在 DJI Manifold 2G NVIDIA Jetson TX2 上找不到 nvidia-smi 命令
nvidia-smi command not found on DJI Manifold 2G NVIDIA Jetson TX2
在运行宁nvidia-smi
通过终端后,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)。
有没有人用 Nvidia jetson tx2 遇到过类似的问题?
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
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 GPU 规格的详细信息:
cd /usr/local/cuda/samples/1_Utilities//deviceQuery/
sudo make
./deviceQuery
并且您可以使用 tegrastats 在 运行 时监控您的 GPU 使用情况:
sudo tegrastats
并检查项目 GR3D,例如:
GR3D_FREQ 0%@318
当前时钟为 318MHz 时使用率为 0%。
在运行宁nvidia-smi
通过终端后,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)。
有没有人用 Nvidia jetson tx2 遇到过类似的问题?
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
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 GPU 规格的详细信息:
cd /usr/local/cuda/samples/1_Utilities//deviceQuery/
sudo make
./deviceQuery
并且您可以使用 tegrastats 在 运行 时监控您的 GPU 使用情况:
sudo tegrastats
并检查项目 GR3D,例如:
GR3D_FREQ 0%@318
当前时钟为 318MHz 时使用率为 0%。