Thank you. It is helpful to know Cuda 11.5 is not the issue.
Just for additional information, GPU worked with a CUDA sample code, so the issue seems to be not in CUDA installment itself, but how that is recognized by Pytorch.
/usr/local/cuda/samples/4_Finance/BlackScholes$ ./BlackScholes
[./BlackScholes] - Starting...
GPU Device 0: "Ampere" with compute capability 8.6

Initializing data...
...allocating CPU memory for options.
...allocating GPU memory for options.
...generating input data in CPU mem.
...copying input data to GPU mem.
Data init done.

Executing Black-Scholes GPU kernel (512 iterations)...
Options count             : 8000000
BlackScholesGPU() time    : 0.237344 msec
Effective memory bandwidth: 337.063867 GB/s
Gigaoptions per second    : 33.706387

BlackScholes, Throughput = 33.7064 GOptions/s, Time = 0.00024 s, Size = 8000000 options, NumDevsUsed = 1, Workgroup = 128

Reading back GPU results...
Checking the results...
...running CPU calculations.

Comparing the results...
L1 norm: 1.741792E-07
Max absolute error: 1.192093E-05

Shutting down...
...releasing GPU memory.
...releasing CPU memory.
Shutdown done.

[BlackScholes] - Test Summary

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

Test passed