and the fact that people go as far as recompiling tensorflow to support later CUDA versions may be a hint on how this could end. But I would save all my work before attempting this. Once the extraction process finishes, the wizard will start. The progress bar appears to show the files are being saved to the selected location. Added the ability to query GPU NVLink error rates / counts via out of band. #Nvidia cuda toolkit 9.2 installation failed driverSelect a location on your hard drive and click OK. For convenience, the NVIDIA driver is installed as part of the CUDA Toolkit. #Nvidia cuda toolkit 9.2 installation failed installUse the downloaded CUDA 9.2 base installer to install CUDA toolkit and driver packages Install CUDA repository metada sudo dpkg -i cuda-repo-ubuntu16049.2. If NVidia is right about binary compatibility, you may try to simply rename or link your CUDA 9.2 library as a CUDA 9.0 library and it should work. Run the executable file you downloaded from NVIDIA’s website. Remove the existing CUDA installation folder sudo rm -Rf /usr/local/cuda 3. So the reason looks rather vague - he might mean that CUDA 9.1 (and 9.2) requires graphics card driver that are perhaps a bit too recent to be really convenient, but that is an uneducated guess. The answer to why is driver issues in the ones required by 9.1, not many new features we need in cuda 9.1, and a few more minor issues. Even easier to install, the tensorflow-gpu package installed from conda currently comes bundled with CUDA 9.2. It is occasionally the case that a side task. GPU: Any GPU with CUDA Compute Capability 2.0 or higher. to get less scary error messages, making it a more approachable toolkit. And in practice, you will find working non-official pre-built binaries with later versions of CUDA and CuDNN on the net. Operating System: Linux x8664, Linux ppc64le, Linux aarch64 or Windows x8664. Unzip CUDA installer like an archive into a directory, say C:\cuda9.2. If you have multiple versions of CUDA Toolkit installed, CuPy will automatically choose one. So technically, it should not be a problem to support later iterations of a CUDA driver. Current Visual Studio 2017 version is 15.7.3, so I had to download an earlier version from here. NVIDIA CUDA GPU with the Compute Capability 3.0 or larger. The CUDA driver is backward compatible, meaning that applications compiled against a particular version of the CUDA will continue to work on subsequent (later) driver releases.
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