Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Cen. Deep learning framework by BAIR. Created by Yangqing Jia Lead Developer Evan Shelhamer. View On GitHub; Installation. Prior to installing, have a glance through this guide and take note of the details for your platform. • Contents • 1. Building the solution Building the solution Now it is time to compile Caffe from source. Loading the solution in Visual Studio 2013 Start your Visual Studio 2013 (you may need to do this as Administrator if you faced permission problems for accessing file in the C: drive). Now from File -> Open -> Project/Solutoin load the Caffe.sln file located in C: projects caffe windows folder. After the solution has been loaded, the Solution Explorer window should look like the following image. Solution Explorer window when Caffe is loaded. Editing compile options To tell Visual Studio what to build for us (e.g. CPU, GPU, cuDNN, Matlab and Python support) you only need to edit the CommonSettings.props (highlighted in the above image) file. I personally do not care about the Matlab and Python wrappers, but if you would like to have them, follow the guide of the authors: Python To build Caffe Python wrapper set PythonSupport to true in. Windows CommonSettings.props. Download Miniconda 2.7 64-bit Windows installer. Install for all users and add Python to PATH (through installer). Run the following commands from elevated command prompt. Remark After you have built solution with Python support, in order to use it you have to either: • set PythonPath environment variable to point to Build x64 Release pycaffe, or • copy folder Build x64 Release pycaffe caffe under lib site-packages. Matlab To build Caffe Matlab wrapper set MatlabSupport to true and MatlabDir to the root of your Matlab installation in. Windows CommonSettings.props. Remark After you have built solution with Matlab support, in order to use it you have to: • add the generated matcaffe folder to Matlab search path, and • add Build x64 Release to your system path. Back to our own journey, to compile Caffe with CUDA and cuDNN you need to make the following changes in the CommonSettings.props file: • Set the to false • Set the to true • Set the correct GPU Architecture by editing the tag. In my case, I am using a GTX 770 graphics card and I am good with these options: compute_30,sm_30 You can add pairs of compute _xx and sm_xx architectures separated by ‘;‘ characters. If you are not sure what shader model and compute capabilities your graphics card has, you can learn more at. • Give the following path for the option: C: Program Files NVIDIA GPU Computing Toolkit CUDA v7.5 because as you can see further more in this file, it always appends cuda to this directory. To make it short, your first few lines of CommonSettings.props file should look like this. Build false true 7.5 false false compute_30,sm_30;compute_52,sm_52 C: Program Files NVIDIA GPU Computing Toolkit CUDA v7.5 $(SolutionDir) scripts. Now, make sure that your build mode is set to Debug and the target architecture is set to x64. Then just press F6 or from Build menu select the Build Solution option. After about 20 minutes (on a Core i5/4670) you should be done! At the end of build process, the resulted executable and library files will be generated in C: projects caffe Build x64 Debug folder. Now you can start using Caffe! I also recommend to add this directory to your Path environment variable. I am planning to run a simple image pattern recognition using Caffe. If I succeeded, I will add a tutorial here in my website! If you faced any problems, let me know in the comment section or in the dedicated. 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