Jetson TX2上的demo(原创)
Jetson TX2上的demo
一、快速傅里叶-海动图 sample
The CUDA samples directory is copied to the home directory on the device by JetPack. The built binaries are in the following directory:
/home/ubuntu/NVIDIA_CUDA-<version>_Samples/bin/armv7l/linux/release/gnueabihf/
这里的version需要看你自己安装的CUDA版本而定
Run the samples at the command line or by double-clicking on them in the file browser. For example, when you run the oceanFFT sample, the following screen is displayed.

二、车辆识别加框sample
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

三、GEMM(通用矩阵乘法)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 Summary0 error(s)
四、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.
五、设备查询
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 X2Result = PASS
六、大型项目的测试
详情查看https://developer.nvidia.com/embedded/jetpack
这里面还有一些项目
Jetson TX2上的demo(原创)的更多相关文章
- 在Jetson TX2上显示摄像头视频并使用python进行caffe推理
参考文章:How to Capture Camera Video and Do Caffe Inferencing with Python on Jetson TX2 与参考文章大部分都是相似的,如果 ...
- 在Jetson TX2上捕获、显示摄像头视频
参考文章:How to Capture and Display Camera Video with Python on Jetson TX2 与参考文章大部分都是相似的,如果不习惯看英文,可以看看我下 ...
- 在Jetson TX2上安装caffe和PyCaffe
caffe是Nvidia TensorRT最支持的深度学习框架,因此在Jetson TX2上安装caffe很有必要.顺便说一句,下面的安装是支持python3的. 先决条件 在Jetson TX2上完 ...
- 在Jetson TX2上安装OpenCV(3.4.0)
参考文章:How to Install OpenCV (3.4.0) on Jetson TX2 与参考文章大部分都是相似的,如果不习惯看英文,可以看看我下面的描述 在我们使用python3进行编程时 ...
- Jetson TX2安装tensorflow(原创)
Jetson TX2安装tensorflow 大致分为两步: 一.划分虚拟内存 原因:Jetson TX2自带8G内存这个内存空间在安装tensorflow编译过程中会出现内存溢出引发的安装进程奔溃 ...
- Jetson TX2 安装JetPack3.3教程
Jetson TX2 刷机教程(JetPack3.3版本) 参考网站:https://blog.csdn.net/long19960208/article/details/81538997 版权声明: ...
- 02-NVIDIA Jetson TX2 通过JetPack 3.1刷机完整版(踩坑版)
未经允许,不得擅自改动和转载 文 | 阿小庆 2018-1-20 本文继第一篇文章:01-NVIDIA Jetson TX2开箱上电显示界面 TX2 出厂时,已经自带了 Ubuntu 16.04 系统 ...
- Jetson TX2火力全开
Jetson Tegra系统的应用涵盖越来越广,相应用户对性能和功耗的要求也呈现多样化.为此NVIDIA提供一种新的命令行工具,可以方便地让用户配置CPU状态,以最大限度地提高不同场景下的性能和能耗. ...
- 在TX2上多线程读取视频帧进行caffe推理
参考文章:Multi-threaded Camera Caffe Inferencing TX2之多线程读取视频及深度学习推理 背景 一般在TX2上部署深度学习模型时,都是读取摄像头视频或者传入视频文 ...
随机推荐
- Codeforces 888E Maximum Subsequence
原题传送门 E. Maximum Subsequence time limit per test 1 second memory limit per test 256 megabytes input ...
- AtCoder Grand Contest 019
最近比较懒,写了俩题就跑了 A - Ice Tea Store 简化背包 #include<cstdio> #include<algorithm> using namespac ...
- zoj 3228:Searching the String
Description Little jay really hates to deal with string. But moondy likes it very much, and she's so ...
- 最短路 spfa 算法 && 链式前向星存图
推荐博客 https://i.cnblogs.com/EditPosts.aspx?opt=1 http://blog.csdn.net/mcdonnell_douglas/article/deta ...
- HTTP协议----->连接管理
1. TCP连接 1.1 TCP为HTTP提供了一条可靠的比特传输管道. TCP(Transmission Control Protocol)----传输控制协议,是主机对主机层的传输控制协议,提 ...
- python之hashlib、configparser、logging模块
hashlib模块 Python的hashlib提供了常见的摘要算法,如MD5,SHA1等等. 什么是摘要算法呢?摘要算法又称哈希算法.散列算法.它通过一个函数,把任意长度的数据转换为一个长度固定的数 ...
- Maven中央仓库源地址改为阿里云(IDEA)
我的Maven是IDEA2017.1.2集成的,所以settings.xml在此位置 E:\Program Files\JetBrains\IntelliJ IDEA 2017.1.2\plugins ...
- PHP性能分析工具xhprof的安装使用与注意事项
前言 xhprof由facebook开源出来的一个PHP性能监控工具,占用资源很少,甚至能够在生产环境中进行部署. 它可以结合graphviz使用,能够以图片的形式很直观的展示代码执行耗时. 下面主要 ...
- FSFS和VDFS存储方式的区别
简单来说这个是VisualSVN基于FSFS文件系统格式扩展的.也就是说,分布式版本管理DVCS兴起之后,大家发现多个仓库的好处了,开始给SVN增加这个功能. 至于FSFS本身是SVN在2004年开始 ...
- 怎么在谷歌浏览器中安装.crx扩展名的离线Chrome插件?
李宗申 2014-9-26 23:33:33 20人评论 分类:实用方法 摘要 : 如果用户得到的离线版的Chrome插件文件(扩展名为.crx),该如何将其安装到谷歌浏览器Chrome中去呢? ...