I am a bit confused by your answer as my question referred to the necessity of co-existence of both NVIDIA CUDA and conda cudatoolkit installations.
NVIDIA CUDA TOOLKIT VISUAL STUDIO INSTALL
Everything unchecked in Driver components is installed when you install the graphics you for your time! You do not have to do this if you don't want to.Īfter CUDA is installed, install the latest graphics driver for your graphics card from NVIDIA. It keeps the size down and speeds up installation. Visual Studio Integration (unless you will be using Visual Studio ) (in CUDA).You can see in your conda installation you have CUDA 10.0 not 10.1.Ģnd: Check to ensure you have the following paths in your environment variables and above conda's paths:Ĭ:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\\binĬ:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\\extras\CUPTI\lib圆4Ĭ:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\\includeĬ:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\\lib\圆4ĮXTRA: When I install CUDA, I specifically uncheck the following settings (through advanced setup): I know for my build I use CUDA 10.0 with cuDNN v7.50. No, you don't need both there are two probable issues you are encountering:ġst: I don't believe TF supports CUDA 10.1 yet ( I could be wrong). The cudart64-100.dll cannot be found error popping at version mismatch between CUDA/CUDNN, Python and Tensorflow refers to the dll library located in the conda environment and not to the NVIDIA CUDA installation (which in my case contains the cudart64-101.dll and still works) Seems to resolve version mismatch issues encountered with pip install, keras::install_keras() or with tensorflow::install_tensorflow()īeside CUDA, the NVIDIA CUDA Toolkit installs a plethora of tools that may not be used by everyone therefore, a guide to the custom installation of the CUDA Toolkit would be more than welcome Sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))Ĭonda create -n tensorflow tensorflow-gpu Version mismatch issues encountered at the installation of Tensorflow with local GPU support led me question the need for the coexistence on the same machine of both CUDA packages, namely:Īnd the conda installation of the cudatoolkit along with cudnnĪs shown above, on my machine these packages have different versions but tensorflow is configured for GPU support and is working:
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