In this post I am going to use the OpenCV’s performance tests to compare the CUDA and CPU implementations. The idea, is to get an indication of which OpenCV and/or Computer Vision algorithms, in general, benefit the most from GPU acceleration, and therefore, under what circumstances it might be a good idea to invest in […]
Because the pre-built Windows libraries available for OpenCV 3.4 do not include the CUDA modules, or the support for Intel’s Math Kernel Libraries (MKL) or Intel Threaded Building Blocks (TBB) performance libraries, I have included the build instructions, below for anyone who is interested. If you just need the Windows libraries then go to Download […]
OpenCV 3.4 which is compatible with CUDA 9.1 and Visual Studio 2017 was released on 23/12/2017, go to Building OpenCV 3.4 on Windows with CUDA 9.1, Intel MKL+TBB, for the updated guide. Because the pre-built Windows libraries available for OpenCV v3.3 do not include the CUDA modules, I have included the build […]
The below instructions should have you set up with both Keras 1.2.2 and theano 0.9 on Windows 8.1 in 30 minutes or less, depending on the speed of your internet connection. I can confirm that this set up is suitable for all the lessons in the fantastic Practical Deep Learning For Coders, Part 1, course. […]
OpenCV 3.3 was released on 03/08/2017, go to Building OpenCV 3.3 on Windows with CUDA 8.0, Intel MKL+TBB and python bindings, for the updated guide. Because the pre-built Windows libraries available for OpenCV v3.2 do not include the CUDA modules, I have provided them for download here, and included the build instructions […]
After teaching myself CUDA, luckily for me I started on the more recent SDK’s, I wanted to share my experience regarding some of the most useful resources available, to help anyone interested in accelerating their own algorithms, to be as productive as possible. In my opinion the fastest way to learn GPU programming with CUDA, […]