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Table of Contents

Preinstalled

Infonote
You may need to update for NVIDIA

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

FYI, the required NVIDIA driver: https://docs.nvidia.com/deploy/cuda-compatibility/index.htmlTensorflow compatible version:

  1. Determine the specific TensorFlow version.

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

windows#tested_build_configurations

NVIDIA driver

...

  1. windows#gpu

  2. Figure out the compatibility with dependent NVIDA driver.
    Refer to https://

...

  1. docs.nvidia.

...

  1. com/deploy/cuda-compatibility/index.

...

  1. html#binary-compatibility

NVIDIA driver

Checking current version

Code Block
languagepy
# ComCuda Driver version check
$ nvidia-smi
WedThu Mar 04 3 13:2500:2419 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 461442.7262       Driver Version: 461442.7262       CUDA Version: 1110.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GeForce GTX 1060   WDDM  | 00000000:01:00.0  On |                  N/A |
| N/A   52C49C    P8     5W4W /  N/A |    495MiB831MiB /  6144MiB |      3%0%      Default | |        
                      |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
Warning

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

Download: https://www.nvidia.co.kr/Download/index.aspx?lang=kr

Manual installing the CUDA library.

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-sections.

Child pages (Children Display)

CUDA Toolkit

Download: https://developer.nvidia.com/cuda-toolkit-archive

Warning

If NVIDA driver has been already installed as what is satisfied, unselect the driver into the custom install step.

cuDNN

Download:  https://developer.nvidia.com/rdp/cudnn-archive

Installation: https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installwindows