Upon running nvidia-smi
through terminal, i am met with nvidia-smi command not found
However, i am aware that jetpack 3.3 (the nvidia drivers) have already been installed.
Has anyone encountered similar problems with Nvidia jetson tx2 ?
System specs:
DJI Manifold 2G (Nvidia Jetson TX2)
Jetpack 3.3.0
ARMv8 Processor rev 3 (v8l) × 4 ARMv8 Processor rev 0 (v8l) × 2
NVIDIA Tegra X2 (nvgpu)/integrated
8GB ram, Ubuntu 16.04 LTS
UPDATE AND EDIT (SOLVED): While nvidia-smi does not run, One of the answers posted below by user @SeB helped. So after getting the ./deviceQuery executable made, one can see the following. Which tells you the details of your GPU
/usr/local/cuda/samples/1_Utilities/deviceQuery$ ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "NVIDIA Tegra X2"
CUDA Driver Version / Runtime Version 9.0 / 9.0
CUDA Capability Major/Minor version number: 6.2
Total amount of global memory: 7839 MBytes (8219348992 bytes)
( 2) Multiprocessors, (128) CUDA Cores/MP: 256 CUDA Cores
GPU Max Clock rate: 1301 MHz (1.30 GHz)
Memory Clock rate: 1600 Mhz
Memory Bus Width: 128-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: 32768
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
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 = 9.0, CUDA Runtime Version = 9.0, NumDevs = 1
Result = PASS
I think that nvidia-smi is only available so far for NVIDIA discrete GPUs, but Jetsons have an integrated GPU (sharing physical memory with system).
You can find details about your GPU specs with deviceQuery utility in CUDA samples:
cd /usr/local/cuda/samples/1_Utilities//deviceQuery/
sudo make
./deviceQuery
and you may monitor your GPU usage at run-time with tegrastats:
sudo tegrastats
and check for item GR3D such as:
GR3D_FREQ 0%@318
saying 0% usage with current clock at 318MHz.