The reason: in both pip installs (1./2.), there is no guarantee that your dependencies work in all settings. #Download nvidia cuda toolkit install#With pip, you can use "CUDA Toolkit" (1.), but you should not! You can also install "cudatoolkit" (2.) with pip, but that is also not recommended. When using anaconda installer ( conda install tensorflow-gpu), you do not need to install the system "CUDA Toolkit" (standalone, meaning outside of Python). EDIT: Please mind that using Anaconda to install tensorflow is recommended, see and a guide at. Tensorflow: with executable (standalone) install + pip / conda tensorflow + tensorflow-gpu: at the moment maximally "CUDA Toolkit" version 10.1 can be installed, see ->.In my probably special case, the installation succeeded only with MKL ON & NINJA OFF. I have succeeded in installing from source only after many tries, see here. Pytorch from source (if you have an older graphics card and you need to build your own pytorch version with all dependencies): with standalone / system CUDA Toolkit and standalone / system cuDNN before you install pytorch, see and a guide at. #Download nvidia cuda toolkit how to#You can get this done with pip, but setting it up like this is for example not recommended by pytorch who recommend conda: "Anaconda is our recommended package manager since it installs all dependencies." And since conda cannot use the "CUDA Toolkit", see How to run pytorch with NVIDIA "cuda toolkit" version instead of the official conda "cudatoolkit" version?, using "CUDA Toolkit" is not recommended either, which should mean the same for Tensorflow - and it does, see the last bullet point.
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