Jetson依赖说明

更新时间：2023-01-09

一、硬件准备

本文使用的设备是jetson nano，也可以使用Jetson Xavier TX2/NX/AGX等设备。

二、jetpack依赖

本实验使用的模型依赖于JetPack 4.4，在安装 JetPack 时务必安装对应的组件：

使用 SDK Manager 安装 JetPack 需要勾选 TensorRT、OpenCV、CUDA、cuDNN 等选项。
使用 SD Card Image 方式（仅对 Jetson Nano 和 Jetson Xavier NX 有效）则无需关心组件问题，默认会全部安装。

在已经安装好的Jetson设备上，可以使用apt-cache show nvidia-jetpack、jetsonUtilities和jtop来查看jetpack版本。

1.使用apt-cache show nvidia-jetpack

执行如下命令，查看jetson版本：

                Bash
                
            

                nvidia@miivii-tegra:~$ sudo apt-cache show nvidia-jetpack
[sudo] password for nvidia:
Package: nvidia-jetpack
Architecture: arm64
Version: 4.5-b129
Priority: standard
Section: metapackages
Maintainer: NVIDIA Corporation
Installed-Size: 194
Depends: nvidia-cuda (= 4.5-b129), nvidia-opencv (= 4.5-b129), nvidia-cudnn8 (= 4.5-b129), nvidia-tensorrt (= 4.5-b129), nvidia-visionworks (= 4.5-b129), nvidia-container (= 4.5-b129), nvidia-vpi (= 4.5-b129), nvidia-l4t-jetson-multimedia-api (>> 32.5-0), nvidia-l4t-jetson-multimedia-api (<< 32.6-0)
Filename: pool/main/n/nvidia-jetpack/nvidia-jetpack_4.5-b129_arm64.deb
Size: 29360
MD5sum: 06962c42e462f643455d6194d1a2d641
SHA1: cb17547b902b2793e0df86d561809ecdbf7e401f
SHA256: 002646e6d81d13526ade23d7c45180014f3cd9e9f5fb0f8896b77dff85d6b9fe
SHA512: 99e95085ecd9ff4c33a0fc01da35a56447db2e6f372aa08c9e307a4dfd955c0ccd2d9c27e508d808f54e24827cba022338e0fc32a7bebce421c5381e16e1ac23
Homepage: http://developer.nvidia.com/jetson
Description: NVIDIA Jetpack Meta Package
Description-md5: ad1462289bdbc54909ae109d1d32c0a8
            

2.使用jetsonUtilities

下载jetsonUtilities到本地，然后执行如下python脚本：

                Shell
                
            

                nano@jetson-nano:~$ python jetsonInfo.py
NVIDIA Jetson AGX Xavier [16GB]
 L4T 32.5.0 [ JetPack 4.5 ]
   Ubuntu 18.04.5 LTS
   Kernel Version: 4.9.201-tegra
 CUDA 10.2.89
   CUDA Architecture: 7.2
 OpenCV version: 4.1.1
   OpenCV Cuda: NO
 CUDNN: 8.0.0.180
 TensorRT: 7.1.3.0
 Vision Works: 1.6.0.501
 VPI: ii libnvvpi1 1.0.15 arm64 NVIDIA Vision Programming Interface library
 Vulcan: 1.2.70
            

上述为jetpack 4.5版本的信息。

3.使用jtop

参考jetson_stats官网安装jtop程序，安装命令如下：

                Shell
                
            

                # 1. 更新系统包
sudo apt-get update
sudo apt-get upgrade
# 2. 安装pip
sudo apt-get install python-pip
# 3. 检查 pip 是否安装成功
pip -V
# 4.安装jtop
sudo -H pip install -U jetson-stats
# 5.查看系统服务状态
sudo systemctl status jetson_stats
# 6.卸载jtop
sudo pip uninstall jetson-stats
# 7. 查看pip软件清单
pip list
            

安装完毕以后，可以执行jtop命令查看jetson信息，如下图所示：

三、nvidia-docker依赖

jetson设备默认安装了docker，建议使用docker 19.03及以上版本。可以在jetson设备上执行docker info查看docker信息，执行结果如下：

Sehll

1$ docker info
2Client:
3 Debug Mode: false
4 
5Server:
6 Containers: 38
7  Running: 21
8  Paused: 0
9  Stopped: 17
10 Images: 12
11 Server Version: 19.03.6
12 Storage Driver: overlay2
13  Backing Filesystem: extfs
14  Supports d_type: true
15  Native Overlay Diff: true
16 Logging Driver: json-file
17 Cgroup Driver: cgroupfs
18 Plugins:
19  Volume: local
20  Network: bridge host ipvlan macvlan null overlay
21  Log: awslogs fluentd gcplogs gelf journald json-file local logentries splunk syslog
22 Swarm: inactive
23 Runtimes: nvidia runc
24 Default Runtime: nvidia
25 Init Binary: docker-init
26 containerd version:
27 runc version:
28 init version:
29 Security Options:
30  seccomp
31   Profile: default
32 Kernel Version: 4.9.140-tegra
33 Operating System: Ubuntu 18.04.5 LTS
34 OSType: linux
35 Architecture: aarch64
36 CPUs: 4
37 Total Memory: 3.871GiB
38 Name: jetson-nano
39 ID: O7GP:DDD5:5CIR:LEWJ:2BQ3:4WIW:VA4H:JDCP:5VGL:L2K3:PLZ7:KBHO
40 Docker Root Dir: /var/lib/docker
41 Debug Mode: false
42 Registry: https://index.docker.io/v1/
43 Labels:
44 Experimental: false
45 Insecure Registries:
46  127.0.0.0/8
47 Live Restore Enabled: false

如果Default Runtime不是nvidia，而是runc，则修改/etc/docker/daemon.json文件，添加"default-runtime": "nvidia"，修改完毕以后的/etc/docker/daemon.json文件如下所示：

                Shell
                
            

                nano@jetson-nano:~$ cat /etc/docker/daemon.json
{
    "default-runtime": "nvidia",
    "runtimes": {
        "nvidia": {
            "path": "nvidia-container-runtime",
            "runtimeArgs": []
        }
    }
}
            

修改完毕以后，重启docker，执行如下命令：

Bash

1sudo systemctl daemon-reload
2sudo systemctl restart docker

四、库文件依赖

EasyEdge Jetson 推理镜像依赖库文件，需下载对应版本库文件，并将其放置在 Jetson 设备 /etc/nvidia-container-runtime/host-files-for-container.d/ 目录下，如下图所示：

库文件下载地址：

jetpack 4.4：easyedge_runtime_j44.csv

jetpack 4.5：easyedge_runtime_j45.csv

五、查看其他信息常用命令

1.查看nv_tegra_release版本

Plain Text

1nano@jetson-nano:~$ head -n 1 /etc/nv_tegra_release
2# R32 (release), REVISION: 4.3, GCID: 21589087, BOARD: t210ref, EABI: aarch64, DATE: Fri Jun 26 04:38:25 UTC 2020

2.查看cuda-driver版本

查看cuda驱动版本，可以使用一下命令：

                Shell
                
                nano@jetson-nano:~$  dpkg -l | grep cuda-driver
ii  cuda-driver-dev-10-2                          10.2.89-1                                        arm64        CUDA Driver native dev stub library

3.查看TensorRT版本

查看TensorRT版本，可以使用一下命令：

                Shell
                
            

                nano@jetson-nano:~$  dpkg -l | grep TensorRT
ii  graphsurgeon-tf                               7.1.3-1+cuda10.2                                 arm64        GraphSurgeon for TensorRT package
ii  libnvinfer-bin                                7.1.3-1+cuda10.2                                 arm64        TensorRT binaries
ii  libnvinfer-dev                                7.1.3-1+cuda10.2                                 arm64        TensorRT development libraries and headers
ii  libnvinfer-doc                                7.1.3-1+cuda10.2                                 all          TensorRT documentation
ii  libnvinfer-plugin-dev                         7.1.3-1+cuda10.2                                 arm64        TensorRT plugin libraries
ii  libnvinfer-plugin7                            7.1.3-1+cuda10.2                                 arm64        TensorRT plugin libraries
ii  libnvinfer-samples                            7.1.3-1+cuda10.2                                 all          TensorRT samples
ii  libnvinfer7                                   7.1.3-1+cuda10.2                                 arm64        TensorRT runtime libraries
ii  libnvonnxparsers-dev                          7.1.3-1+cuda10.2                                 arm64        TensorRT ONNX libraries
ii  libnvonnxparsers7                             7.1.3-1+cuda10.2                                 arm64        TensorRT ONNX libraries
ii  libnvparsers-dev                              7.1.3-1+cuda10.2                                 arm64        TensorRT parsers libraries
ii  libnvparsers7                                 7.1.3-1+cuda10.2                                 arm64        TensorRT parsers libraries
ii  nvidia-container-csv-tensorrt                 7.1.3.0-1+cuda10.2                               arm64        Jetpack TensorRT CSV file
ii  python-libnvinfer                             7.1.3-1+cuda10.2                                 arm64        Python bindings for TensorRT
ii  python-libnvinfer-dev                         7.1.3-1+cuda10.2                                 arm64        Python development package for TensorRT
ii  python3-libnvinfer                            7.1.3-1+cuda10.2                                 arm64        Python 3 bindings for TensorRT
ii  python3-libnvinfer-dev                        7.1.3-1+cuda10.2                                 arm64        Python 3 development package for TensorRT
ii  tensorrt                                      7.1.3.0-1+cuda10.2                               arm64        Meta package of TensorRT
ii  uff-converter-tf                              7.1.3-1+cuda10.2                                 arm64        UFF converter for TensorRT package
            

自定义加速卡算力指标采集

Jetson资源监控

百度智能云

智能边缘 BIE