![]() I had no clue where the libraries would be installed, so I called my old friend find to the rescue. Too bad: still no luck! Specify where to find libnvcuvid Makefile:52: recipe for target '3_Imaging/cudaDecodeGL/Makefile.ph_build' failed ![]() Make: Leaving directory '/home/maarten/NVIDIA_CUDA-8.0_Samples/3_Imaging/cudaDecodeGL' Makefile:381: recipe for target 'cudaDecodeGL' failed I cd‘ed into that folder and issued make.Īfter a long wait (skipped here for brevity), I got nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).Ĭollect2: error: ld returned 1 exit status You need to give it a target directory to copy the samples to I choose. The CUDA toolkit comes with some sample code, which can be copied to a directory of your choice by running the cuda-install-samples-8.0.sh script, found in /usr/local/cuda-8.0/bin/. I don’t feel like writing C-code for the graphics card myself, but that isn’t necessary either. See if the compiler can actually compile code Running nvcc -V told me it wasn’t installed, but I could install it installing the nvidia-cuda-toolkit package. The CUDA Toolkit comes with the NVIDIA CUDA Compiler, or nvcc for short. Now comes the hardest part: trying to get it all to work. etc/modprobe.d/nf and put the following in it: blacklist nouveauĪfter that, you need to make sure the initial kernel image is also updated: sudo update-initramfs -u.įinally, I did a reboot, just to be sure, but I don’t think it is really necessary. To blacklist them (meaning the Linux kernel will never load them), I created a file at If you want to use CUDA, you cannot use the open-source Nouveau drivers for NVIDIA graphics cards. Installing the toolkit is pretty straightforward, and it listed on the download page as well: It will probably work just fine, but I prefer this approach. The deb (local) will download everything upfront, and then you have to install another patch. In case Nvidia decides to release updates to the toolkit, I hope this approach will make it easier to get them. I choose the deb (network) installer since it is the smallest to download and it will configure APT repositories for you. DownloadingĪfter that, I headed to the CUDA Toolkit download page and make the following choices: I choose Ubuntu 16.04 since it is an LTS release, which means it will still receive security patches, and since it is officially supported by NVIDIA. Since I have had good experiences with Ubuntu, and the machine had an old version of Ubuntu installed, I upgraded that to the latest Long Term Support (LTS) release: 16.04.2 at the time of writing. Preparationsīefore even thinking of installing something, I had to make sure my machine was running a supported operating system. ![]() If you’re up for a journey, continue reading…. Luckily, NVIDIA distributes the CUDA toolkit which lets you do that. Only problem is: you can’t write arbitrary code and just run it on a graphics card. Sure, it isn’t a high-end card for todays standards, but at least it might be fun to try it. To give my old workstation annex gaming PC a new meaning in life, why not try to employ its NVIDIA GT218 for some experiments? When I was at university, I followed some courses and specialisations in this field, but then during my career I hardly ever used any of it.īack in those years, complex neural nets and genetic algorithms took days to build, mainly because we didn’t have the computing power for that.īut nowadays, things have changed, and such models can relatively quickly be built using a commodity graphics card. As such, moving up to CUDA 7.0 probably won’t happen anytime soon, as NVidia dropped support for 32-bit applications with 7.0.Lately, my interest for machine learning and artificial intelligence has revived. We try to maintain a balance between keeping Triton’s dependencies current, while preserving compatibility with older compilers and architectures. This means your end users must have driver version 340.29 or newer installed on their NVidia-based systems, in order for Triton to perform its best.The main benefit is that CUDA 6.5 will let you build Triton’s CUDA DLL under Visual Studio 2013 natively, without requiring Visual Studio 2012 to be installed as well. If you’re a licensed user of Triton and are building it from source, you’ll want to install NVidia’s CUDA Toolkit 6.5 on your build system now.This is important for a couple of reasons: CUDA is the technology Triton uses to accelerate its wave equations on NVidia graphics cards, by spreading that computation out among the thousands of cores on your GPU. ![]() As of version 3.26, the Triton Ocean SDK is now built using NVidia’s CUDA Toolkit version 6.5 instead of 6.0.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |