![]() We'll install the driver with apt-get in the next step. When running the installer, make sure to not install the driver that comes with CUDA. ![]() Thus, you have to install with the runfile, to opt-out of installing the driver. As a result, installing CUDA from apt-get doesn't work since it installs this driver version. I couldn't just install CUDA and have it work, since certain CUDA version (e.g., 8.0) come with a driver version (in the case of CUDA 8.0, driver version 375.26) that doesn't support the GTX 1080 Ti and other newer cards. Looks like driver version 381 is out of beta and on the PPA, so I've updated the recommended driver versions and install instructions accordingly. If you want to use CUDA 8 for some reason (e.g. You can easily install CUDA 9 on most Linux distributions with your package manager ( see here for details). If you install CUDA 9, the driver version that comes with it should be fully compatible with the 1080 Ti. Tensorflow 1.5.0 and PyTorch 0.3 now have pre-built binaries for CUDA 9. I've updated the install instructions to use driver version 410 (necessary for CUDA 10, but should retain backwards compatibility with older CUDA versions). The same tricks should also work for the newer Titan Xp graphics card. Tensorflow and PyTorch since the card (as of this writing) is relatively new, the process was pretty involved. I recently had to figure out how to set up a new Ubuntu 16.04 machine with NVIDIA's new GTX 1080 Ti graphics card for use with CUDA-enabled machine learning libraries, e.g. Installing and Updating GTX 1080 Ti Drivers / CUDA on Ubuntu Apmachine learning, python, nvidia, CUDA, drivers, tensorflow
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