On the Nvidia Xavier AGX A false is returned with the command torch.cuda.is_available().
Here are the python commands



import torch
torch.version
‘1.9.0’




x = torch.rand(5, 3)
print(x)
tensor([[0.2251, 0.7957, 0.5024],
[0.0310, 0.7917, 0.1989],
[0.5037, 0.4068, 0.3340],
[0.0275, 0.4699, 0.5500],
[0.8979, 0.1096, 0.2719]])




torch.version.cuda
torch.cuda.is_available()
False



*********** At the Ubuntu prompt I get this for Cuda
Command: cat /usr/local/cuda/version.txt
Returns:     CUDA Version 10.2.300

Command: ./deviceQuery
Returns:
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: “Xavier”
CUDA Driver Version / Runtime Version          10.2 / 10.2
CUDA Capability Major/Minor version number:    7.2
Total amount of global memory:                 15817 MBytes (16584876032 bytes)
( 8) Multiprocessors, ( 64) CUDA Cores/MP:     512 CUDA Cores
GPU Max Clock rate:                            1377 MHz (1.38 GHz)
Memory Clock rate:                             1377 Mhz
Memory Bus Width:                              256-bit
L2 Cache Size:                                 524288 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 number of registers available per block: 65536
Warp size:                                     32
Maximum number of threads per multiprocessor:  2048
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:                     No
Integrated GPU sharing Host Memory:            Yes
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 Compute Preemption:            Yes
Supports Cooperative Kernel Launch:            Yes
Supports MultiDevice Co-op Kernel Launch:      Yes
Device PCI Domain ID / Bus ID / location ID:   0 / 0 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS

I am using Python 3.6
Torch 1.9.0
torchaudio   0.10.0
torchvision   0.10.0
Cuda 10.2
pandas                        1.1.5
numpy                         1.19.4
Any help would be greatly appreciated