為在 jetson nano 的 conky 加速

conky 若是利用 exec, execp 等命令，會大量耗費資源

之前使用 execi 執行 tegrastats 取得一些系統資訊

直接改用 C, 編寫出 jetson_fan, 

jetson_temp_ao, jetson_temp_cpu, jetson_temp_gpu,

jetson_power_cpu, jetson_power_gpu, jetson_power_all

等變數

$ vi .conkyrc

RAM:${jetson_ram_usage}${alignr}${jetson_ram}

RAM lfb:${alignr}${jetson_ram_lfb}

SWAP:${jetson_swap_usage}${alignr}${jetson_swap}

SWAP Cached:${alignr}${jetson_swap_cached}

EMC Bandwidth:${jetson_emc} ${jetson_emc_bar}

CPU0:${jetson_cpu_frq 0} ${jetson_cpu_bar 0}

CPU1:${jetson_cpu_frq 1} ${jetson_cpu_bar 1}

CPU2:${jetson_cpu_frq 2} ${jetson_cpu_bar 2}

CPU3:${jetson_cpu_frq 3} ${jetson_cpu_bar 3}

GPU  ${jetson_gpu} ${jetson_gpu_bar}

${font :blod:size=12}Temperature ${hr}

${font :normal:size=10}Thermal:${alignr} ${jetson_temp_thermal}

PLL:${alignr} ${jetson_temp_pll}

CPU:${alignr} ${jetson_temp_cpu}

Wifi:${alignr} ${jetson_temp_wifi}

PMIC:${alignr} ${jetson_temp_pmic}

GPU:${alignr} ${jetson_temp_gpu}

AO:${alignr} ${jetson_temp_ao}

Fan Speed:${jetson_fan} ${jetson_fan_bar}

以下是 conky source 的 patch

conky in jetson nano

參考 Conky Installation

安裝相依套件

$ sudo apt-get install cmake

$ sudo apt-get install libimlib2-dev

$ sudo apt-get install libncurses5-dev

$ sudo apt-get install libx11-dev

$ sudo apt-get install libxdamage-dev

$ sudo apt-get install libxft-dev

$ sudo apt-get install libxinerama-dev

$ sudo apt-get install libxml2-dev

$ sudo apt-get install libxext-dev

$ sudo apt-get install libcurl4-openssl-dev

$ sudo apt-get install liblua5.3-dev

確認 cmake 版本要 >=3.8

$ cmake --version

在桌面顯示 主機資訊

參考 Ubuntu 20.04 System Monitoring with Conky widgets, Ubuntu 18.04也可用
$ sudo apt-get install conky-all

開始/Startup Applications

加入 /usr/bin/conky

$ sudo reboot

$ cp /etc/conky/conky.conf ~/.conkyrc

$ vi ~/.conkyrc

conky.config = {

update_interval = 1,

cpu_avg_samples = 2,

net_avg_samples = 2,

out_to_console = false,

override_utf8_locale = true,

double_buffer = true,

no_buffers = true,

text_buffer_size = 32768,

imlib_cache_size = 0,

own_window = true,

own_window_type = 'normal',

own_window_argb_visual = true,

own_window_argb_value = 80,

own_window_hints = 'undecorated,below,sticky,skip_taskbar,skip_pager',

border_inner_margin = 5,

border_outer_margin = 0,

xinerama_head = 1,

alignment = 'bottom_right',

gap_x = 0,

gap_y = 33,

draw_shades = false,

draw_outline = false,

draw_borders = false,

draw_graph_borders = false,

use_xft = true,

font = 'Ubuntu Mono:size=12',

xftalpha = 0.8,

uppercase = false,

default_color = 'white',

own_window_colour = '#000000',

minimum_width = 300, minimum_height = 0,

alignment = 'top_right',

};

conky.text = [[

${font sans-serif:bold:size=16}${time %Y-%m-%d}${alignr}${time %H:%M:%S}${font}

${font sans-serif:bold:size=10}SYSTEM ${hr 2}

${font sans-serif:normal:size=8}$sysname $kernel $alignr $machine

Host:$alignr$nodename

Uptime:$alignr$uptime

File System: $alignr${fs_type}

Processes: $alignr ${execi 1000 ps aux | wc -l}

${font sans-serif:bold:size=10}CPU ${hr 2}

${font sans-serif:normal:size=8}${execi 1000 grep model /proc/cpuinfo | cut -d : -f2 | tail -1 | sed 's/\s//'}

${font sans-serif:normal:size=8}CPU:${cpugraph cpu0 50}

CPU: ${cpu cpu0}% ${cpubar cpu0}

${font sans-serif:bold:size=10}Nvidia GPU ${hr 2}

${font sans-serif:normal:size=8}${execpi 1000 (nvidia-smi --query-gpu=gpu_name --format=csv,noheader)}

Temperature:${alignr}${execpi 3 (nvidia-smi --query-gpu=temperature.gpu --format=csv,noheader)}°C

Fan Speed:${alignr}${execpi 3 (nvidia-smi --query-gpu=fan.speed --format=csv,noheader)}

Utilization:${alignr}${execpi 3 (nvidia-smi --query-gpu=utilization.gpu --format=csv,noheader)}

Power:${alignr}${execpi 3 (nvidia-smi --query-gpu=power.draw --format=csv,noheader,nounits)}/${execpi 10 (nvidia-smi --query-gpu=power.default_limit --format=csv,noheader)}

Memory:${alignr}${execpi 3 (nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits)}/${execpi 10 (nvidia-smi --query-gpu=memory.total --format=csv,noheader)}

${font Courier-New:normal:size=8}PID${alignc}Name${alignr}Memory

${execpi 3 (nvidia-smi -q|tr -d " "|awk -F ":" '/ProcessID/{printf $2"\t";getline;getline;printf "%-28s",substr($2, 1, 28)"\t";getline;printf $2"\n"}')}

${font sans-serif:bold:size=10}MEMORY ${hr 2}

${font sans-serif:normal:size=8}RAM $alignc $mem / $memmax $alignr $memperc%

$membar

SWAP $alignc ${swap} / ${swapmax} $alignr ${swapperc}%

${swapbar}

${font sans-serif:bold:size=10}DISK USAGE ${hr 2}

${font sans-serif:normal:size=8}/ $alignc ${fs_used /} / ${fs_size /} $alignr ${fs_used_perc /}%

${fs_bar /}

${font Ubuntu:bold:size=10}NETWORK ${hr 2}

${font sans-serif:normal:size=8}Local IPs:${alignr}External IP:

${execi 1000 ip a | grep inet | grep -vw lo | grep -v inet6 | cut -d \/ -f1 | sed 's/[^0-9\.]*//g'}  ${alignr}${execi 1000  wget -q -O- http://ipecho.net/plain; echo}

${font sans-serif:normal:size=8}Down: ${downspeed eno1}  ${alignr}Up: ${upspeed eno1} 

${color lightgray}${downspeedgraph eno1 80,130 } ${alignr}${upspeedgraph eno1 80,130 }$color

${font sans-serif:bold:size=10}TOP PROCESSES ${hr 2}

${font sans-serif:normal:size=8}Name $alignr PID   CPU%   MEM%${font sans-serif:normal:size=8}

${top name 1} $alignr ${top pid 1} ${top cpu 1}% ${top mem 1}%

${top name 2} $alignr ${top pid 2} ${top cpu 2}% ${top mem 2}%

${top name 3} $alignr ${top pid 3} ${top cpu 3}% ${top mem 3}%

${top name 4} $alignr ${top pid 4} ${top cpu 4}% ${top mem 4}%

${top name 5} $alignr ${top pid 5} ${top cpu 5}% ${top mem 5}%

${top name 6} $alignr ${top pid 6} ${top cpu 6}% ${top mem 6}%

${top name 7} $alignr ${top pid 7} ${top cpu 7}% ${top mem 7}%

${top name 8} $alignr ${top pid 8} ${top cpu 8}% ${top mem 8}%

${top name 9} $alignr ${top pid 9} ${top cpu 9}% ${top mem 9}%

${top name 10} $alignr ${top pid 10} ${top cpu 10}% ${top mem 10}%

]];

conky 常用變數參考

其中花費最多時間的是

nvidia-smi -q|tr -d " "|awk -F ":" '/ProcessID/{printf $2"\t";getline;getline;printf "%-28s",substr($2, 1, 28)"\t";getline;printf $2"\n"}'

增加 Nvidia GPU 資料