I already have a CUDA toolkit installed, why is conda installing CUDA again?

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I have installed cuda version 11.2 and CUDNN version 8.1 in ubuntu

cnvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Mon_Nov_30_19:08:53_PST_2020
Cuda compilation tools, release 11.2, V11.2.67
Build cuda_11.2.r11.2/compiler.29373293_0

When I installed tensorflow-gpu in conda environment, it is again installing cuda and cudnn.

  • Why is it happening.
  • How to stop conda from installing cuda and cudnn again?
  • Can I just use cuda and cudnn that I have already installed? If yes, how?

enter image description here

cudnn tensorflow2.0 conda tensorflow cuda
1 Answers
  1. Why is it happening?

Conda expects to manage any packages you install and all their dependencies. The intention is that you literally never have to install anything else by hand for any packages they distribute in their own channel. If a GPU accelerated package requires a CUDA runtime, conda will try to select and install a correctly versioned CUDA runtime for the version of the Python package it has selected for installation.

  1. How to stop conda from installing cuda and cudnn again?

You probably can't, or at least can't without winding up with a non-functional Tensorflow installation. But see here -- what conda installs is only the necessary, correctly versioned CUDA runtime components to make their GPU accelerated packages work. All they don't/can't install is a GPU driver for the hardware.

  1. Can I just use cuda and cudnn that I have already installed?

You say you installed CUDA 11.2. If you look at the conda output, you can see that it wants to install a CUDA 10.2 runtime. As you are now fully aware, versioning is critical to Tensorflow and a Tensorflow build requiring CUDA 10.2 won't work with CUDA 11.2. So even if you were to stop conda from performing the dependency installation, there is a version mismatch so it wouldn't work.

  1. If yes, how?

See above.

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