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Table of Contents

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Pre-check up

Note

Firstly, You have to determine which versions you want to use for each library and NVIDA driver.

  1. Determine the specific TensorFlow version.

  2. Figure out the compatibility with dependent libraries.
    Refer to https://www.tensorflow.org/install/source_windows#gpu

  3. Figure out the compatibility with dependent NVIDA driver.
    Refer to https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility

If the versions of CUDA of what you gonna use are satisfied with NVIDA driver, you can skip this page.
See the next sub-pages.

NVIDIA driver

Checking current version

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Warning

This Driver is included within CUDA toolkit of the next section.
Before installing the driver you have to check out the above “PreinstalledPre-check up” section.
If not have to do it, you’d better you don’t install the driver.

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Info

You can install CUDA library with conda.
Furthermore, you can do several cuda versions independently.
It’s more convenient and has some advantages, you’d better use conda if there are not any other matters.

The way of using conda, you can see the next sub-sectionspages.

Child pages (Children Display)

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