Jetson TX2 安裝jetpack，cuda，opencv

進入ubuntu系統後，默認是16.04的系統，不過都是在命令行下，須要安裝圖形界面。

在命令行應該有用戶名和密碼，還有安裝教程，基本上是這樣

用戶：nvidia
密碼：nvidia

cd ${HOME}/NVIDIA_INSTALLER
sudo ./installer.sh

以後就能夠進入圖形界面。

而後須要刷機和安裝cuda的，這裏推薦使用Jetpack，下載地址

在使用jetpack以前你須要另一臺裝有ubuntu的電腦，而後在這電腦上安裝jetpack，而不是TX2上。

注意將電腦和TX2都連到同一網段，同一路由器就行。

下載以後執行sh文件，而後會跳出 輸入密碼解壓 以後會跳出

這裏host-ubuntu的本身的電腦，能夠選擇什麼都不裝，Action那欄是no action的狀態。 不刷機的話Target-Jetson TX2下Linux for Tegre host Side ... , Flash OS Image to Target也要選擇no action。

最後的install on Target能夠本身選擇安裝，建議除了opencv能夠以後本身裝，其餘均可以裝上，相似下圖：

next會出現確認的選擇，所有都選上。

這邊填上TX2的IP地址和用戶名密碼

而後坐等裝完。

---

opencv的安裝能夠推薦一下網站進行安全，很全： http://dev.t7.ai/jetson/opencv/

---

所有安裝完以後能夠測試下。

deviceQuery

nvidia@tegra-ubuntu:~/work/TensorRT/tmp/usr/src/tensorrt$ cd /usr/local/cuda/samples/1_Utilities/deviceQuery
nvidia@tegra-ubuntu:/usr/local/cuda/samples/1_Utilities/deviceQuery$ ls
deviceQuery  deviceQuery.cpp  deviceQuery.o  Makefile  NsightEclipse.xml  readme.txt
nvidia@tegra-ubuntu:/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          8.0 / 8.0
  CUDA Capability Major/Minor version number:    6.2
  Total amount of global memory:                 7851 MBytes (8232062976 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
  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 = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = NVIDIA Tegra X2
Result = PASS

內存帶寬測試

nvidia@tegra-ubuntu:/usr/local/cuda/samples/1_Utilities/bandwidthTest$ ./bandwidthTest
[CUDA Bandwidth Test] - Starting...
Running on...

 Device 0: NVIDIA Tegra X2
 Quick Mode

 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)    Bandwidth(MB/s)
   33554432         20215.8

 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)    Bandwidth(MB/s)
   33554432         20182.2

 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)    Bandwidth(MB/s)
   33554432         35742.8

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

GEMM 測試

nvidia@tegra-ubuntu:/usr/local/cuda/samples/7_CUDALibraries/batchCUBLAS$ ./batchCUBLAS -m1024 -n1024 -k1024
batchCUBLAS Starting...

GPU Device 0: "NVIDIA Tegra X2" with compute capability 6.2


 ==== Running single kernels ====

Testing sgemm
#### args: ta=0 tb=0 m=1024 n=1024 k=1024  alpha = (0xbf800000, -1) beta= (0x40000000, 2)
#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 0.00372291 sec  GFLOPS=576.83
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=1024 n=1024 k=1024  alpha = (0x0000000000000000, 0) beta= (0x0000000000000000, 0)
#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 0.10940003 sec  GFLOPS=19.6296
@@@@ dgemm test OK

 ==== Running N=10 without streams ====

Testing sgemm
#### args: ta=0 tb=0 m=1024 n=1024 k=1024  alpha = (0xbf800000, -1) beta= (0x00000000, 0)
#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 0.03462315 sec  GFLOPS=620.245
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=1024 n=1024 k=1024  alpha = (0xbff0000000000000, -1) beta= (0x0000000000000000, 0)
#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 1.09212208 sec  GFLOPS=19.6634
@@@@ dgemm test OK

 ==== Running N=10 with streams ====

Testing sgemm
#### args: ta=0 tb=0 m=1024 n=1024 k=1024  alpha = (0x40000000, 2) beta= (0x40000000, 2)
#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 0.03504515 sec  GFLOPS=612.776
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=1024 n=1024 k=1024  alpha = (0xbff0000000000000, -1) beta= (0x0000000000000000, 0)
#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 1.09177494 sec  GFLOPS=19.6697
@@@@ dgemm test OK

 ==== Running N=10 batched ====

Testing sgemm
#### args: ta=0 tb=0 m=1024 n=1024 k=1024  alpha = (0x3f800000, 1) beta= (0xbf800000, -1)
#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 0.03766394 sec  GFLOPS=570.17
@@@@ sgemm test OK
Testing dgemm
#### args: ta=0 tb=0 m=1024 n=1024 k=1024  alpha = (0xbff0000000000000, -1) beta= (0x4000000000000000, 2)
#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 1.09389901 sec  GFLOPS=19.6315
@@@@ dgemm test OK

Test Summary
0 error(s)