ML/DL
- Jack kim
Pre-check up
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#gpuFigure 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
$ nvidia-smi
Thu Mar 04 13:00:19 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 442.62 Driver Version: 442.62 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 1060 WDDM | 00000000:01:00.0 On | N/A |
| N/A 49C P8 4W / N/A | 831MiB / 6144MiB | 0% Default |
+-------------------------------+----------------------+----------------------+This Driver is included within CUDA toolkit of the next section.
Before installing the driver you have to check out the above “Pre-check up” 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.
CUDA Toolkit
Download: https://developer.nvidia.com/cuda-toolkit-archive
cuDNN
Download: https://developer.nvidia.com/rdp/cudnn-archive
Installation: https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installwindows