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]
SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including
... [More] convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning. [Less]
Fast, flexible and fun neural networks.
Combining lessons from previous projects with new design elements, and written entirely in Python, Brainstorm has been designed to work on multiple platforms with multiple computing backends.