orin 上，之空間不足

# 找尋占空間之檔案

$ sudo du -sh /* 2>/dev/null | sort -hr | head -n 10

$ sudo du -sh /home/* 2>/dev/null | sort -hr | head -n 10

# 清空 Docker 的 cache

$ docker system df

$ docker builder prune

# Docker 資料搬家

$ sudo systemctl stop docker

$ sudo systemctl stop docker.socket

$ sudo rsync -aqxP /var/lib/docker/ /mnt/Data/docker_data

$ sudo vi /etc/docker/daemon.json 

{

    "runtimes": {

        "nvidia": {

            "args": [],

            "path": "nvidia-container-runtime"

        }

    },

    "data-root": "/mnt/Data/docker-data",

    "iptables": false,

    "bridge": "docker0"

}

$ sudo systemctl stop containerd

$ sudo vi /etc/containerd/config.toml

root = "/mnt/Data/containerd"

$ sudo mkdir -p /mnt/Data/containerd

$ sudo rsync -avz /var/lib/containerd/ /mnt/Data/containerd/

$ sudo systemctl start containerd

$ sudo systemctl start docker

$ sudo systemctl start docker.socket

Ollama 之 Open-webui + Searxng + Redis + Caddy

參考 https://forums.developer.nvidia.com/t/playbook-1-open-webui-searxng-private-web-search-on-dgx-spark/359578

參考 https://forums.developer.nvidia.com/t/building-local-hybrid-llms-on-dgx-spark-that-outperform-top-cloud-models/359569

參考 https://forums.developer.nvidia.com/t/dgx-spark-rag-on-docker/363125

$ > searxng/limiter.toml

$ vi searxng/settings.yml

use_default_settings: true

search:

  formats:

    - html

    - json

server:

  # 安全起見，secret_key 可用 openssl rand -hex 32 產生

  secret_key: "c199725f396362fd99ad0e3239fbb5be9d01c04083cffb7e16d50301c67288ee"

  limiter: false

  image_proxy: true

valkey:

  url: redis://redis:6379/0

engines:

  - name: ahmia

    disabled: true

  - name: torch

    disabled: true

$ vi docker-compose-llm.yaml

version: '3.8'

services:

  caddy:

    container_name: caddy

    image: docker.io/library/caddy:2-alpine

    networks:

      - webnet

    ports:

      - "80:80"   # HTTP 埠號

      - "443:443" # HTTPS 埠號

      - "443:443/udp" # HTTP/3 支援

    restart: unless-stopped

    volumes:

      - ./caddy/Caddyfile:/etc/caddy/Caddyfile:ro

      - caddy-data:/data:rw

      - caddy-config:/config:rw

    environment:

      - SEARXNG_HOSTNAME=${SEARXNG_HOSTNAME:-localhost}

      - SEARXNG_TLS=${LETSENCRYPT_EMAIL:-internal}

  redis:

    container_name: redis

    image: docker.io/valkey/valkey:8-alpine

    command: valkey-server --save 30 1 --loglevel warning

    restart: unless-stopped

    networks:

      - webnet

    volumes:

      - valkey-data2:/data

  searxng:

    container_name: searxng

    image: docker.io/searxng/searxng:latest

    restart: unless-stopped

    networks:

      - webnet

    ports:

      - "0.0.0.0:8888:8080"

    volumes:

      - ./searxng:/etc/searxng:rw

      - searxng-log:/var/cache/searxng:rw

    environment:

      - SEARXNG_BASE_URL=https://${SEARXNG_HOSTNAME:-localhost}/searxng/

      #- SEARXNG_BASE_URL=https://${SEARXNG_HOSTNAME:-localhost}/

  litellm:

    image: ghcr.io/berriai/litellm:main-latest

    container_name: litellm

    restart: unless-stopped

    ports:

      - "4000:4000"

    volumes:

      - ./litellm_config.yaml:/app/config.yaml:ro

    command: --config /app/config.yaml --detailed_debug --num_workers 4

    networks:

      - webnet

    environment:

      - LITELLM_MASTER_KEY=${LITELLM_KEY}

    env_file:

      - .env

  open-webui:

    image: ghcr.io/open-webui/open-webui:cuda

    container_name: open-webui

    restart: unless-stopped

    ports:

      - "8080:8080"

    volumes:

      - open-webui:/app/backend/data

      - ${HOME_CACHE:-~/.cache}/huggingface:/root/.cache/huggingface

    environment:

      - ENABLE_WEB_SEARCH=true

      - WEB_SEARCH_ENGINE=searxng

      - SEARXNG_URL=http://searxng:8080

      - OPENAI_API_BASE_URL=http://litellm:4000/v1

      - OPENAI_API_KEY=${LITELLM_KEY:-0p3n-w3bu!}

    networks:

      - webnet

      

networks:

  webnet:

    external: true

volumes:

  caddy-data:

  caddy-config:

  valkey-data2:

  searxng-log:

  open-webui:

$ vi caddy/Caddyfile

{$SEARXNG_HOSTNAME} {

    encode gzip zstd

    header {

        Strict-Transport-Security "max-age=31536000;"

        X-Content-Type-Options "nosniff"

        X-Frame-Options "SAMEORIGIN"

        Referrer-Policy "no-referrer"

    }

    #handle /searxng* {

    #    uri strip_prefix /searxng

    #    reverse_proxy searxng:8080

    #}

    #handle /litellm* {

    #    uri strip_prefix /litellm

    #    reverse_proxy litellm:4000

    #}

    handle_path /searxng* {

        reverse_proxy searxng:8080

    }

    handle_path /litellm* {

        reverse_proxy litellm:4000

    }

    handle {

        reverse_proxy open-webui:8080

    }

}

$ vi litellm_config.yaml

model_list:

  - model_name: Nemotron-3-Nano-30B-A3B # 這是你在 Open WebUI 選單中會看到的名稱

    litellm_params:

      model: openai/Nemotron-3-Nano-30B-A3B  # 這裡填寫你 vLLM 載入的模型完整路徑或名稱

      api_base: http://vllm-Nemotron-3-Nano-30B-A3B:8000/v1 # vLLM 的 API 地址

      health_check_url: http://vllm-Nemotron-3-Nano-30B-A3B:8000/health

      api_key: "not-needed" # vLLM 預設不需 key，但 litellm 要求必填

      rpm: 10 # 每分鐘請求限制 (選填)

  - model_name: Qwen3.5-35B-A3B # 這是你在 Open WebUI 選單中會看到的名稱

    litellm_params:

      model: openai/Qwen3.5-35B-A3B  # 這裡填寫你 vLLM 載入的模型完整路徑或名稱

      api_base: http://vllm-Qwen3.5-35B-A3B:8000/v1 # vLLM 的 API 地址

      health_check_url: http://vllm-Qwen3.5-35B-A3B:8000/health

      api_key: "not-needed" # vLLM 預設不需 key，但 litellm 要求必填

      rpm: 10 # 每分鐘請求限制 (選填)

  - model_name: GLM-4.7-Flash # 這是你在 Open WebUI 選單中會看到的名稱

    litellm_params:

      model: openai/GLM-4.7-Flash  # 這裡填寫你 vLLM 載入的模型完整路徑或名稱

      api_base: http://vllm-GLM-4.7-Flash:8000/v1 # vLLM 的 API 地址

      health_check_url: http://vllm-GLM-4.7-Flash:8000/health

      api_key: "not-needed" # vLLM 預設不需 key，但 litellm 要求必填

      rpm: 10 # 每分鐘請求限制 (選填)

  - model_name: Qwen3.5-9B # 這是你在 Open WebUI 選單中會看到的名稱

    litellm_params:

      model: openai/Qwen3.5-9B  # 這裡填寫你 vLLM 載入的模型完整路徑或名稱

      api_base: http://192.168.0.107:8080/v1 # vLLM 的 API 地址

      api_key: "not-needed" # vLLM 預設不需 key，但 litellm 要求必填

      rpm: 10 # 每分鐘請求限制 (選填)

litellm_settings:

  drop_params: True       # 如果 Open WebUI 傳送了 vLLM 不支援的參數，自動剔除避免報錯

  set_verbose: False      # 若要除錯可改為 True 查看詳細日誌

#general_settings:

#  #master_key: ${LITELLM_KEY} # 對應你 .env 裡的 OPENAI_API_KEY (0p3n-w3bu!)

#  master_key: asdfasdf23

$ vi .env

SEARXNG_HOSTNAME=www.fwwrcom.tw

LETSENCRYPT_EMAIL=ewr@gmail.com

HOME_CACHE=/home/spark/.cache

LITELLM_KEY=asdfasdf23

$ vi sta_llm.sh

#!/bin/bash

# 1. 檢查 .env 檔案是否存在 (避免啟動失敗)

if [ ! -f .env ]; then

    echo "❌ 錯誤: 找不到 .env 檔案，請先建立它。"

    exit 1

fi

# 2. 執行 Docker Compose

echo "🚀 正在啟動 LLM 服務..."

docker compose -f docker-compose-llm.yaml up -d

# 3. 檢查啟動狀態

if [ $? -eq 0 ]; then

    echo "✅ 服務已成功在背景執行！"

    echo "使用 'docker compose -f docker-compose-llm.yaml logs -f' 查看全部日誌。"

    echo "使用 'docker compose -f docker-compose-llm.yaml logs -f litellm' 查看 litellm 日誌。"

else

    echo "❌ 啟動失敗，請檢查配置。"

fi

$ docker compose -f docker-compose-llm.yaml up -d

$ docker compose -f docker-compose-llm.yaml up -d --force-recreate

# 測試 searxng 網路

$ docker exec -it caddy ping searxng

# 瀏覽器開啟 http://localhost:8888

$ curl "http://localhost:8888/search?q=test&format=json"

$ curl "http://192.168.0.108:8888/search?q=test&format=json"

$ curl "https://www.fwwrcom.com.tw/searxng/search?q=test&format=json"

$ curl "http://192.168.0.108:8888/"

$ curl "https://www.fwwrcom.com.tw/searxng/"

$ curl http://192.168.0.108:4000/v1/models \

  -H "Authorization: Bearer asdfasdf23"

$ curl -s http://192.168.0.108:4000/v1/chat/completions \

  -H "Authorization: Bearer asdfasdf23" \

  -H "Content-Type: application/json" \

  -d '{

    "model": "Qwen3.5-35B-A3B",

    "messages": [{"role": "user", "content": "請你自我介紹"}],

    "max_tokens": 64

  }'

$ curl -i https://www.fwwrcom.com.tw/litellm/v1/chat/completions \

  -H "Authorization: Bearer asdfasdf23" \

  -H "Content-Type: application/json" \

  -d '{

    "model": "Qwen3.5-35B-A3B",

    "messages": [{"role": "user", "content": "請你自我介紹"}],

    "max_tokens": 64

  }'

ubuntu 之 nfs server 和 client

A 主機

$ sudo apt update

$ sudo apt install -y nfs-kernel-server

$ sudo vi /etc/exports

/mnt/Data/models 192.168.1.20(ro,sync,no_subtree_check)

ro：只讀（強烈建議，避免模型被誤寫）

sync：資料一致性

no_subtree_check：效能與穩定性

/mnt/Data/models 192.168.1.20(ro,all_squash,anonuid=1000,anongid=1000,sync,no_subtree_check)

all_squash：所有 client user 都映射成 anonymous

anonuid/anongid：指定成某個 UID/GID

$ sudo exportfs -ra

$ sudo systemctl restart nfs-kernel-server

$ showmount -e localhost

B 主機

$ sudo apt update

$ sudo apt install -y nfs-common

$ sudo mkdir -p /mnt/models

$ sudo mount -t nfs 192.168.1.10:/mnt/Data/models /mnt/models

$ ls /mnt/models

$ sudo vi /etc/fstab

192.168.1.10:/mnt/Data/models  /mnt/models  nfs  ro,_netdev,auto  0  0

Ubuntu 之 錄音 與 撥放, 使用 arecord aplay ffmpeg

列出錄音設備

$ arecord -l

**** List of CAPTURE Hardware Devices ****

card 0: PCH [HDA Intel PCH], device 0: ALCS1200A Analog [ALCS1200A Analog]

  Subdevices: 1/1

  Subdevice #0: subdevice #0

card 0: PCH [HDA Intel PCH], device 2: ALCS1200A Alt Analog [ALCS1200A Alt Analog]

  Subdevices: 1/1

  Subdevice #0: subdevice #0

指定 card:0, device 0, 使用16000採樣，錄音10秒

$ arecord -Dhw:0,0 -d 10 -f cd -r 16000 -c 2 -t wav test.wav

Recording WAVE 'test.wav' : Signed 16 bit Little Endian, Rate 16000 Hz, Stereo

Warning: rate is not accurate (requested = 16000Hz, got = 44100Hz)

         please, try the plug plugin 

$ arecord -D mono --device=hw:0,0 -d 10 -f cd -r 16000 -c 2 -t wav test.wav

Recording WAVE 'test.wav' : Signed 16 bit Little Endian, Rate 16000 Hz, Stereo

Warning: rate is not accurate (requested = 16000Hz, got = 44100Hz)

         please, try the plug plugin 

列出播放設備

$ aplay -l

**** List of PLAYBACK Hardware Devices ****

card 0: PCH [HDA Intel PCH], device 0: ALCS1200A Analog [ALCS1200A Analog]

  Subdevices: 1/1

  Subdevice #0: subdevice #0

card 0: PCH [HDA Intel PCH], device 1: ALCS1200A Digital [ALCS1200A Digital]

  Subdevices: 1/1

  Subdevice #0: subdevice #0

card 0: PCH [HDA Intel PCH], device 3: HDMI 0 [HDMI 0]

  Subdevices: 1/1

  Subdevice #0: subdevice #0

從錄音設備直接撥出

$ arecord -Dhw:0,0 -d 10 -f cd -r 16000 | aplay -Dhw:0,0 -r 16000

Recording WAVE 'stdin' : Signed 16 bit Little Endian, Rate 16000 Hz, Stereo

Warning: rate is not accurate (requested = 16000Hz, got = 44100Hz)

         please, try the plug plugin 

Playing WAVE 'stdin' : Signed 16 bit Little Endian, Rate 44100 Hz, Stereo

安裝 ffmpeg

$ sudo add-apt-repository ppa:savoury1/ffmpeg4  

$ sudo apt-cache policy ffmpeg  

$ sudo apt-get install ffmpeg  

$ ffmpeg -version  

$ sudo add-apt-repository --remove ppa:savoury1/ffmpeg4  

參考 https://ffmpeg.org/ffmpeg-devices.html

列出裝置

$ ffmpeg -devices

Devices:

 D. = Demuxing supported

 .E = Muxing supported

 --

 DE alsa            ALSA audio output

  E caca            caca (color ASCII art) output device

 DE fbdev           Linux framebuffer

 D  iec61883        libiec61883 (new DV1394) A/V input device

 D  jack            JACK Audio Connection Kit

 D  kmsgrab         KMS screen capture

 D  lavfi           Libavfilter virtual input device

$ cat /proc/asound/cards

 0 [PCH            ]: HDA-Intel - HDA Intel PCH

                      HDA Intel PCH at 0xa7230000 irq 148

 1 [NVidia         ]: HDA-Intel - HDA NVidia

                      HDA NVidia at 0xa5080000 irq 17

錄音

$ ffmpeg -f alsa -i hw:0 test.wav

安裝 Ubuntu 20.04

$ sudo apt-get update

$ sudo apt-get upgrade

$ sudo apt-get install ssh

$ sudo vi /etc/fstab

#中間的空格要使用 tab

ip:/share_folder /mnt/mount_folder nfs defaults,bg 0 0

$ cd /mnt

$ sudo mkdir QNAP_A QNAP_B

$ sudo mount -a

$ mkdir -p ~/.config/autostart

$ cp /usr/share/applications/vino-server.desktop ~/.config/autostart/

$ gsettings set org.gnome.Vino prompt-enabled false

$ gsettings set org.gnome.Vino require-encryption false

$ gsettings set org.gnome.Vino authentication-methods "['vnc']"

$ gsettings set org.gnome.Vino vnc-password $(echo -n 'ChangeToYourPasswd'|base64)

$ sudo vi /etc/gdm3/custom.conf

WaylandEnable=false

AutomaticLoginEnable = true

AutomaticLogin = UserLoginName

$ vi vino.sh

DISP=`ps -u $(id -u) -o pid= | \

    while read pid; do

        cat /proc/$pid/environ 2>/dev/null | tr '\0' '\n' | grep '^DISPLAY=:'

    done | grep -o ':[0-9]*' | sort -u`

echo $DISP

/usr/lib/vino/vino-server --display=$DISP

$ chmod +x vino.sh

依據 使用最新版本的 driver

CUDA Toolkit and Corresponding Driver Versions

https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html

dGPU Setup for Ubuntu

https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html

Ubuntu 20.04

GStreamer 1.16.3

NVIDIA driver 525.125.06

CUDA 12.1

TensorRT 8.5.3.1

$ sudo ubuntu-drivers devices

$ sudo apt-get install nvidia-driver-535

$ sudo reboot

$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.1-1_all.deb

$ sudo dpkg -i cuda-keyring_1.1-1_all.deb

$ sudo apt-get update

$ sudo apt-get -y install cuda-12-2

$ sudo apt-get -y install cuda-12-1

安裝 cuDNN

參考 https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

到 2.2. Downloading cuDNN for Linux(https://developer.nvidia.com/cudnn)

下載 Local Install for Ubuntu18.04 x86_64(Deb)

$ sudo apt-get install zlib1g

$ sudo dpkg -i cudnn-local-repo-ubuntu2004-8.9.5.29_1.0-1_amd64.deb

sudo cp /var/cudnn-local-repo-ubuntu2004-8.9.5.29/cudnn-local-98C06E99-keyring.gpg /usr/share/keyrings/

$ sudo apt-get update

$ apt list -a libcudnn8

$ sudo apt-get install libcudnn8=8.9.5.29-1+cuda12.2

$ sudo apt-get install libcudnn8-dev=8.9.5.29-1+cuda12.2

$ sudo apt-get install libcudnn8-samples=8.9.5.29-1+cuda12.2

$ update-alternatives --display libcudnn

$ cp -r /usr/src/cudnn_samples_v8/ .

$ cd cudnn_samples_v8/mnistCUDNN/

$ sudo apt-get install libfreeimage3 libfreeimage-dev

$ make clean && make

$ ./mnistCUDNN

...

Test passed!

安裝 TensorRT 8.6.1

https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-861/install-guide/index.html

$ sudo apt-get install python3-pip

$ sudo apt-get install python3.8.venv

$ python3 -m venv envs/tensorrt

$ source envs/tensorrt/bin/activate

$ pip3 install --upgrade pip

$ python3 -m pip install --extra-index-url https://pypi.nvidia.com tensorrt_libs

$ python3 -m pip install --extra-index-url https://pypi.nvidia.com tensorrt_bindings

$ python3 -m pip install --upgrade tensorrt

$ python3 -m pip install --upgrade tensorrt_lean

$ python3 -m pip install --upgrade tensorrt_dispatch

測試  TensorRT Python

$ python3

>>> import tensorrt

>>> print(tensorrt.__version__)

>>> assert tensorrt.Builder(tensorrt.Logger())

>>> import tensorrt_lean as trt

>>> print(trt.__version__)

>>> assert trt.Builder(trt.Logger())

>>> import tensorrt_dispatch as trt

>>> print(trt.__version__)

>>> assert trt.Builder(trt.Logger())

連結 https://developer.nvidia.com/tensorrt 按 GET STARTED

連結 https://developer.nvidia.com/tensorrt-getting-started 按 DOWNLOAD NOW

選擇 TensorRT 8

選擇 TensorRT 8.6 GA

TensorRT 8.6 GA for Ubuntu 20.04 and CUDA 12.0 and 12.1 DEB local repo Package

$ sudo dpkg -i nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb

$ sudo cp /var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/nv-tensorrt-local-9A1EDFBA-keyring.gpg /usr/share/keyrings/

$ sudo apt-get update

$ sudo apt-get install tensorrt

$ sudo apt-get install libnvinfer-lean8

$ sudo apt-get install libnvinfer-vc-plugin8

$ sudo apt-get install python3-libnvinfer-lean

$ sudo apt-get install python3-libnvinfer-dispatch

$ python3 -m pip install numpy

$ sudo apt-get install python3-libnvinfer-dev

$ python3 -m pip install protobuf

$ sudo apt-get install uff-converter-tf

$ python3 -m pip install numpy onnx

$ sudo apt-get install onnx-graphsurgeon

確認安裝

$ dpkg-query -W tensorrt

tensorrt        8.6.1.6-1+cuda12.0

安裝 DeepStream

$ sudo apt-get install libssl1.1

$ sudo apt-get install libgstreamer1.0-0

$ sudo apt-get install gstreamer1.0-tools

$ sudo apt-get install gstreamer1.0-plugins-good

$ sudo apt-get install gstreamer1.0-plugins-bad

$ sudo apt-get install gstreamer1.0-plugins-ugly

$ sudo apt-get install gstreamer1.0-libav

$ sudo apt-get install libgstreamer-plugins-base1.0-dev

$ sudo apt-get install libgstrtspserver-1.0-0

$ sudo apt-get install libjansson4

$ sudo apt-get install libyaml-cpp-dev

$ sudo apt-get install libjsoncpp-dev

$ sudo apt-get install protobuf-compiler

$ sudo apt-get install gcc

$ sudo apt-get install make

$ sudo apt-get install git

$ sudo apt-get install python3

$ git clone https://github.com/edenhill/librdkafka.git

$ cd librdkafka

$ git reset --hard 7101c2310341ab3f4675fc565f64f0967e135a6a

$ ./configure

$ make

$ sudo make install

$ sudo mkdir -p /opt/nvidia/deepstream/deepstream-6.3/lib

$ sudo cp /usr/local/lib/librdkafka* /opt/nvidia/deepstream/deepstream-6.3/lib

https://catalog.ngc.nvidia.com/orgs/nvidia/resources/deepstream

下載 deepstream-6.3_6.3.0-1_arm64.deb

$ wget --content-disposition 'https://api.ngc.nvidia.com/v2/resources/nvidia/deepstream/versions/6.3/files/deepstream-6.3_6.3.0-1_amd64.deb'

$ sudo apt-get install ./deepstream-6.3_6.3.0-1_amd64.deb

$ cd /opt/nvidia/deepstream/deepstream-6.3/samples/configs/deepstream-app

$ deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt 

安裝 Docker

https://docs.docker.com/engine/install/ubuntu/

$ sudo apt-get update

$ sudo apt-get install ca-certificates curl gnupg

$ sudo install -m 0755 -d /etc/apt/keyrings

$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg

$ sudo chmod a+r /etc/apt/keyrings/docker.gpg

$ echo \

  "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \

  "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \

  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

$ sudo apt-get update

$ sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

$ sudo docker run --rm hello-world

安裝 NVIDIA Container Toolkit

參考 https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html

$ curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \

  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \

    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \

    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \

  && \

    sudo apt-get update

$ sudo apt-get install -y nvidia-container-toolkit

$ sudo nvidia-ctk runtime configure --runtime=docker

$ sudo systemctl restart docker

$ sudo groupadd docker

$ sudo usermod -a -G docker $USER

$ docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

安裝 NGC CLI

參考 https://ngc.nvidia.com/setup/installers/cli

$ wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/ngc-apps/ngc_cli/versions/3.30.1/files/ngccli_linux.zip -O ngccli_linux.zip && unzip ngccli_linux.zip

$ find ngc-cli/ -type f -exec md5sum {} + | LC_ALL=C sort | md5sum -c ngc-cli.md5

$ sha256sum ngccli_linux.zip

$ chmod u+x ngc-cli/ngc

$ echo "export PATH=\"\$PATH:$(pwd)/ngc-cli\"" >> ~/.bash_profile && source ~/.bash_profile

$ ngc config set

# 直接 enter 即可

$ docker login nvcr.io

Username: $oauthtoken

Password: <Your API Key>

用 Docker 開發 DeepStream 6.3

https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_docker_containers.html

$ sudo docker pull nvcr.io/nvidia/deepstream:6.3-gc-triton-devel

$ export DISPLAY=:0

$ xhost +

$ docker run -it --rm --net=host --gpus all -e DISPLAY=$DISPLAY --device /dev/snd -v /tmp/.X11-unix/:/tmp/.X11-unix nvcr.io/nvidia/deepstream:6.3-gc-triton-devel

# cd samples/configs/deepstream-app

# deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt 

# exit

$ sudo docker ps -a

$ sudo docker stop container_id

$ sudo docker rm container_id

$ sudo docker image list

$ sudo docker image rm image_id

Ubuntu 安裝 exiv2, 開發 exif 相關程式

參考 https://github.com/Exiv2/exiv2/tree/main

參考 https://github.com/Exiv2/exiv2/tree/main#PlatformLinux

download cmake from https://cmake.org/download/

$ tar xvfz cmake-3.27.6.tar.gz

$ cd cmake-3.27.6

$ sudo apt-get install libssl-dev

$ ./bootstrap

$ make -j4

$ sudo make install

$ git clone https://github.com/Exiv2/exiv2.git

$ cd exiv2

$ sudo apt-get install --yes build-essential ccache clang cmake git google-mock libbrotli-dev libcurl4-openssl-dev libexpat1-dev libgtest-dev libinih-dev libssh-dev libxml2-utils libz-dev python3 zlib1g-dev

$ cmake -S . -B build -G "Unix Makefiles"

$ cmake --build build

$ ctest --test-dir build --verbose

$ sudo cmake --install build

$ g++ -o exifprint exifprint.cpp -lexiv2

使用 sudo 不輸入密碼

增加可以 reboot 的 myuser

$ sudo deluser myuser

$ adduser myuser

$ sudo gpasswd -a myuser sudo

$ echo "myuser ALL = NOPASSWD: /usr/sbin/reboot" | sudo tee /etc/sudoers.d/60_myuser

$ sudo chmod 0440 /etc/sudoers.d/60_myuser

若不幸輸入錯字，會產生如下錯誤

>>> /etc/sudoers: syntax error near line 24 <<<

sudo: parse error in /etc/sudoers near line 24

sudo: no valid sudoers sources found, quitting

sudo: unable to initialize policy plugin

使用下列方法修復

$ pkexec visudo

What now? 會停在此處，別怕按下 Enter

Options are:

  (e)dit sudoers file again

  e(x)it without saving changes to sudoers file

  (Q)uit and save changes to sudoers file (DANGER!)

使用 ssh 不用輸入密碼

hostA$ ssh-keygen

hostA$ ssh-copy-id "user@hostB -p 22"

hostA$ ssh user@hostB "command.sh arg1 arg2"

ubuntu 多網卡之 default route

$ cd /etc/NetworkManager/system-connections/

編輯相對網卡的檔案, 在 [ipv4] 下, 加入 route

$ sudo vi 'Wired connection 1.nmconnection'

[ipv4]

route1=0.0.0.0/0,192.168.0.254,1

install pytorch in ubuntu

參考 PyTorch 官方網站

參考 安裝舊版本

參考 安裝版本文件

$ python3 -m venv pytorch

$ source pytorch/bin/activate

$ pip3 install --upgrade --no-cache-dir pip

$ sudo update-alternatives --config cuda

$ pip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

檢查 memory leak

安裝 valgrind

$ sudo apt update

$ sudo apt install snapd

$ sudo snap install valgrind --classic

$ gcc -g -o debug_prog debug_prog.c

$ g++ -g -o debug_prog debug_prog.c

開啟兩個 terminal

$ valgrind --vgdb=yes --vgdb-error=0 --tool=memcheck --leak-check=full ./debug_prog

$ gdb ./debug_prog

(gdb) target remote | vgdb

(gdb) break linenum

(gdb) next

(gdb) step

(gdb) monitor leak_check full reachable any

(gdb) kill

(gdb) quit

若只是單純檢查 memory leak

$ valgrind --tool=memcheck --leak-check=full ./debug_prog

suppression 一些錯誤

$ valgrind --tool=memcheck --leak-check=full --gen-suppressions=all ./debug_prog

...

{

   <insert_a_suppression_name_here>

   Memcheck:Leak

   match-leak-kinds: definite

   fun:malloc

   fun:g_malloc

   obj:/usr/lib/x86_64-linux-gnu/libglib-2.0.so.0.5600.4

   fun:call_init

   fun:_dl_init

   obj:/lib/x86_64-linux-gnu/ld-2.27.so

}

...

利用上個命令的輸出，產生 local.supp 檔

$ valgrind --tool=memcheck --leak-check=full --supressions=./local.supp ./debug_prog

$ valgrind --tool=memcheck --leak-check=full --gen-suppressions=all --log-file=supp.log --supressions=./local.supp ./debug_prog

$ cat ./supp.log | ./suppressions.sh > local.supp

$ valgrind --tool=memcheck --leak-check=full --supressions=./local.supp --supressions=./gtk.supp ./debug_prog

suppressions.sh 若產生下列錯誤

awk: 34: unexpected character '&'

需改變 awk 的版本為 gawk

$ awk -W version

$ sudo apt-get update

$ sudo apt-get install gawk

去除 definitely loss

== 16,384 bytes in 1 blocks are definitely lost in loss record 1,443 of 1,448

==    at 0x4C330C5: malloc (vg_replace_malloc.c:393)

==    by 0x6A0BBD8: g_malloc (gmem.c:99)

==    by 0x6A1674B: g_quark_init (gquark.c:62)

==    by 0x40108D2: call_init (dl-init.c:72)

==    by 0x40108D2: _dl_init (dl-init.c:119)

==    by 0x40010C9: ??? (in /lib/x86_64-linux-gnu/ld-2.27.so)

下載 gst.supp 和 glib.supp gtk.supp

https://gitlab.freedesktop.org/gstreamer/common/-/blob/master/gst.supp

https://github.com/GNOME/glib/blob/main/tools/glib.supp

https://gist.github.com/pendingchaos/81feddb95c06aeb58e2f

Makefile 的

CFLAGS+= -g -O0

安裝 libglib 的 debug symbols 版本

$ echo "deb http://ddebs.ubuntu.com $(lsb_release -cs) main restricted universe multiverse

deb http://ddebs.ubuntu.com $(lsb_release -cs)-updates main restricted universe multiverse

deb http://ddebs.ubuntu.com $(lsb_release -cs)-proposed main restricted universe multiverse" | \

sudo tee -a /etc/apt/sources.list.d/ddebs.list

$ sudo apt install ubuntu-dbgsym-keyring

$ sudo apt update

$ sudo apt install libglib2.0-bin-dbgsym libglib2.0-0-dbgsym libglib2.0-dev-bin-dbgsym

export G_DEBUG=gc-friendly

export G_SLICE=always-malloc

valgrind -v --time-stamp=yes --tool=memcheck --leak-check=full \

  --gen-suppressions=all --log-file=supp.log \

  --suppressions=./glib.supp \

  --suppressions=./gst.supp \

  --suppressions=./gtk.supp \

  ./main

常用觀測記憶體使用狀態命令

$ while true; do echo -n `date +"%Y/%m/%d %H:%M:%S"`" " | tee -a aaa.log ; grep "VmRSS" /proc/26885/status | tee -a aaa.log ; sleep 60 ; done

$ watch "grep 'VmRSS\|VmPeak\|VmData\|VmStk\|VmExe\|VmLib\|Threads' /proc/26885/status" 

ubuntu cpu 工作頻率設定

$ sudo apt-get install cpufrequtils

$ cpufreq-info

$ for i in {0..15}

> do

>   sudo cpufreq-set -d 800000 -u 3200000 -c $i

> done

$ watch -n1 "grep Hz /proc/cpuinfo | grep -v model"

$ watch sensors

debug in Ubuntu

參考 讓程序崩潰時產生core dump
$ g++ -g test.cpp 

$ gcc -g test.cpp 

# 編譯程式時加入 -g 

$ ulimit -a core file size        (blocks, -c) 0

# 表示部會產生 core dump

$ ulimit -c unlimited

# 將 core file size 改為 unlimited

$ sudo apt install systemd-coredump

$ coredumpctl

# 列出所有的 core dump

$ coredumpctl gdb

# 打開最近的 core dump

$ coredumpctl gdb 123

# 打開 pid=123 的 core dump

(gdb) bt

# back trace 顯示錯誤的地方

(gdb) q

# 離開 gdb

Ubuntu 開關(當)機相關命令

顯示 Linux 開機資訊指令

dmesg -T

顯示系統關機記錄和運行級別改變的日誌

last -x

/var/log/syslog

/var/log/syslog.1

The system log typically contains the greatest deal of information by default about your Ubuntu system.

Ubuntu 18.04 重灌

因為 Ubuntu 18.04 只能安裝 DeepStream 6.0.1

在 Ubuntu 20.04 才能安裝 DeepStream 6.1

所以在 Ubuntu 18.04 上安裝 Docker Ubuntu 20.04

版本對應如下

===========================

DS 6.1

Ubuntu 20.04

GCC 9.4.0

CUDA 11.6.1

cuDNN 8.4.0.27

TRT 8.2.5.1

Display Driver:R510.47.03

GStreamer 1.16.2

OpenCV 4.2.0

deepstream:6.1

===========================

DS 6.0.1

Ubuntu 18.04

GCC 7.3.0

CUDA 11.4.1

cuDNN 8.2+

TRT 8.0.1

Display Driver:R470.63.01

GStreamer 1.14.5

OpenCV 3.4.0

deepstream:6.0.1

===========================

安裝作業系統

BIOS 選擇開機

Install Ubuntu

Installation type 選 Something else

Create partition/Mount point 選擇 /

Settings/Details/Users

Unlock

Automatic Login: ON

更新系統，安裝一些常用套件

$ sudo apt-get update

$ sudo apt-get upgrade

$ sudo apt-get install ssh

$ sudo apt-get install python3-pip

mount nfs

$ sudo apt-get install nfs-common

$ sudo mount -t nfs ip:/share_folder /mount_folder

$ sudo vi /etc/fstab

#中間的空格要使用 tab

ip:/share_folder /mnt/mount_folder nfs defaults,bg 0 0

$ sudo mount -a

vnc

$ sudo apt-get install x11vnc

$ sudo x11vnc -storepasswd

$ sudo chown user.group ~/.vnc/passwd

安裝顯示卡驅動，CUDA 和 CUDNN

$ sudo ubuntu-drivers devices

$ sudo apt-get install nvidia-driver-510

https://developer.nvidia.com/cuda-downloads

選擇 Archive of Previous CUDA Releases

選擇 CUDA Toolkit 11.4.1

選擇 deb(local), deb(network) 不可使用了

$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin

$ sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600

$ wget https://developer.download.nvidia.com/compute/cuda/11.4.1/local_installers/cuda-repo-ubuntu1804-11-4-local_11.4.1-470.57.02-1_amd64.deb

$ sudo dpkg -i cuda-repo-ubuntu1804-11-4-local_11.4.1-470.57.02-1_amd64.deb

$ sudo apt-key add /var/cuda-repo-ubuntu1804-11-4-local/7fa2af80.pub

$ sudo apt-get update

$ sudo apt-get -y install cuda-11-4

之後再安裝一遍 CUDA Toolkit 11.3.1(因為 TensorRT 8.0.1 需要 CUDA-11-3)

$ sudo apt-get -y install cuda-11-3

之後再安裝一遍 CUDA Toolkit 11.6.1(因為 DeepStream 6.1 需要 CUDA-11-6)

$ sudo apt-get -y install cuda-11-6

$ update-alternatives --display cuda

$ update-alternatives --config cuda

參考 https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

到 2.2. Downloading cuDNN for Linux

下載 Local Install for Ubuntu18.04 x86_64(Deb)

$ sudo apt-get install zlib1g

$ sudo dpkg -i cudnn-local-repo-ubuntu1804-8.4.1.50_1.0-1_amd64.deb

$ sudo cp /var/cudnn-local-repo-ubuntu1804-8.4.1.50/cudnn-local-BA71F057-keyring.gpg /usr/share/keyrings/

$ sudo apt-get update

$ apt list -a libcudnn8

$ sudo apt-get install libcudnn8=8.4.1.50-1+cuda11.6

$ sudo apt-get install libcudnn8-dev=8.4.1.50-1+cuda11.6

$ sudo apt-get install libcudnn8-samples=8.4.1.50-1+cuda11.6

安裝 TensorRT 8.0.1

因為使用 Docker 安裝 DeepStream 6.1, 所以不用安裝 TensorRT 8.2.5.1

參考 https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html

在 TensorRT Archives 切換安裝文件版本

選擇 8.0.1 的 TensorRT Installation Guide

在 3. Downloading TensorRT

連結 https://developer.nvidia.com/tensorrt 按 GET STARTED

連結 https://developer.nvidia.com/tensorrt-getting-started 按 DOWNLOAD NOW

選擇 TensorRT 8

選擇 TensorRT 8.0 GA

選擇 TensorRT 8.0.1 GA for Ubuntu 18.04 and CUDA 11.3 DEB local repo package

$ sudo dpkg -i nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626_1-1_amd64.deb

$ sudo apt-key add /var/nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626/7fa2af80.pub

$ sudo apt-get update

$ sudo apt --fix-broken install

$ sudo apt-get upgrade

$ sudo apt-get install tensorrt

安裝 DeepStream

因為不選擇最新的版本

參考 https://docs.nvidia.com/metropolis/deepstream-archive.html

選擇版本 6.0.1 DeepStream SDK Development Guid

到 dGPU Setup for Ubuntu

$ sudo apt install libssl1.0.0

$ sudo apt install libgstreamer1.0-0

$ sudo apt install gstreamer1.0-tools

$ sudo apt install gstreamer1.0-plugins-good

$ sudo apt install gstreamer1.0-plugins-bad

$ sudo apt install gstreamer1.0-plugins-ugly

$ sudo apt install gstreamer1.0-libav

$ sudo apt install libgstrtspserver-1.0-0

$ sudo apt install libjansson4

$ sudo apt install gcc

$ sudo apt install make

$ sudo apt install git

$ sudo apt install python3

$ cd /usr/bin

$ sudo ln -s python3 python

$ git clone https://github.com/edenhill/librdkafka.git

$ cd librdkafka

$ git reset --hard 7101c2310341ab3f4675fc565f64f0967e135a6a

$ ./configure

$ make

$ sudo make install

$ sudo mkdir -p /opt/nvidia/deepstream/deepstream-6.0/lib

$ sudo cp /usr/local/lib/librdkafka* /opt/nvidia/deepstream/deepstream-6.0/lib

下載 Deepstream 6.0 dGPU Debian package

https://developer.nvidia.com/deepstream-6.0_6.0.1-1_amd64deb

$ sudo apt-get install ./deepstream-6.0_6.0.1-1_amd64.deb

$ rm ${HOME}/.cache/gstreamer-1.0/registry.x86_64.bin

$ cd /opt/nvidia/deepstream/deepstream-6.0/samples/configs/deepstream-app/

$ deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt

安裝  Nvidia Docker

參考 https://docs.docker.com/engine/install/ubuntu/

$ sudo apt-get update

$ sudo apt-get install ca-certificates

$ sudo apt-get install curl

$ sudo apt-get install gnupg

$ sudo apt-get install lsb-release

$ sudo mkdir -p /etc/apt/keyrings

$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg

$ echo \

  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \

  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

$ sudo apt-get update

$ sudo apt-get install docker-ce docker-ce-cli containerd.io docker-compose-plugin

$ sudo docker run --rm hello-world

參考 https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker

$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \

      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \

      && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \

            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \

            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

$ sudo apt-get update

$ sudo apt-get install -y nvidia-docker2

$ sudo systemctl restart docker

$ sudo groupadd docker

$ sudo usermod -a -G docker $USER

$ sudo reboot

安裝 NGC CLI

參考 https://ngc.nvidia.com/setup/installers/cli

$ wget --content-disposition https://ngc.nvidia.com/downloads/ngccli_linux.zip && \

  unzip ngccli_linux.zip && \

  chmod u+x ngc-cli/ngc

$ find ngc-cli/ -type f -exec md5sum {} + | LC_ALL=C sort | md5sum -c ngc-cli.md5

$ echo "export PATH=\"\$PATH:$(pwd)/ngc-cli\"" >> ~/.bash_profile && source ~/.bash_profile

$ ngc config set

# 直接 enter 即可

$ docker login nvcr.io

Username: $oauthtoken

Password: <Your API Key>

使用 Docker 安裝 TensorRT OSS

開啟 https://github.com/nvidia/TensorRT

切換至 Tags 8.0.1

$ git clone -b master https://github.com/nvidia/TensorRT TensorRT_OSS-8.0.1

$ cd TensorRT_OSS-8.0.1/

$ git describe --tags

8.2.0-EA-2-g96e2397

$ git tag -l

$ git branch -r

$ git checkout 8.0.1

$ git log -1

$ git describe --tags

8.0.1

$ git submodule update --init --recursive

$ vi docker/ubuntu-18.04.Dockerfile

修改下列一行

RUN cd /usr/local/bin && wget https://ngc.nvidia.com/downloads/ngccli_cat_linux.zip && \

  unzip ngccli_cat_linux.zip && chmod u+x ngc-cli/ngc && \

  rm ngccli_cat_linux.zip ngc-cli.md5 && echo "no-apikey\nascii\n" | ngc-cli/ngc config set

$ cat docker/ubuntu-18.04.Dockerfile | grep CUDA_VERSION

ARG CUDA_VERSION=11.3.1

$ ./docker/build.sh --file docker/ubuntu-18.04.Dockerfile --tag tensorrt-ubuntu18.04-cuda11.3 --cuda 11.3.1

$ ./docker/launch.sh --tag tensorrt-ubuntu18.04-cuda11.3 --gpus all

/workspace$ cd $TRT_OSSPATH

/workspace/TensorRT$ mkdir -p build && cd build

/workspace/TensorRT/build$ cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out

/workspace/TensorRT/build$ make -j$(nproc)

/workspace/TensorRT/build$ exit

$ mkdir backup

$ sudo mv /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.0.1 backup/

$ sudo cp build/out/libnvinfer_plugin.so.8.0.1 /usr/lib/x86_64-linux-gnu/

另外還要再安裝 TensorRT 8.2.1 給 Docker Ubuntu 20.04

將 8.0.1 改成 8.2.1, 18.04 改成 20.04, 11.3.1 改成 11.4.2, 但不需安裝

用 Docker 開發 DeepStream 6.0.1

參考 https://docs.nvidia.com/metropolis/deepstream/6.0.1/dev-guide/text/DS_docker_containers.html

$ docker pull nvcr.io/nvidia/deepstream:6.0.1-devel

$ xhost +

access control disabled, clients can connect from any host

$ sudo docker run --gpus all -it --rm --net=host \

  -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/localtime:/etc/localtime \

  -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-6.0 nvcr.io/nvidia/deepstream:6.0.1-devel

# update-alternatives --display cuda

# cat /etc/os-release

# cd samples/configs/deepstream-app

# deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt 

# cd /opt/nvidia/deepstream/deepstream-6.0/sources/apps/sample_apps/deepstream-app

# export CUDA_VER=11.4

# make

# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/nvidia/deepstream/deepstream-6.0/lib/gst-plugins/

# export URI=rtsp://user:passwd@192.168.0.108:554/live1s2.sdp

# gst-launch-1.0 uridecodebin uri=$URI ! nvvideoconvert ! nveglglessink

# exit

用 Docker 開發 DeepStream 6.1

https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_docker_containers.html

$ docker pull nvcr.io/nvidia/deepstream:6.1-devel

$ xhost +

access control disabled, clients can connect from any host

$ sudo docker run --gpus all -it --rm --net=host \

  -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/localtime:/etc/localtime \

  -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-6.1 nvcr.io/nvidia/deepstream:6.1-devel

# cd samples/configs/deepstream-app

# deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt 

# cd /opt/nvidia/deepstream/deepstream-6.1/sources/apps/sample_apps/deepstream-app

# export CUDA_VER=11.6

# make

# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/nvidia/deepstream/deepstream-6.1/lib/gst-plugins/

# export URI=rtsp://user:passwd@192.168.0.108:554/live1s2.sdp

# gst-launch-1.0 uridecodebin uri=$URI ! nvvideoconvert ! nveglglessink

# exit

用 Docker DeepStream 6.0.1 測試 Integrate TAO model with DeepStream SDK

$ git clone https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git deepstream_tao_apps-tao3.0_ds6.0.1

$ sudo apt install gitk

$ cd deepstream_tao_apps-tao3.0_ds6.0.1/

$ git branch -r

$ git checkout release/tao3.0_ds6.0.1

$ git log -1

$ sudo docker run --gpus all -it --rm --net=host \

> -v /etc/localtime:/etc/localtime \

> -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY \

> -v /home/mark/Data/TensorRT/TensorRT_OSS-8.0.1/:/home/TensorRT \

> -v /home/mark/Data/DeepStream/deepstream_tap_apps/deepstream_tao_apps-tao3.0_ds6.0.1/:/home/deepstream_tao_apps \

> -w /opt/nvidia/deepstream/deepstream-6.0 nvcr.io/nvidia/deepstream:6.0.1-devel

# cd /home/deepstream_tao_apps/

# ./download_models.sh 

# export CUDA_VER=11.4

# make

# cp /home/TensorRT/build/out/libnvinfer_plugin.so.8.0.1 /usr/lib/x86_64-linux-gnu/

# ./apps/tao_detection/ds-tao-detection -c configs/frcnn_tao/pgie_frcnn_tao_config.txt -i /opt/nvidia/deepstream/deepstream-6.0/samples/streams/sample_720p.h264 -d

# cd /opt/nvidia/deepstream/deepstream-6.0/sources/gst-plugins/gst-nvdsvideotemplate/

# make

# cp libnvdsgst_videotemplate.so /opt/nvidia/deepstream/deepstream-6.0/lib/gst-plugins/

# rm -rf ~/.cache/gstreamer-1.0/

# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/nvidia/deepstream/deepstream-6.0/lib/cvcore_libs/

# cd /home/deepstream_tao_apps/apps/tao_others/

# make

# export URI=rtsp://user:passwd@192.168.0.108:554/live1s2.sdp

# cd deepstream-bodypose2d-app/

# ./deepstream-bodypose2d-app 3 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt $URI ./body2dout

# cd ../deepstream-emotion-app/

# ./deepstream-emotion-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./landmarks

# cd ../deepstream-faciallandmark-app/

# ./deepstream-faciallandmark-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./landmarks

# cd ../deepstream-gaze-app/

# ./deepstream-gaze-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./gazenet

# cd ../deepstream-gesture-app/

# ./deepstream-gesture-app 3 3 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt $URI ./gesture

# cd ../deepstream-heartrate-app/

# ./deepstream-heartrate-app 3 $URI ./heartrate

# exit

用 Docker DeepStream 6.1 測試 Integrate TAO model with DeepStream SDK

$ git clone https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git deepstream_tao_apps-tao3.0_ds6.1ga

$ sudo apt install gitk

$ cd deepstream_tao_apps-tao3.0_ds6.1ga/

$ git branch -r

$ git checkout release/tao3.0_ds6.1ga

$ git log -1

$ sudo docker run --gpus all -it --rm --net=host \

> -v /etc/localtime:/etc/localtime \

> -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY \

> -v /home/mark/Data/TensorRT/TensorRT_OSS-8.2.1/:/home/TensorRT \

> -v /home/mark/Data/DeepStream/deepstream_tap_apps/deepstream_tao_apps-tao3.0_ds6.1ga/:/home/deepstream_tao_apps \

> -w /opt/nvidia/deepstream/deepstream-6.1 nvcr.io/nvidia/deepstream:6.1-devel

# cp /home/TensorRT/build/out/libnvinfer_plugin.so.8.2.1 /usr/lib/x86_64-linux-gnu/

# cd /usr/lib/x86_64-linux-gnu/

# rm libnvinfer_plugin.so.8

# ln -s libnvinfer_plugin.so.8.2.1 libnvinfer_plugin.so.8

# cd /home/deepstream_tao_apps/

# ./download_models.sh 

# export CUDA_VER=11.6

# make

# ./apps/tao_detection/ds-tao-detection -c configs/frcnn_tao/pgie_frcnn_tao_config_dgpu.txt -i /opt/nvidia/deepstream/deepstream-6.1/samples/streams/sample_720p.h264 -d

# cd /opt/nvidia/deepstream/deepstream-6.1/sources/gst-plugins/gst-nvdsvideotemplate/

# make

# cp libnvdsgst_videotemplate.so /opt/nvidia/deepstream/deepstream-6.1/lib/gst-plugins/

# rm -rf ~/.cache/gstreamer-1.0/

# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/nvidia/deepstream/deepstream-6.1/lib/cvcore_libs/

# cd /home/deepstream_tao_apps/apps/tao_others/

# make

# export URI=rtsp://user:passwd@192.168.0.108:554/live1s2.sdp

# cd deepstream-bodypose2d-app/

# ./deepstream-bodypose2d-app 3 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt 0 0 $URI ./body2dout

# cd ../deepstream-emotion-app/

# ./deepstream-emotion-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./landmarks

# cd ../deepstream-faciallandmark-app/

# ./deepstream-faciallandmark-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./landmarks

# cd ../deepstream-gaze-app/

# ./deepstream-gaze-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./gazenet

# cd ../deepstream-gesture-app/

# ./deepstream-gesture-app 3 3 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt $URI ./gesture

# cd ../deepstream-heartrate-app/

# ./deepstream-heartrate-app 3 $URI ./heartrate

# exit

安裝其他元件

參考 Ubuntu 18.04 重灌 上的 安裝 CMake 和 安裝 OpenCV

WireShark in Ubuntu

$ sudo apt-get update
$ sudo apt-get install wireshark

.

.

.

Do you want to continue? [Y/n]

Should non-superusers be able to capture packets? <Yes>

$ sudo usermod -aG wireshark $(whoami)

$ sudo reboot

$ wireshark

apt-get update error

# apt-get update

Err:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease

  The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC

.

.

.

W: GPG error: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC

E: The repository 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease' is no longer signed.

# apt-key del 7fa2af80

# apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub

# apt-get update

ubuntu install GTK3

$ sudo apt-get install libgtk-3-dev

$ sudo apt-get install libclutter-gst-3.0

Ubuntu 加入 RamDisk

$ sudo mkdir /mnt/ramdisk

$ sudo chmod 1777 /mnt/ramdisk

$ sudo vi /etc/fstab

tmpfs /mnt/ramdisk tmpfs rw,size=1G 0 0

$ sudo reboot

set frpc as service in Ubuntu

vi /etc/systemd/system/frpc.service

[Unit]

Description=frp client

Wants=network-online.target

After=network.target network-online.target

[Service]

ExecStart=/path_to/frpc -c /path_to/frpc.ini

[Install]

WantedBy=multi-user.target

$ sudo systemctl daemon-reload

$ sudo systemctl enable frpc

$ sudo systemctl start frpc

$ sudo systemctl status frpc

$ sudo systemctl staop frpc