在 DJI Manifold 2G NVIDIA Jetson TX2 上找不到 nvidia-smi 命令

Question

通过终端运行nvidia-smi 后，我遇到了 nvidia-smi command not found 但是，我知道已经安装了 jetpack 3.3（nvidia 驱动程序）。 有人遇到过 Nvidia jetson tx2 的类似问题吗？

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

更新和编辑（已解决）： 虽然 nvidia-smi 没有运行，但用户@SeB 在下面发布的答案之一有所帮助。因此，在生成 ./deviceQuery 可执行文件后，可以看到以下内容。它会告诉您 GPU 的详细信息

/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

Answer 1

我认为 nvidia-smi 目前仅适用于 NVIDIA 独立 GPU，但 Jetsons 具有集成 GPU（与系统共享物理内存）。

您可以在 CUDA 示例中使用 deviceQuery 实用程序找到有关您的 GPU 规格的详细信息：

cd /usr/local/cuda/samples/1_Utilities//deviceQuery/
sudo make
./deviceQuery 

您可以在运行时使用 tegrastats 监控您的 GPU 使用情况：

sudo tegrastats

并检查项目 GR3D，例如：

 GR3D_FREQ 0%@318

说当前时钟为 318MHz，使用率为 0%。