Thank you for reply.
I have reinstalled from following link,
pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
However, the situation is the same.
Following is the result of deviceQuery of CUDA. It suggest there is surely a GPU with driver version 11.6/ runtime version 11.5 …
 ./deviceQuery
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "NVIDIA GeForce RTX 3080 Laptop GPU"
  CUDA Driver Version / Runtime Version          11.6 / 11.5
  CUDA Capability Major/Minor version number:    8.6
  Total amount of global memory:                 16384 MBytes (17179344896 bytes)
  (048) Multiprocessors, (128) CUDA Cores/MP:    6144 CUDA Cores
  GPU Max Clock rate:                            1245 MHz (1.25 GHz)
  Memory Clock rate:                             6001 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 4194304 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total shared memory per multiprocessor:        102400 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  1536
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Managed Memory:                Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.6, CUDA Runtime Version = 11.5, NumDevs = 1
Result = PASS

Followings  are current ideas  that may be causing the issue. How do you think?


Cuda toolkit 11.5 is not supported by Pytroch, so need downgrading of the toolkit to 11.3


There are different similar directories in /usr/local/ and Pytorch is confusing which to use.   There are “cuda”,  “cuda-11”, and  “cuda-11.5”  folders  in /usr/local