deviceQuery works on host but not docker container

[0xAl3xH]

On my host, I am able to run deviceQuery:

/usr/bin/cuda-samples/deviceQuery Starting...

 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:                 7772 MBytes (8149360640 bytes)
  ( 6) Multiprocessors, ( 64) CUDA Cores/MP:     384 CUDA Cores
  GPU Max Clock rate:                            1109 MHz (1.11 GHz)
  Memory Clock rate:                             1109 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'm runningmeta-tegra branch kirkstone-l4t-r32.7.x which has Linux4Tegra release: R32.7.4 and JetPack release: 4.6.4. Using the l4t-cuda docker image nvcr.io/nvidia/l4t-cuda:10.2.460-runtime I am unable to run deviceQuery even though I am passing --runtime nvidia.

# docker run -it --runtime nvidia ac921ae1d2ac bash
# ./deviceQuery
./deviceQuery Starting...

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

cudaGetDeviceCount returned 35
-> CUDA driver version is insufficient for CUDA runtime version
Result = FAIL

Any ideas what may be wrong?

[madisongh]

I don't think that the container support in the kirkstone-l4t-r32.7.x branch is fully working, mainly due to #857. The fix for that issue is in kirkstone and later for newer L4T releases, but it did not get back-ported to work with r32.7.x.

That's not the reason for this particular error message, but it will be difficult to try and resolve that problem with the current state of container support in that branch.

[dwalkes]

There's a Work in Progress attempt to port this at #1194 which started the effort to backport but is not yet complete, could use help.

If I were going to work on this any further I'd start by getting a reference example working with stock Nvidia Jetpack 4.6 release, then comparing to the kirkstone deployment from OE4T.