First of all, install according to the normal installation method:
the necessary conditions for success are:
1. The version number should be correct, that is, CUDA should be installed above 11.1 (because CUDA version supported by 30 AMP architecture graphics card starts from 11.1)
link: https://developer.nvidia.com/zh-cn/cuda-downloads
2. Cudnn needs to install the, Link (to register and log in to NVIDIA account) https://developer.nvidia.com/zh-cn/cudnn
If you haven’t installed it, you can see other posts https://so.csdn.net/so/search/all?q=3060%20tensorflow& t=all& p=1& s=0& tm=0& lv=-1& ft=0& l=& U =
after installation, enter the created environment and run tf.test.is_ gpu_ available()。
if the computer can detect the graphics card, it can display the number of cores, computing power and other parameters of each graphics card, but the final answer is false
if the command line shows that cusolver64 cannot be found_ 10 documents

, at the following address C:// program files/NVIDIA GPU computing toolkit/CUDA/V11.1/bin

Will cusolver64_ 11. DLL renamed to cusolver64_ 10. Dll
and then run tf.test.is again_ gpu_ available()

Your uncle made it!