![]() Please make sure that the kernel source packages are installed and set up correctly. When you tried to run the base installer (cuda_n) you received this lovely message (specially on EC2 instances) : " The driver installation is unable to locate the kernel source.You got stuck in “login loop” after installing Nvidia Driver.You were greeted by a black screen after installing Nvidia Driver.Some of the CUDA samples use other libraries such as OpenMP or MPI or OpenGL. Note: the Optical Flow sample (HSOpticalFlow) and 3D stereo sample (stereoDisparity) take rouglhy 1 minute each to execute since they compare results with CPU code. eg:Ĭd ~/NVIDIA_CUDA-6.5_Samples/2_Graphics/simpleGLĬd ~/NVIDIA_CUDA-6.5_Samples/3_Imaging/bicubicTextureĬd ~/NVIDIA_CUDA-6.5_Samples/3_Imaging/bilateralFilter (This will only work if you are using a Linux/Unix machine or you run an X server such as the free "Xming" for Windows). If you are running these programs through an SSH remote terminal, you can remotely display the windows on your desktop by typing "export DISPLAY=:0" and then executing the program. Note: Many of the CUDA samples use OpenGL GLX and open graphical windows. ![]() Install writeable copies of the CUDA samples to your device's home directory (it will create a "NVIDIA_CUDA-6.5_Samples" folder):īuild the CUDA samples (takes around 15 minutes on Jetson TK1): ![]() If you think you will write your own CUDA code or you want to see what CUDA can do, then follow this section to build & run all of the CUDA samples. Installing & running the CUDA samples (optional) (note that the above flag is a capital "V" not lower-case "v"). Verify that the CUDA Toolkit is installed on your device: bashrc login script, and start using it in your current console:Įcho "# Add CUDA bin & library paths:" > ~/.bashrcĮcho "export PATH=/usr/local/cuda/bin:$PATH" > ~/.bashrcĮcho "export LD_LIBRARY_PATH=/usr/local/cuda/lib:$LD_LIBRARY_PATH" > ~/.bashrc # Add yourself to the "video" group to allow access to the GPUĪdd the 32-bit CUDA paths to your. # Install the package full of CUDA samples (optional) # Install "cuda-toolkit-6-0" if you downloaded CUDA 6.0, or "cuda-toolkit-6-5" if you downloaded CUDA 6.5, etc. # Download & install the actual CUDA Toolkit including the OpenGL toolkit from NVIDIA. # Install the CUDA repo metadata that you downloaded manually for L4T Some more direct links for Jetson TK1: CUDA 6.5 Toolkit for L4T Rel 21.2, and CUDA 6.5 Toolkit including the CUDA 6.5 Getting Started Linux Guide. (Make sure you download the Toolkit for L4T and not the Toolkit for Ubuntu since that is for cross-compilation instead of native compilation). deb file for the CUDA Toolkit for L4T either using a web browser on the device, or download on your PC then copy the file to your device using a USB flash stick or across the network. Installing the CUDA Toolkit onto your device for native CUDA developmentĭownload the. ![]() In comparison, native compilation happens onboard the Jetson device and thus is the same no matter which OS or desktop you have. The CUDA Toolkit currently only supports cross-compilation from an Ubuntu 12.04 or 14.04 Linux desktop. Native compilation is generally the easiest option, but takes longer to compile, whereas cross-compilation is typically more complex to configure and debug, but for large projects it will be noticeably faster at compiling. cross-compilation (compiling code on an x86 desktop in a special way so it can execute on the Jetson TK1 target device).native compilation (compiling code onboard the Jetson TK1).You have two options for developing CUDA applications for Jetson TK1: ![]()
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