5 projects found that use cuda as the programming language.

Logo Somoclu 1.7.2

by peterwittek - November 24, 2016, 22:43:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 23991 views, 4377 downloads, 3 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, Julia, R, and MATLAB are supported.

  • New: The coefficient of the Gaussian neighborhood function exp(-||x-y||^2/(2(coeffradius)^2)) is now exposed in all interfaces as a parameter.
  • New: get_bmu function in the Python interface to get the best matching units given an activation map.
  • Changed: Updated PCA initialization in the Python interface to work with sk-learn 0.18 onwards.
  • Changed: Radii can be float values.
  • Fixed: Only positive values were written back to codebook during update.
  • Fixed: Sparse data is read correctly when there are class labels.

Logo Theano 0.8.1

by jaberg - April 1, 2016, 19:22:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 29802 views, 5041 downloads, 3 subscriptions

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.


Theano 0.8.1 (29th of March, 2016)

* Fix compilation on Mac with CLT 7.3

Theano 0.8 (21th of March, 2016)

We recommend to everyone to upgrade to this version.


* Python 2 and 3 support with the same code base
* Faster optimization
* Integration of CuDNN for better GPU performance
* Many Scan improvements (execution speed up, ...)
* optimizer=fast_compile moves computation to the GPU.
* Better convolution on CPU and GPU. (CorrMM, cudnn, 3d conv, more parameter)
* Interactive visualization of graphs with d3viz
* cnmem (better memory management on GPU)
* BreakpointOp
* Multi-GPU for data parallism via Platoon (https://github.com/mila-udem/platoon/)
* More pooling parameter supported
* Bilinear interpolation of images
* New GPU back-end:

    * Float16 new back-end (need cuda 7.5)
    * Multi dtypes
    * Multi-GPU support in the same process

Logo CURFIL 1.1

by hanschul - August 18, 2014, 13:54:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2845 views, 653 downloads, 1 subscription

About: CURFIL uses NVIDIA CUDA to accelerate random forest training and prediction for RGB and RGB-D images. It focuses on image labelling tasks, such as image segmentation or classification applications. CURFIL allows to search for optimal hyper-parameter configurations (e.g. using the hyperopt) package) by massively decreasing training time.


Initial Announcement on mloss.org.

Logo Caffe 0.9999

by sergeyk - August 9, 2014, 01:57:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12714 views, 2046 downloads, 2 subscriptions

About: Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. We believe that Caffe is the fastest available GPU CNN implementation. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters. Even in CPU mode, computing predictions on an image takes only 20 ms (in batch mode).


LOTS of stuff: https://github.com/BVLC/caffe/releases/tag/v0.9999

Logo GPUML GPUs for kernel machines 4

by balajivasan - February 26, 2010, 18:12:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7411 views, 1378 downloads, 1 subscription

About: GPUML is a library that provides a C/C++ and MATLAB interface for speeding up the computation of the weighted kernel summation and kernel matrix construction on GPU. These computations occur commonly in several machine learning algorithms like kernel density estimation, kernel regression, kernel PCA, etc.


Initial Announcement on mloss.org.