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Jetson Nano Installation

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vanguard
修改2021-04-19 10:52:01
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修改2021-04-19 10:52:01
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NVIDIA® Jetson Nano™ 开发者套件

Nvidia在边缘计算/Xavier推出的最低配版GPU运算平台

Jetson Nano 开发者套件入门

https://developer.nvidia.com/zh-cn/embedded/learn/get-started-jetson-nano-devkit

官方培训课程(需注册)

https://courses.nvidia.com/courses/course-v1:DLI+S-RX-02+V2/info

包含一个板载内存和处理器核心(带大大的散热器),和一块承载的板

TECHNICAL SPECIFICATIONS

GPU

NVIDIA Maxwell architecture with 128 NVIDIA CUDA® cores

CPU

Quad-core ARM Cortex-A57 MPCore processor

Memory

4 GB 64-bit LPDDR4, 1600MHz 25.6 GB/s

Storage

16 GB eMMC 5.1

Video Encode

250MP/sec 1x 4K @ 30 (HEVC) 2x 1080p @ 60 (HEVC) 4x 1080p @ 30 (HEVC) 4x 720p @ 60 (HEVC) 9x 720p @ 30 (HEVC)

Video Decode

500MP/sec 1x 4K @ 60 (HEVC) 2x 4K @ 30 (HEVC) 4x 1080p @ 60 (HEVC) 8x 1080p @ 30 (HEVC) 9x 720p @ 60 (HEVC)

Camera

12 lanes (3x4 or 4x2) MIPI CSI-2 D-PHY 1.1 (1.5 Gb/s per pair)

Connectivity

Gigabit Ethernet, M.2 Key E

Display

HDMI 2.0 and eDP 1.4

USB

4x USB 3.0, USB 2.0 Micro-B

Others

GPIO, I2C, I2S, SPI, UART

Mechanical

69.6 mm x 45 mm 260-pin edge connector

https://developer.nvidia.com/embedded/jetson-nano

Developer kit carrier boards 两个版本,分别支持4GB和2GB内存

1. 硬件准备 - 买买买,注意电源输入,5V3A配合跳线设置,电压太高烧了,电流太低性能不能出来

2. 安装操作系统 - https://developer.nvidia.com/jetson-nano-sd-card-image 用Rufus之类的工具烧录启动盘一样操作就行

3. 升级环境 - 和ubuntu的操作基本一致

代码语言:shell
复制
# 扩容操作 2GB -> 4GB # 参考DLI教程
free -m
sudo systemctl disable nvzramconfig
sudo falcate -l 4G /mnt/4GB.swap
sudo chmod 600 /mnt/4GB.swap
sudo mkswap /mnt/4GB.swap
sudo vi /etc /fstab
# /mnt/4GB.swap swap swap defaults 0 0
sudo reboot
free -m

# Docker安装 # 参考DLI教程
mkdir -p ~/nvdli-data
sudo docker run --runtime nvidia -it --rm --network host \
    --volume ~/nvdli-data:/nvdli-nano/data \
    --device /dev/video0 \
    nvcr.io/nvidia/dli/dli-nano-ai:<tag>
echo "sudo docker run --runtime nvidia -it --rm --network host \
    --volume ~/nvdli-data:/nvdli-nano/data \
    --device /dev/video0 \
    nvcr.io/nvidia/dli/dli-nano-ai:v2.0.1-r32.4.4" > docker_dli_run.sh
chmod +x docker_dli_run.sh
./docker_dli_run.sh
# http://192.168.55.1:8888/ # dlinano

# Python3环境
sudo apt update
sudo apt list full-upgrade
sudo apt install python3
sudo apt install python3-pip
# which python3
ln -s /usr/bin/python3 /usr/bin/python
# pip3 -> pip
pip install --upgrade pip
pip install virtualenv

# Headless 支持远程登陆
sudo apt install net-tools
sudo apt install ssh

sudo apt-get update
sudo apt-get install libhdf5-serial-dev hdf5-tools libhdf5-dev \
     zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran

sudo apt-get install python3-pip
sudo pip3 install -U pip testresources setuptools==49.6.0 

sudo pip3 install -U numpy==1.19.4 future==0.18.2 mock==3.0.5 \
     h5py==2.10.0 keras_preprocessing==1.1.1 \
     keras_applications==1.0.8 gast==0.2.2 futures protobuf pybind11

sudo pip3 install --extra-index-url \
     https://developer.download.nvidia.com/compute/redist/jp/v$JP_VERSION tensorflow
sudo apt-get install virtualenv
python3 -m virtualenv -p python3 <chosen_venv_name>

# CUDA
sudo gedit  ~/.bashrc
export CUBA_HOME=/usr/local/cuda-10.2
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda-10.2/bin:$PATH
source ~/.bashrc

# Jetson Nano 开发者套件入门

https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit

# TensorFlow Installation for Jetson Platform

https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html

# Camera

https://cloud.tencent.com/developer/article/1421907

# Getting Started

https://developer.nvidia.com/embedded/learn/getting-started-jetson#tutorials

# Run Tensorflow models on the Jetson Nano with TensorRT

https://gilberttanner.com/blog/run-tensorflow-on-the-jetson-nano

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

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