how to configure deep learning environment on nvidia xaiver arm platform

Publish Date: 2019-01-18

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Guide

device: JETSON AGX XAVIER
ubuntu 18.04 aarch64
cmake: 3.10.2—>3.13.3(手动升级)
cmake-gui: 3.10.2
nvidia driver:
GPU arch(s): sm_72
cuda 10.0
cudnn 7.3.1
glog 0.35 (0.35)
gflags 2.2.1 (2.2.1)
gtest 1.8 (1.8)
boost 1.66 (1.65.1)
protobuf(编译为static库) (3.6.1)
opencv 3.4.0

升级默认的cmake 3.10.2到最新版3.13.3解决CUDA_cublas_device_LIBRARY问题。
protobuf 3.0.0会导致trainpilot编译出错，所以需要源码编译安装protobuf 3.6.1。

jetson products

Jetson TX1 (fp32,fp16)
Jetson TX2 (fp32,fp16)
Jetson AGX Xavier (fp32,fp16,int8,dla)
Jetson Nano (Jetbot)

see cuda-gpus

xaiver device

see xaiver
开机：按下power键（3个按键依次为power,force recovery和reset建），板子后面的一个指示灯亮，发白光。系统启动。

xaiver磁盘只有28G,在编译的过程中可以增加移动硬盘，在移动硬盘上编译opencv,caffe,sdklite,trainpilot等代码,安装到xaiver系统。

check system

for desktop ubuntu

uname -a 
Linux ke 4.13.0-36-generic #40~16.04.1-Ubuntu SMP Fri Feb 16 23:25:58 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux

uname -p
x86_64

for xaiver

uname -a 
Linux l4t 4.9.108-tegra #1 SMP PREEMPT Wed Oct 31 15:17:21 PDT 2018 aarch64 aarch64 aarch64 GNU/Linux

uname -p
aarch64

AArch64是ARMv8 架构的一种执行状态。
xaiver默认安装 ubuntu 18.04 版本号为bionic, ubuntu 16.04的版本号为xenial

install packages

sudo apt-get install build-essential
sudo apt-get install libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

sudo apt-get install git cmake cmake-gui

cuda

sudo apt-get install libleveldb-dev liblmdb-dev libsnappy-dev libhdf5-serial-dev 
sudo apt-get install libopenblas-dev liblapack-dev libatlas-base-dev

upgrade cmake

sudo apt-get -y purge cmake

wget https://github.com/Kitware/CMake/releases/download/v3.13.3/cmake-3.13.3.tar.gz
cd cmake-3.13.3
./configure
make -j8
sudo make install

aarch64架构默认安装到/usr/local/lib和/usr/local/bin/cmake

Compile Package

gflags && glog

#wget https://github.com/schuhschuh/gflags/archive/v2.2.1.tar.gz
#wget https://github.com/google/glog/archive/v0.3.5.tar.gz

sudo apt-get install libgflags-dev libgoogle-glog-dev

gflags: 2.2.1; glog: 0.3.5
aarch64架构默认安装到/usr/include和/usr/lib/aarch64-linux-gnu/libgflags.so
对应x86_64架构是默认安装到/usr/include和/usr/lib/x86_64-linux-gnu/libgflags.so

gtest

#wget https://github.com/google/googletest/archive/release-1.8.0.tar.gz

sudo apt-get install libgtest-dev
cd /usr/src/gtest # only source files
mkdir build && cd build 
cmake -DBUILD_SHARED_LIBS=ON ..
make -j8 && make install

gtest只是下载了源码1.8.0，需要compile,默认是static
aarch64架构默认安装到/usr/local/include/gtest和/usr/local/lib/libgtest.so,/usr/local/lib/libgtest_main.so

boost

apt-get install --no-install-recommends libboost-all-dev

1.65.1
aarch64架构默认安装到/usr/include和/usr/lib/aarch64-linux-gnu/libboost_date_time.so,/usr/lib/aarch64-linux-gnu/libboost_filesystem.so

protobuf

apt-get install libprotobuf-dev protobuf-compiler

3.0.0
aarch64架构默认安装到/usr/include和/usr/lib/aarch64-linux-gnu/libprotobuf.so,/usr/lib/aarch64-linux-gnu/libprotobuf-lite.so
protoc默认安装到/usr/bin/protoc

notice

protobuf 3.0.0会导致trainpilot编译出错，所以需要源码编译安装protobuf 3.6.1。

wget https://github.com/protocolbuffers/protobuf/releases/download/v3.6.1/protobuf-cpp-3.6.1.tar.gz

tar -xvf protobuf-cpp-3.6.1.tar.gz
cd protobuf-3.6.1 
./configure --disable-shared CXXFLAGS="-fPIC"
./configure --disable-static CXXFLAGS="-fPIC"
make -j8 

/usr/local/bin/protoc --version
3.6.1

opencv

wget https://github.com/opencv/opencv/archive/3.4.0.zip

ssl error

git clone https://github.com/opencv/opencv.git
Cloning into 'opencv'...
fatal: unable to access 'https://github.com/opencv/opencv.git/': server certificate verification failed. CAfile: /etc/ssl/certs/ca-certificates.crt CRLfile: none

solution

git config --global http.sslverify false

cmake-gui display error

cmake-gui ..
qt.qpa.screen: QXcbConnection: Could not connect to display 
Could not connect to any X display.

solution:

export DISPLAY=':0.0'

clock

see here

make: warning: Clock skew detected. Your build may be incomplete

fix

sudo date -s 1/18/2019
sudo date -s 1/18/2019

系统时间有问题。

caffe

cd caffe && mkdir build && cmake ..

errors

CMake Error: The following variables are used in this project, but they are set to NOTFOUND.
Please set them or make sure they are set and tested correctly in the CMake files:
CUDA_cublas_device_LIBRARY (ADVANCED)
    linked by target "caffe" in directory /home/nvidia/workspace/caffe/src/caffe

CUDA_cublas_device_LIBRARY error

fix

see here

I also got the problem when I compiled caffe with the latest CUDA 10.0, and after upgrading CMake from 3.12.1 to 3.12.2 it’s done @harumo11

Confirmed: I had the same problem (Ubuntu 18.04, CUDA 10.0) and installing CMake 3.12.2 (instead of the distro’s 3.10.2) fixed it.

升级默认的cmake 3.10.2到最新版3.13.3解决CUDA_cublas_device_LIBRARY问题。

cmake --version
3.13.3

cmake -DCMAKE_INSTALL_PREFIX=/usr/local/ ..

nvpmodel

TX2 nvpmodel

Jetson TX2 nvpmodel

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。

Jetson TX2由一个GPU和一个CPU集群组成。 CPU集群由双核丹佛2处理器和四核ARM Cortex-A57组成，通过高性能互连架构连接。拥有6个CPU核心和一个GPU，您可以不必自行运行所有性能/功耗来测试最佳的运行状态，因为NVIDIA的新的命令工具Nvpmodel，提供了5种模式。在Jetson TX2上。下表列出了CPU内核的模式以及正在使用的CPU和GPU的最大频率。

6 cpu mode

# nvpmodel
# /etc/nvpmodel.conf
cat /proc/cpuinfo

sudo nvpmodel -q –-verbose # 查看当前的模式
sudo nvpmodel -p –-verbose # 打印支持的所有模式及其配置
sudo nvpmodel -m 0 # 启动最高性能，此时所有CPU均已启动，但对应的主频还不是最高的
sudo ~/jetson_clocks.sh # 开启最大频率

jetson_clocks

see here

jetson_clocks.sh --help  
sudo ~/jetson_clocks.sh --show # 查看当前System配置
sudo ~/jetson_clocks.sh # 开启最大频率

# Checking GPU/DLA utilization/stats 
sudo ~/tegrastats