0. 简介

Jetson TX2【1】是基于 NVIDIA Pascal™ 架构的 AI 单模块超级计算机，性能强大（1 TFLOPS），外形小巧，节能高效（7.5W），非常适合机器人、无人机、智能摄像机和便携医疗设备等智能终端设备。Jatson TX2 与 TX1 相比，内存和 eMMC 提高了一倍，CUDA 架构升级为 Pascal，每瓦性能提高一倍，支持 Jetson TX1 模块的所有功能，支持更大、更深、更复杂的深度神经网络。

TX2 内部结构如下：

1. 开箱

过程细节不展开，板卡上电后来张照片：

2. 刷机

TX2 出厂时，已经自带了 Ubuntu 16.04 系统，可以直接启动。但一般我们会选择刷机，目的是更新到最新的 JetPack L4T，并自动安装最新的驱动、CUDA Toolkit、cuDNN、TensorRT。

刷机注意以下几点：

Host 需要安装 Ubuntu 14.04，至少预留 15 GB 硬盘空间，不要用 root 用户运行 JetPack-${VERSION}.run，我用的是 JetPack-L4T-3.1-linux-x64.runTX2 需要进入 Recovery Mode，参考随卡自带的说明书步骤刷机时间大概需要 1~2 小时，会格式化 eMMC，主要备份数据

3. 运行视频目标检测 Demo

刷机成功后，重启 TX2，连接键盘鼠标显示器，就可以跑 Demo 了。

nvidia@tegra-ubuntu:~/tegra_multimedia_api/samples/backend$ ./backend 1 ../../data/Video/sample_outdoor_car_1080p_10fps.h264 H264 --trt-deployfile ../../data/Model/GoogleNet_one_class/GoogleNet_modified_oneClass_halfHD.prototxt --trt-modelfile ../../data/Model/GoogleNet_one_class/GoogleNet_modified_oneClass_halfHD.caffemodel --trt-forcefp32 0 --trt-proc-interval 1 -fps 10

视频截图如下：

4. 运行 TensorRT Benchmark

TensorRT 【3】是 Nvidia GPU 上的深度学习 inference 优化库，可以将训练好的模型通过优化器生成 inference 引擎

将 TX2 设置为 MAXP （最高性能）模式，运行 TensorRT 加速的 GoogLeNet、VGG16 得到处理性能如下：

5. TX2 不支持的 feature

不支持 int8待发现

参考文献

【1】嵌入式系统开发者套件和模块 | NVIDIA Jetson | NVIDIA【2】Download and Install JetPack L4T【3】TensorRT

附录

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

内存带宽测试

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 测试

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) 相关资源：基于NVIDIA jetson平台Torch安装教程 Jetson-Inference使用