 current contributors

about 4 years since last commit

0 users on Open Hub

Inactive
0.0
 
0 Reviews
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Mostly written in C++
Licenses: apache_2
Tags deep_learning deployment GPU machine_learning multi-gpu neural_networks production scalable sparse

clDNN

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  Analyzed about 2 hours ago

Compute Library for Deep Neural Networks (clDNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL inference on Intel® HD Graphics Driver and Intel® Iris® graphics (also referred to as Intel® Processor Graphics). clDNN includes highly ... [More] optimized building blocks to implement convolutional neural networks (CNN) with C and C++ interfaces. We recently introduced the Intel Deep-Learning Deployment Toolkit, as part of the Intel Computer-Vision SDK to provide application developers an easy way to use the DL acceleration capabilities of Intel® Processor Graphics. [Less]

231K lines of code

4 current contributors

over 1 year since last commit

0 users on Open Hub

Very Low Activity
0.0
 
0 Reviews
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Mostly written in C++
Licenses: apache_v2
Tags c++ deep_learning dnn GPU Intel