5 projects found that use cuda as the programming language.


Logo Theano 1.0.0

by jaberg - November 16, 2017, 17:42:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 37027 views, 6214 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.

Changes:

Theano 1.0.0 (15th of November, 2017)

Highlights (since 0.9.0):

  • Announcing that MILA will stop developing Theano <https://groups.google.com/d/msg/theano-users/7Poq8BZutbY/rNCIfvAEAwAJ>_

  • conda packages now available and updated in our own conda channel mila-udem To install it: conda install -c mila-udem theano pygpu

  • Support NumPy 1.13

  • Support pygpu 0.7

  • Moved Python 3.* minimum supported version from 3.3 to 3.4

  • Added conda recipe

  • Replaced deprecated package nose-parameterized with up-to-date package parameterized for Theano requirements

  • Theano now internally uses sha256 instead of md5 to work on systems that forbid md5 for security reason

  • Removed old GPU backend theano.sandbox.cuda. New backend theano.gpuarray is now the official GPU backend

  • Make sure MKL uses GNU OpenMP

  • NB: Matrix dot product (gemm) with mkl from conda could return wrong results in some cases. We have reported the problem upstream and we have a work around that raises an error with information about how to fix it.

  • Improved elemwise operations

  • Speed-up elemwise ops based on SciPy

  • Fixed memory leaks related to elemwise ops on GPU

  • Scan improvements

  • Speed up Theano scan compilation and gradient computation

  • Added meaningful message when missing inputs to scan

  • Speed up graph toposort algorithm

  • Faster C compilation by massively using a new interface for op params

  • Faster optimization step, with new optional destroy handler

  • Documentation updated and more complete

  • Added documentation for RNNBlock

  • Updated conv documentation

  • Support more debuggers for PdbBreakpoint

  • Many bug fixes, crash fixes and warning improvements


Logo Somoclu 1.7.4

by peterwittek - June 6, 2017, 15:48:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 32855 views, 5905 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.

Changes:
  • New: Verbosity parameter in the command-line, Python, MATLAB, and Julia interfaces.
  • Changed: Calculation of U-matrix parallelized.
  • Changed: Moved feeding data to train method in the Python interface.
  • Fixed: The random seed was set to 0 for testing purposes. This is now changed to a wall-time based initialization.
  • Fixed: Sparse matrix reader made more robust.
  • Fixed: Compatibility with kohonen 3 resolved.
  • Fixed: Compatibility with Matplotlib 2 resolved.

Logo CURFIL 1.1

by hanschul - August 18, 2014, 13:54:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3473 views, 784 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.

Changes:

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 ] 15299 views, 2441 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).

Changes:

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 ] 8152 views, 1518 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.

Changes:

Initial Announcement on mloss.org.