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Pre-check up
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Firstly, You have to determine which versions you want to use for each library and NVIDA driver. Determine the specific TensorFlow version. Figure out the compatibility with dependent libraries.
Refer to https://www.tensorflow.org/install/source_windows#gpu Figure out the compatibility with dependent NVIDA driver.
Refer to https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility
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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 |
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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 |
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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. |
CUDA Toolkit
Download: https://developer.nvidia.com/cuda-toolkit-archive
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