DEV Community: Bruno Verachten

Compile FFmpeg on the Odroid XU4 to get hardware acceleration

Bruno Verachten — Wed, 22 Apr 2026 07:44:26 +0000

Originally published on November 28, 2019 at https://bruno.verachten.fr/2019/11/28/ffmpeg-for-xu4/

sudo apt-get build-dep ffmpeg

sudo apt install debhelper libchromaprint-dev frei0r-plugins-dev libgnutls28-dev libx265-dev ladspa-sdk libaom-dev libserd-dev libsord-dev libsratom-dev libsndfile1-dev libiec61883-dev libavc1394-dev libavc1394-0 libass-dev libbluray-dev libbs2b-dev libcaca-dev

 mkdir ffmpeg_sources
   36  cd ffmpeg_sources
   37  wget http://deb.debian.org/debian/pool/main/f/ffmpeg/ffmpeg_4.1.4.orig.tar.xz
   38  xz -d ffmpeg_4.1.4.orig.tar.xz
   39  tar -xf ffmpeg_4.1.4.orig.tar
   40  wget http://deb.debian.org/debian/pool/main/f/ffmpeg/ffmpeg_4.1.4.orig.tar.xz
   41  ll
   42  cd ffmpeg-4.1.4/
   43  wget http://deb.debian.org/debian/pool/main/f/ffmpeg/ffmpeg_4.1.4-1~deb10u1.debian.tar.xz
   44  xz -d ffmpeg_4.1.4-1~deb10u1.debian.tar.xz
   45  ll
   46  tar -xf ffmpeg_4.1.4-1~deb10u1.debian.tar
   47  ll
   48  rm ffmpeg_4.1.4-1~deb10u1.debian.tar

sudo -E dpkg-buildpackage -us -uc -d

ffmpeg -i bbb_sunflower_1080p_30fps_stereo_abl.mp4 -an -vcodec h264_v4l2m2m -b:v 2M -pix_fmt nv21 output.mp4
ffmpeg version 4.1.4-1~deb10u1 Copyright (c) 2000-2019 the FFmpeg developers
  built with gcc 8 (Debian 8.3.0-6)
  configuration: --prefix=/usr --extra-version='1~deb10u1' --toolchain=hardened --libdir=/usr/lib/arm-linux-gnueabihf --incdir=/usr/include/arm-linux-gnueabihf --arch=arm --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 22.100 / 56. 22.100
  libavcodec     58. 35.100 / 58. 35.100
  libavformat    58. 20.100 / 58. 20.100
  libavdevice    58.  5.100 / 58.  5.100
  libavfilter     7. 40.101 /  7. 40.101
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  3.100 /  5.  3.100
  libswresample   3.  3.100 /  3.  3.100
  libpostproc    55.  3.100 / 55.  3.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'bbb_sunflower_1080p_30fps_stereo_abl.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 1
    compatible_brands: isomavc1
    creation_time   : 2013-12-16T17:46:34.000000Z
    title           : Big Buck Bunny, Sunflower version
    artist          : Blender Foundation 2008, Janus Bager Kristensen 2013
    comment         : Creative Commons Attribution 3.0 - http://bbb3d.renderfarming.net
    genre           : Animation
    composer        : Sacha Goedegebure
  Duration: 00:10:34.53, start: 0.000000, bitrate: 5482 kb/s
    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1920x2160 [SAR 1:1 DAR 8:9], 4999 kb/s, 30 fps, 30 tbr, 30k tbn, 60 tbc (default)
    Metadata:
      creation_time   : 2013-12-16T17:46:34.000000Z
      handler_name    : GPAC ISO Video Handler
    Stream #0:1(und): Audio: mp3 (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 160 kb/s (default)
    Metadata:
      creation_time   : 2013-12-16T17:46:41.000000Z
      handler_name    : GPAC ISO Audio Handler
    Stream #0:2(und): Audio: ac3 (ac-3 / 0x332D6361), 48000 Hz, 5.1(side), fltp, 320 kb/s (default)
    Metadata:
      creation_time   : 2013-12-16T17:46:41.000000Z
      handler_name    : GPAC ISO Audio Handler
    Side data:
      audio service type: main
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (native) -> h264 (h264_v4l2m2m))
Press [q] to stop, [?] for help
[h264_v4l2m2m @ 0x575a90] driver 'exynos-gsc' on card 'exynos-gsc gscaler'
    Last message repeated 1 times
[h264_v4l2m2m @ 0x575a90] driver 's5p-mfc' on card 's5p-mfc-enc'
[h264_v4l2m2m @ 0x575a90] Using device /dev/video11
[h264_v4l2m2m @ 0x575a90] driver 's5p-mfc' on card 's5p-mfc-enc'
[h264_v4l2m2m @ 0x575a90] h264 profile not found
[h264_v4l2m2m @ 0x575a90] Encoder adjusted: qmin (0), qmax (51)
Output #0, mp4, to 'output.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 1
    compatible_brands: isomavc1
    composer        : Sacha Goedegebure
    title           : Big Buck Bunny, Sunflower version
    artist          : Blender Foundation 2008, Janus Bager Kristensen 2013
    comment         : Creative Commons Attribution 3.0 - http://bbb3d.renderfarming.net
    genre           : Animation
    encoder         : Lavf58.20.100
    Stream #0:0(und): Video: h264 (h264_v4l2m2m) (avc1 / 0x31637661), nv21, 1920x2160 [SAR 1:1 DAR 8:9], q=2-31, 2000 kb/s, 30 fps, 15360 tbn, 30 tbc (default)
    Metadata:
      creation_time   : 2013-12-16T17:46:34.000000Z
      handler_name    : GPAC ISO Video Handler
      encoder         : Lavc58.35.100 h264_v4l2m2m
[mp4 @ 0x5695f0] Non-monotonous DTS in output stream 0:0; previous: 0, current: 0; changing to 1. This may result in incorrect timestamps in the output file.
[mp4 @ 0x5695f0] Non-monotonous DTS in output stream 0:0; previous: 832000, current: 0; changing to 832001. This may result in incorrect timestamps in the output file.

Terminal recording on the OrangePi Zero

Bruno Verachten — Wed, 22 Apr 2026 07:44:23 +0000

Originally published on January 3, 2020 at https://bruno.verachten.fr/2020/01/03/terminal-recording-on-the-orangepi-zero/

Terminal recording on the OrangePi Zero

Lately, I started writing a few articles on the OrangePi Zero, as I am trying to use it as part of a conference recording tool and to reference what I know about this board on the OrangePi Community docs website.

Of course, I have to show my command lines, and their results.

I used to post my commands on gist, then use link:https://carbon.now.sh/?bg=rgba(171%2C 184%2C 195%2C 1[Carbon]&t=seti&wt=none&l=auto&ds=true&dsyoff=20px&dsblur=68px&wc=true&wa=true&pv=56px&ph=56px&ln=false&fm=Hack&fs=14px&lh=133%&si=false&es=2x&wm=false) to generate an image with the commands and their outputs… But that’s quite a lot of boring work…

I then discovered asciinema and the recording that can be transformed into a GIF file thanks to asciicast2gif.

It worked pretty well under Ubuntu for my previous projects, but it was not really practical to launch it, issue an ssh command, and then exit twice before having the recording… and modifying it to get rid of the SSH command.

Furthermore, GIF are not usable for visually impaired people, so using something like SVG where they could copy and paste would be better in my opinion…

Unfortunately, SVG is not handled by medium, so it will have to be used on other media, like my Ghost instance (not available yet).

Enters termtosvg. That’s a wonderful tool aimed at producing animated SVG after recording terminal sessions.

It compiled like a breeze (well, a strong, smelly and cold breeze) on the Zero, so I used it quite intensively (before discovering it could not be used on Medium).

I then searched for tools able to transform an animated SVG to GIF, so that I would not have to redo all my terminal recordings… and was not able to do so.

There is one library called librsvg that could be used by ffmpeg, but I have not been able to compile it on the Zero, and it’s just an hypothesis, there is no guarantee at all that it could be used to produce GIF files from animated SVG files.

termtosvg can produce files than can be used by asciinema and then asciicast2gif to produce gif files, but the process is cumbersome and does not work on the Zero. I had to use a X86 Linux machine to do it.

Anyway, with this article, you won’t be able to produce GIF images starting from a terminal recording if you’re using exclusively a Zero, but beautiful animated SVG that you will be able to use on your blog, or anywhere else where animated SVG is supported.

If you’re brave enough to also use an X86 machine, go to the end of this article, and take time to get married, get children before getting your first GIF file.

TermToSVG installation

TL;DR

git config --global http.proxy http://proxy-machine:proxy-port
sudo fallocate -l 1G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile
sudo nano /etc/fstab
git clone https://github.com/nbedos/termtosvg.git
sudo apt-get install python3-setuptools libxml2-dev libxslt-dev python3-dev python3-pip
pip3 install wheel
cd termtosvg
echo "Let's modify the Makefile so that we use python3 and pip3 as I can't get rid of Python2.7.x on this machine..."
echo "See at https://gist.github.com/gounthar/b1870915710a0437b4e4eb8690e93553#file-makefile-patch"
make build
pip3 install dist/termtosvg-1.1.0-py3-none-any.whl

The real installation process

Let’s try to install termtosvg on the Zero. First of all, let’s configure the proxy if needed and then get the source code:

poddingue@orangepizero:~$ git clone https://github.com/nbedos/termtosvg.git
Cloning into 'termtosvg'...
remote: Enumerating objects: 99, done.
remote: Counting objects: 100% (99/99), done.
remote: Compressing objects: 100% (59/59), done.
remote: Total 1592 (delta 59), reused 68 (delta 40), pack-reused 1493
Receiving objects: 100% (1592/1592), 1.37 MiB | 2.08 MiB/s, done.
Resolving deltas: 100% (930/930), done.
poddingue@orangepizero:~$ cd termtosvg/
poddingue@orangepizero:~/termtosvg$ make build
rm -rf dist && \
python setup.py sdist bdist_wheel
usage: setup.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]
   or: setup.py --help [cmd1 cmd2 ...]
   or: setup.py --help-commands
   or: setup.py cmd --help

error: invalid command 'bdist_wheel'
make: *** [Makefile:31: build] Error 1

Of course, the setuptools module can’t be found. I have not installed/configured Python2 setup tools. But as I had already done if for Python3, I modified the Makefile so that python is now python3, and pip is now pip3:

PIP=pip3
PYTHON=python3

If I had not done it yet, it would have looked that way:

poddingue@orangepizero:~/termtosvg$ sudo apt-get install python3-setuptools
Reading package lists... Done
Building dependency tree
Reading state information... Done
Suggested packages:
  python-setuptools-doc
The following NEW packages will be installed:
  python3-setuptools
0 upgraded, 1 newly installed, 0 to remove and 0 not upgraded.
Need to get 306 kB of archives.
After this operation, 1,353 kB of additional disk space will be used.
Get:1 http://httpredir.debian.org/debian buster/main armhf python3-setuptools all 40.8.0-1 [306 kB]
Fetched 306 kB in 0s (1,225 kB/s)
Selecting previously unselected package python3-setuptools.
(Reading database ... 66711 files and directories currently installed.)
Preparing to unpack .../python3-setuptools_40.8.0-1_all.deb ...
Unpacking python3-setuptools (40.8.0-1) ...
Setting up python3-setuptools (40.8.0-1) ...

Now that Python setup tools are installed, let’s try it again:

poddingue@orangepizero:~/termtosvg$ make build
rm -rf dist && \
python3 setup.py sdist bdist_wheel
usage: setup.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]
   or: setup.py --help [cmd1 cmd2 ...]
   or: setup.py --help-commands
   or: setup.py cmd --help

error: invalid command 'bdist_wheel'
make: *** [Makefile:31: build] Error 1

Ok, fair enough, I’ll install wheel thanks to pip.

pip3 install wheel
Collecting wheel
  Using cached https://files.pythonhosted.org/packages/00/83/b4a77d044e78ad1a45610eb88f745be2fd2c6d658f9798a15e384b7d57c9/wheel-0.33.6-py2.py3-none-any.whl
Installing collected packages: wheel
  The script wheel is installed in '/home/poddingue/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed wheel-0.33.6

And then, as gently suggested by pip3:

export PATH=/home/poddingue/.local/bin:$PATH

Ok, let’s try it again:

poddingue@orangepizero:~/termtosvg$ make build
rm -rf dist && \
python3 setup.py sdist bdist_wheel
running sdist
running egg_info
creating termtosvg.egg-info
writing termtosvg.egg-info/PKG-INFO
writing dependency_links to termtosvg.egg-info/dependency_links.txt
writing requirements to termtosvg.egg-info/requires.txt
writing top-level names to termtosvg.egg-info/top_level.txt
writing manifest file 'termtosvg.egg-info/SOURCES.txt'
reading manifest file 'termtosvg.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no previously-included files matching '__pycache__' found anywhere in distribution
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no files found matching 'man/*.man.?'
writing manifest file 'termtosvg.egg-info/SOURCES.txt'
running check
[...]
Creating tar archive
removing 'termtosvg-1.0.0' (and everything under it)
[...]
running build_scripts
[...]
installing to build/bdist.linux-armv7l/wheel
running install
running install_lib
[...]
Copying termtosvg.egg-info to build/bdist.linux-armv7l/wheel/termtosvg-1.0.0-py3.7.egg-info
[...]
adding 'termtosvg-1.0.0.dist-info/RECORD'
removing build/bdist.linux-armv7l/wheel

Happy? Not really…

poddingue@orangepizero:~/termtosvg$ pip3 install dist/termtosvg-1.0.0-py3-none-any.whl
Processing ./dist/termtosvg-1.0.0-py3-none-any.whl
Collecting pyte (from termtosvg==1.0.0)
  Downloading https://files.pythonhosted.org/packages/66/37/6fed89b484c8012a0343117f085c92df8447a18af4966d25599861cd5aa0/pyte-0.8.0.tar.gz (50kB)
    100% |████████████████████████████████| 51kB 768kB/s
Collecting lxml (from termtosvg==1.0.0)
  Downloading https://files.pythonhosted.org/packages/e4/19/8dfeef50623892577dc05245093e090bb2bab4c8aed5cad5b03208959563/lxml-4.4.2.tar.gz (2.6MB)
    100% |████████████████████████████████| 2.6MB 74kB/s
Collecting wcwidth (from termtosvg==1.0.0)
  Downloading https://files.pythonhosted.org/packages/58/b4/4850a0ccc6f567cc0ebe7060d20ffd4258b8210efadc259da62dc6ed9c65/wcwidth-0.1.8-py2.py3-none-any.whl
Building wheels for collected packages: pyte, lxml
  Running setup.py bdist_wheel for pyte ... done
  Stored in directory: /home/poddingue/.cache/pip/wheels/c0/dd/4a/d0ec26b9d07a3b48e25ba3456dc9bcab875686af6da9e23fcd
  Running setup.py bdist_wheel for lxml ... error
  Complete output from command /usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-install-4whala87/lxml/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" bdist_wheel -d /tmp/pip-wheel-w27wrikj --python-tag cp37:
  Building lxml version 4.4.2.
  Building without Cython.
  ERROR: b'/bin/sh: 1: xslt-config: not found\n'
  _ make sure the development packages of libxml2 and libxslt are installed _

  Using build configuration of libxslt
  running bdist_wheel
  running build
  running build_py
  [...]
  arm-linux-gnueabihf-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DCYTHON_CLINE_IN_TRACEBACK=0 -Isrc -Isrc/lxml/includes -I/usr/include/python3.7m -c src/lxml/etree.c -o build/temp.linux-armv7l-3.7/src/lxml/etree.o -w
  In file included from src/lxml/etree.c:692:
  src/lxml/includes/etree_defs.h:14:10: fatal error: libxml/xmlversion.h: No such file or directory
   #include "libxml/xmlversion.h"
            ^~~~~~~~~~~~~~~~~~~~~
  compilation terminated.
  Compile failed: command 'arm-linux-gnueabihf-gcc' failed with exit status 1
  creating tmp
  cc -I/usr/include/libxml2 -c /tmp/xmlXPathInitp1a2wq_x.c -o tmp/xmlXPathInitp1a2wq_x.o
  /tmp/xmlXPathInitp1a2wq_x.c:1:10: fatal error: libxml/xpath.h: No such file or directory
   #include "libxml/xpath.h"
            ^~~~~~~~~~~~~~~~
  compilation terminated.
  *********************************************************************************
  Could not find function xmlCheckVersion in library libxml2. Is libxml2 installed?
  *********************************************************************************
  error: command 'arm-linux-gnueabihf-gcc' failed with exit status 1
  [...]
Command "/usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-install-4whala87/lxml/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-record-5dypr5tk/install-record.txt --single-version-externally-managed --compile --user --prefix=" failed with error code 1 in /tmp/pip-install-4whala87/lxml/

Damn!

poddingue@orangepizero:~/termtosvg$ sudo apt install libxml2-dev libxslt-dev python3-dev
Reading package lists... Done
Building dependency tree
Reading state information... Done
Note, selecting 'libxslt1-dev' instead of 'libxslt-dev'
python3-dev is already the newest version (3.7.3-1).
The following additional packages will be installed:
  icu-devtools libicu-dev libxslt1.1
Suggested packages:
  icu-doc
The following NEW packages will be installed:
  icu-devtools libicu-dev libxml2-dev libxslt1-dev libxslt1.1
0 upgraded, 5 newly installed, 0 to remove and 0 not upgraded.
Need to get 10.5 MB of archives.
After this operation, 44.6 MB of additional disk space will be used.
Do you want to continue? [Y/n]
Get:1 http://httpredir.debian.org/debian buster/main armhf icu-devtools armhf 63.1-6 [168 kB]
Get:2 http://httpredir.debian.org/debian buster/main armhf libicu-dev armhf 63.1-6 [8,934 kB]
Get:3 http://httpredir.debian.org/debian buster/main armhf libxml2-dev armhf 2.9.4+dfsg1-7+b3 [718 kB]
Get:4 http://httpredir.debian.org/debian buster/main armhf libxslt1.1 armhf 1.1.32-2.2~deb10u1 [217 kB]
Get:5 http://httpredir.debian.org/debian buster/main armhf libxslt1-dev armhf 1.1.32-2.2~deb10u1 [507 kB]
Fetched 10.5 MB in 5s (2,318 kB/s)
Selecting previously unselected package icu-devtools.
(Reading database ... 66802 files and directories currently installed.)
Preparing to unpack .../icu-devtools_63.1-6_armhf.deb ...
Unpacking icu-devtools (63.1-6) ...
Selecting previously unselected package libicu-dev:armhf.
Preparing to unpack .../libicu-dev_63.1-6_armhf.deb ...
Unpacking libicu-dev:armhf (63.1-6) ...
Selecting previously unselected package libxml2-dev:armhf.
Preparing to unpack .../libxml2-dev_2.9.4+dfsg1-7+b3_armhf.deb ...
Unpacking libxml2-dev:armhf (2.9.4+dfsg1-7+b3) ...
Selecting previously unselected package libxslt1.1:armhf.
Preparing to unpack .../libxslt1.1_1.1.32-2.2~deb10u1_armhf.deb ...
Unpacking libxslt1.1:armhf (1.1.32-2.2~deb10u1) ...
Selecting previously unselected package libxslt1-dev:armhf.
Preparing to unpack .../libxslt1-dev_1.1.32-2.2~deb10u1_armhf.deb ...
Unpacking libxslt1-dev:armhf (1.1.32-2.2~deb10u1) ...
Setting up icu-devtools (63.1-6) ...
Setting up libxslt1.1:armhf (1.1.32-2.2~deb10u1) ...
Setting up libicu-dev:armhf (63.1-6) ...
Setting up libxml2-dev:armhf (2.9.4+dfsg1-7+b3) ...
Setting up libxslt1-dev:armhf (1.1.32-2.2~deb10u1) ...
Processing triggers for man-db (2.8.5-2) ...
Processing triggers for libc-bin (2.28-10) ...

All set? Not so sure…

Command "/usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-install-9lgddbtt/lxml/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-record-ghyj96g8/install-record.txt --single-version-externally-managed --compile --user --prefix=" failed with error code 1 in /tmp/pip-install-9lgddbtt/lxml/

The error may have happened because of low memory. Let’s this machine breathe a little better by giving it more swap:

sudo fallocate -l 1G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile
sudo nano /etc/fstab

Now, let’s add this line in /etc/fstab.

/swapfile swap swap defaults 0 0

Let’s try to install it once again. Go and have a coffee, or go for a walk… You could maybe even take time to marry and start a family before the build finishes. Anyway, after some time, you’ll have your beautiful termtosvg package ready to rock!

pip3 install dist/termtosvg-1.0.0-py3-none-any.whl
Processing ./dist/termtosvg-1.0.0-py3-none-any.whl
Collecting lxml (from termtosvg==1.0.0)
  Using cached https://files.pythonhosted.org/packages/e4/19/8dfeef50623892577dc05245093e090bb2bab4c8aed5cad5b03208959563/lxml
Requirement already satisfied: pyte in /home/poddingue/.local/lib/python3.7/site-packages (from termtosvg==1.0.0) (0.8.0)
Requirement already satisfied: wcwidth in /home/poddingue/.local/lib/python3.7/site-packages (from termtosvg==1.0.0) (0.1.8)
Building wheels for collected packages: lxml
  Running setup.py bdist_wheel for lxml ... -


done
  Stored in directory: /home/poddingue/.cache/pip/wheels/73/52/7c/5cd696851d3e5e31a05023dd402b04659a6ec695ecc566c9d3
Successfully built lxml
Installing collected packages: lxml, termtosvg
Successfully installed lxml-4.4.2 termtosvg-1.0.0

This time, it looks like our installation has been successful. Let’s try it for real now.

Testing

Just enter termtosvg, do your stuff, and then do exit. You will have a message that will tell you where your newly created file lies.

That’s cool, but can we do any better? The man page tells us we can record a terminal session with a specific screen geometry:

termtosvg -g 80x24 animation.svg
Recording started, enter "exit" command or Control-D to end
poddingue@orangepizero:~$ echo "Hello, World!"
Hello, World!
poddingue@orangepizero:~$ exit
Rendering ended, SVG animation is animation.svg

Pretty handy!

I am recording my casts with a 200x60 geometry, exporting them in the SVG format. The first step is to record them:

termtosvg record -g 200x60 whatever-name-I-choose.cast

I then ask termtosvg to render the cast as SVG, so that I can use them in websites that can handle them:

termtosvg render whatever-name-I-choose.cast whatever-name-I-choose.svg
Rendering started
Rendering ended, SVG animation is whatever-name-I-choose.svg

Want more? You’re GIF hungry? Ok I resign myself to give you my process, which is not pretty, and requires an X86 machine. As we have seen before, we can record a terminal session in asciicast v2 format. The cast file we have produced during the previous step to generate SVG, can then be used by asciinema (using phantomjs in the background) to produce a GIF file.

docker pull asciinema/asciicast2gif
alias asciicast2gif='docker run --rm -v $PWD:/data asciinema/asciicast2gif'
asciicast2gif -s 2 -t solarized-dark whatever-name-I-choose.cast demo.gif

And here is the final result:

And in GIF for the CMS that can’t display properly SVG.

Sphinx: Installing sphinxbase on the OrangePi 4B

Bruno Verachten — Wed, 22 Apr 2026 07:44:21 +0000

Originally published on June 5, 2020 at https://bruno.verachten.fr/2020/06/05/installing-sphinxbase-on-the-orangepi-4b/

This post will be part of a series regarding generating subtitles for videos with the OrangePi 4B running Armbian. We’re not yet sure of what we’ll use to generate those subtitles, so for the time being, we’ll use Sphinx, and in the following months, we’ll try to setup something that will take advantage of the NPU.

Today, we’ll install SphinxBase.

git clone https://github.com/cmusphinx/sphinxbase.git
cd sphinxbase/
./autogen.sh

Unfortunately we get:

_Error_: You must have `libtool' installed.
Get ftp://ftp.gnu.org/pub/gnu/libtool/libtool-2.2.6b.tar.gz
(or a newer version if it is available)

Ok, my bad:

sudo apt install libtool

Let’s give it another try:

./autogen.sh
[...]
configure: error: in `/home/poddingue/sphinxbase':
configure: error:
  Could not link test program to Python. Maybe the main Python library has been
  installed in some non-standard library path. If so, pass it to configure,
  via the LDFLAGS environment variable.
  Example: ./configure LDFLAGS="-L/usr/non-standard-path/python/lib"
  ============================================================================
   ERROR!
   You probably have to install the development version of the Python package
   for your distribution.  The exact name of this package varies among them.
  ============================================================================

See `config.log' for more details

I see. I may have forgotten to install Python-dev…

sudo apt install python3-dev
[...]
./autogen.sh
[...]
configure: error: in `/home/poddingue/sphinxbase':
configure: error:
  Could not link test program to Python. Maybe the main Python library has been
  installed in some non-standard library path. If so, pass it to configure,
  via the LDFLAGS environment variable.
  Example: ./configure LDFLAGS="-L/usr/non-standard-path/python/lib"
  ============================================================================
   ERROR!
   You probably have to install the development version of the Python package
   for your distribution.  The exact name of this package varies among them.
  ============================================================================

How come? Are you still working with Python 2.x? Naaaah… Ok, you never know, let’s try it this way:

sudo apt install python-dev

Yes, that was it. Now, let’s tackle this error:

checking python extra linking flags... -Xlinker -export-dynamic -Wl,-O1 -Wl,-Bsymbolic-functions
checking consistency of all components of python development environment... yes
checking for swig... no
checking for swig2.0... no
configure: error: swig not found

This should be solved by

sudo apt install swig
./autogen.sh
[...]
config.status: executing libtool commands
Now type `make' to compile the package.

Bingo! Time for a good old

make -j6
make check
[...]
Testsuite summary for sphinxbase 5prealpha
============================================================================
== TOTAL: 10
== PASS:  10
== SKIP:  0
== XFAIL: 0
== FAIL:  0
== XPASS: 0
== ERROR: 0
============================================================================

All went fine. Let’s install it then:

 sudo -E make install

Done. Next step, install Pocket Sphinx.

Installing TensorFlow on the Orange Pi 4B

Bruno Verachten — Wed, 22 Apr 2026 07:44:19 +0000

Originally published on June 19, 2020 at https://bruno.verachten.fr/2020/06/19/installing-tensorflow-on-the-orangepi-4b/

This quick howto is heavily based on Orange Pi’s documentation. To this day, Orange Pi recommended versions of TensorFlow and Python are quite old:

TensorFlow：1.14.0 Python environment：python2.7

Installation of Python environment and dependencies

sudo apt-get install python-pip python-dev libatlas-base-dev
[sudo] password for poddingue:
Reading package lists... Done
Building dependency tree
Reading state information... Done
python-dev is already the newest version (2.7.16-1).
The following additional packages will be installed:
  libatlas3-base python-pip-whl
Suggested packages:
  libatlas-doc liblapack-doc
Recommended packages:
  python-all-dev python-setuptools python-wheel
The following NEW packages will be installed:
  libatlas-base-dev libatlas3-base python-pip python-pip-whl
Need to get 7,168 kB of archives.
After this operation, 40.1 MB of additional disk space will be used.
Do you want to continue? [Y/n]
Get:1 http://debian.proxad.net/debian buster/main arm64 libatlas3-base arm64 3.10.3-8 [2,409 kB]
Get:2 http://debian.proxad.net/debian buster/main arm64 libatlas-base-dev arm64 3.10.3-8 [2,954 kB]
Get:3 http://debian.proxad.net/debian buster/main arm64 python-pip-whl all 18.1-5 [1,591 kB]
Get:4 http://debian.proxad.net/debian buster/main arm64 python-pip all 18.1-5 [215 kB]
Fetched 7,168 kB in 1s (8,178 kB/s)
Selecting previously unselected package libatlas3-base:arm64.
(Reading database ... 112671 files and directories currently installed.)
Preparing to unpack .../libatlas3-base_3.10.3-8_arm64.deb ...
Unpacking libatlas3-base:arm64 (3.10.3-8) ...
Selecting previously unselected package libatlas-base-dev:arm64.
Preparing to unpack .../libatlas-base-dev_3.10.3-8_arm64.deb ...
Unpacking libatlas-base-dev:arm64 (3.10.3-8) ...
Selecting previously unselected package python-pip-whl.
Preparing to unpack .../python-pip-whl_18.1-5_all.deb ...
Unpacking python-pip-whl (18.1-5) ...
Selecting previously unselected package python-pip.
Preparing to unpack .../python-pip_18.1-5_all.deb ...
Unpacking python-pip (18.1-5) ...
Setting up libatlas3-base:arm64 (3.10.3-8) ...
update-alternatives: using /usr/lib/aarch64-linux-gnu/atlas/libblas.so.3 to provide /usr/lib/aarch64-linux-gnu/libblas.so.3 (libblas.so.3-aarch64-linux-gnu) in auto mode
update-alternatives: using /usr/lib/aarch64-linux-gnu/atlas/liblapack.so.3 to provide /usr/lib/aarch64-linux-gnu/liblapack.so.3 (liblapack.so.3-aarch64-linux-gnu) in auto mode
Setting up libatlas-base-dev:arm64 (3.10.3-8) ...
update-alternatives: using /usr/lib/aarch64-linux-gnu/atlas/libblas.so to provide /usr/lib/aarch64-linux-gnu/libblas.so (libblas.so-aarch64-linux-gnu) in auto mode
update-alternatives: using /usr/lib/aarch64-linux-gnu/atlas/liblapack.so to provide /usr/lib/aarch64-linux-gnu/liblapack.so (liblapack.so-aarch64-linux-gnu) in auto mode
Setting up python-pip-whl (18.1-5) ...
Setting up python-pip (18.1-5) ...
Processing triggers for man-db (2.8.5-2) ...
Processing triggers for libc-bin (2.28-10) ...

Install TensorFlow

There are several ways to install TensorFlow, but here we will install if from a downloaded binary. Download the PIP installation package for TensorFlow from the following page: https://github.com/lhelontra/tensorflow-on-arm/releases/tag/v1.14.0 .

This should give something like:

curl "https://github.com/lhelontra/tensorflow-on-arm/releases/download/v1.14.0/tensorflow-1.14.0-cp27-none-linux_aarch64.whl" --output tensorflow-1.14.0-cp27-none-linux_aarch64.whl

Now, let’s use PIP to install TensorFlow.

pip install tensorflow-1.14.0-cp27-none-linux_aarch64.whl
Processing ./tensorflow-1.14.0-cp27-none-linux_aarch64.whl
Collecting keras-preprocessing>=1.0.5 (from tensorflow==1.14.0)
  Downloading https://files.pythonhosted.org/packages/79/4c/7c3275a01e12ef9368a892926ab932b33bb13d55794881e3573482b378a7/Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42kB)
    100% |████████████████████████████████| 51kB 2.5MB/s
Collecting enum34>=1.1.6 (from tensorflow==1.14.0)
  Downloading https://files.pythonhosted.org/packages/6f/2c/a9386903ece2ea85e9807e0e062174dc26fdce8b05f216d00491be29fad5/enum34-1.1.10-py2-none-any.whl
Collecting wheel (from tensorflow==1.14.0)
  Downloading https://files.pythonhosted.org/packages/8c/23/848298cccf8e40f5bbb59009b32848a4c38f4e7f3364297ab3c3e2e2cd14/wheel-0.34.2-py2.py3-none-any.whl
Collecting wrapt>=1.11.1 (from tensorflow==1.14.0)
  Downloading https://files.pythonhosted.org/packages/82/f7/e43cefbe88c5fd371f4cf0cf5eb3feccd07515af9fd6cf7dbf1d1793a797/wrapt-1.12.1.tar.gz
    Complete output from command python setup.py egg_info:
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
    ImportError: No module named setuptools
----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-install-Dd0045/wrapt/

Of course…

sudo apt-get install -y python-setuptools
[...]
  error: invalid command 'bdist_wheel'

Damn!

pip install wheel

And

export PATH=$PATH:/home/poddingue/.local/bin

in `~/.bashrc`, because of

pip install wheel
Collecting wheel
  Using cached https://files.pythonhosted.org/packages/8c/23/848298cccf8e40f5bbb59009b32848a4c38f4e7f3364297ab3c3e2e2cd14/wheel-0.34.2-py2.py3-none-any.whl
Installing collected packages: wheel
  The script wheel is installed in '/home/poddingue/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed wheel-0.34.2

Let’s issue once more `

` bash
pip install tensorflow-1.14.0-cp27-none-linux_aarch64.whl

error: libhdf5.so: cannot open shared object file: No such file or directory
`

Bad luck.

bash Command "/usr/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-install-irkqLB/h5py/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-record-N9oIr_/install-record.txt --single-version-externally-managed --compile --user --prefix=" failed with error code 1 in /tmp/pip-install-irkqLB/h5py/

Something must be missing…

Not better… I think the machine is trying to tell me something… A missing library perhaps?

bash sudo apt install python-h5py pip install tensorflow-1.14.0-cp27-none-linux_aarch64.whl [...] Successfully installed astor-0.8.1 backports.weakref-1.0.post1 funcsigs-1.0.2 gast-0.3.3 google-pasta-0.2.0 keras-applications-1.0.8 mock-3.0.5 tensorflow-1.14.0 tensorflow-estimator-1.14.0 termcolor-1.1.0 rm tensorflow-1.14.0-cp27-none-linux_aarch64.whl

It worked!

Now, let’s edit a test.py file:

` python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')

sess = tf.Session()

sess = tf.compat.v1.Session()
print(sess.run(hello))
`

bash python test.py Hello, TensorFlow!

We all have to start somewhere, don’t we?

TL;DR

bash sudo apt-get install -y python-setuptools python-h5py python-pip python-dev libatlas-base-dev curl -L "https://github.com/lhelontra/tensorflow-on-arm/releases/download/v1.14.0/tensorflow-1.14.0-cp27-none-linux_aarch64.whl" --output tensorflow-1.14.0-cp27-none-linux_aarch64.whll pip install wheel pip install tensorflow-1.14.0-cp27-none-linux_aarch64.whl cat >> ~/.bashrc export PATH=$PATH:/home/poddingue/.local/bin CTRL+D source ~/.bashrc

Installing Caffe on the Orange Pi 4B

Bruno Verachten — Wed, 22 Apr 2026 07:44:17 +0000

Originally published on June 22, 2020 at https://bruno.verachten.fr/2020/06/22/installing-caffee-on-the-orangepi-4b/

Install dependencies

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev
libopencv-dev libhdf5-serial-dev protobuf-compiler cmake
sudo apt-get install --no-install-recommends libboost-all-dev
sudo apt-get install libopenblas-dev liblapack-dev libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

Download Caffe and modify configuration parameters

git clone https://github.com/BVLC/caffe
cd caffe
cp Makefile.config.example Makefile.config

Modify the parameters of Makefile.config Change #CPU_ONLY := 1 into CPU_ONLY := 1 Change ＃OPENCV_VERSION := 3 into OPENCV_VERSION := 3

Install Caffe

mkdir build
cd build
cmake ..
  Protobuf compiler version doesn't match library version 3.6.1

Okay… Let’s try a sudo apt install protobuf-compiler.

protoc --version
libprotoc 3.6.1
cmake ..
  Could NOT find HDF5 (missing: HDF5_LIBRARIES HDF5_INCLUDE_DIRS

My bad, let’s install it.

sudo apt install libhdf5-dev
cmake ..
  Could not find a package configuration file provided by "OpenCV" with any

Let’s install openCV, then…

sudo apt install libopencv-dev
cmake ..
[...]
-- Configuring done
-- Generating done
-- Build files have been written to: /home/poddingue/caffe/build

Next step, let’s build it for real. By the way, kiddoes, don’t try this -j6 at home unless you have a good heatsink… Or the machine will stop/hang up/reboot without prior alert.

make all -j6
make pycaffe -j6
make test -j6
make runtest -j6

Run a simple example

Run the caffe example — mnist instance: Mnist is a handwritten digital library. Originally used for handwritten numeral recognition on checks, and it is now the DL’s starting exercise library. The special model for mnist identification is Lenet, which is the earliest CNN model. The training samples of mnist data are 60,000 units, and the test samples are 10,000 units. Each sample is a black and white picture of size 28*28, and the handwritten number is 0-9, so it is divided into 10 categories.

Download the mnist data first

sh data/mnist/get_mnist.sh
cd data/mnist

There are four files in the directory after running the previous command:

ll
total 53676
-rwxr-xr-x 1 poddingue poddingue      408 Jun 22 16:55 get_mnist.sh
-rw-r--r-- 1 poddingue poddingue  7840016 Jul 21  2000 t10k-images-idx3-ubyte # Training set samples
-rw-r--r-- 1 poddingue poddingue    10008 Jul 21  2000 t10k-labels-idx1-ubyte # Corresponding annotation of training set
-rw-r--r-- 1 poddingue poddingue 47040016 Jul 21  2000 train-images-idx3-ubyte # Testing set sample
-rw-r--r-- 1 poddingue poddingue    60008 Jul 21  2000 train-labels-idx1-ubyte # Corresponding annotation of testing set

Converting the data

These data cannot be used directly in Caffe, and need to be converted into LMDB data:

cd ../..
sh examples/mnist/create_mnist.sh
Creating lmdb...
I0622 17:37:06.109530 12691 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_train_lmdb
I0622 17:37:06.114603 12691 convert_mnist_data.cpp:88] A total of 60000 items.
I0622 17:37:06.114666 12691 convert_mnist_data.cpp:89] Rows: 28 Cols: 28
I0622 17:37:11.407480 12691 convert_mnist_data.cpp:108] Processed 60000 files.
I0622 17:37:12.192065 12698 db_lmdb.cpp:35] Opened lmdb examples/mnist/mnist_test_lmdb
I0622 17:37:12.194784 12698 convert_mnist_data.cpp:88] A total of 10000 items.
I0622 17:37:12.198009 12698 convert_mnist_data.cpp:89] Rows: 28 Cols: 28
I0622 17:37:12.566424 12698 convert_mnist_data.cpp:108] Processed 10000 files.
Done.
ll examples/mnist/
total 136
-rw-r--r-- 1 poddingue poddingue  4520 Jun 22 16:55 convert_mnist_data.cpp
-rwxr-xr-x 1 poddingue poddingue   634 Jun 22 16:55 create_mnist.sh
-rw-r--r-- 1 poddingue poddingue   777 Jun 22 16:55 lenet_adadelta_solver.prototxt
-rw-r--r-- 1 poddingue poddingue   778 Jun 22 16:55 lenet_auto_solver.prototxt
-rw-r--r-- 1 poddingue poddingue  6003 Jun 22 16:55 lenet_consolidated_solver.prototxt
-rw-r--r-- 1 poddingue poddingue   871 Jun 22 16:55 lenet_multistep_solver.prototxt
-rw-r--r-- 1 poddingue poddingue  1738 Jun 22 16:55 lenet.prototxt
-rw-r--r-- 1 poddingue poddingue   886 Jun 22 16:55 lenet_solver_adam.prototxt
-rw-r--r-- 1 poddingue poddingue   790 Jun 22 16:55 lenet_solver.prototxt
-rw-r--r-- 1 poddingue poddingue   830 Jun 22 16:55 lenet_solver_rmsprop.prototxt
-rw-r--r-- 1 poddingue poddingue  2282 Jun 22 16:55 lenet_train_test.prototxt
-rw-r--r-- 1 poddingue poddingue  4814 Jun 22 16:55 mnist_autoencoder.prototxt
-rw-r--r-- 1 poddingue poddingue   451 Jun 22 16:55 mnist_autoencoder_solver_adadelta.prototxt
-rw-r--r-- 1 poddingue poddingue   423 Jun 22 16:55 mnist_autoencoder_solver_adagrad.prototxt
-rw-r--r-- 1 poddingue poddingue   466 Jun 22 16:55 mnist_autoencoder_solver_nesterov.prototxt
-rw-r--r-- 1 poddingue poddingue   433 Jun 22 16:55 mnist_autoencoder_solver.prototxt
drwxr--r-- 2 poddingue poddingue  4096 Jun 22 17:37 mnist_test_lmdb
drwxr--r-- 2 poddingue poddingue  4096 Jun 22 17:37 mnist_train_lmdb
-rw-r--r-- 1 poddingue poddingue 11948 Jun 22 16:55 readme.md
-rwxr-xr-x 1 poddingue poddingue   106 Jun 22 16:55 train_lenet_adam.sh
-rwxr-xr-x 1 poddingue poddingue   118 Jun 22 16:55 train_lenet_consolidated.sh
-rwxr-xr-x 1 poddingue poddingue  4517 Jun 22 16:55 train_lenet_docker.sh
-rwxr-xr-x 1 poddingue poddingue   115 Jun 22 16:55 train_lenet_rmsprop.sh
-rwxr-xr-x 1 poddingue poddingue   101 Jun 22 16:55 train_lenet.sh
-rwxr-xr-x 1 poddingue poddingue   120 Jun 22 16:55 train_mnist_autoencoder_adadelta.sh
-rwxr-xr-x 1 poddingue poddingue   119 Jun 22 16:55 train_mnist_autoencoder_adagrad.sh
-rwxr-xr-x 1 poddingue poddingue   120 Jun 22 16:55 train_mnist_autoencoder_nesterov.sh
-rwxr-xr-x 1 poddingue poddingue   117 Jun 22 16:55 train_mnist_autoencoder.sh

The examples/mnist/create_mnist.sh has generated two folders in the examples/mnist/ directory, namely mnist_train_lmdb and mnist_test_lmdb after the LMDB data conversion succeeded. The data.mdb and lock.mdb stored in the folder are the running data we need.

LevelDB Data

If you want to run leveldb data, run the program under examples/siamese/ folder.

 ./examples/siamese/create_mnist_siamese.sh
Creating leveldb...
Done.
ll examples/siamese/
total 204
-rw-r--r-- 1 poddingue poddingue   4368 Jun 22 16:55 convert_mnist_siamese_data.cpp
-rwxr-xr-x 1 poddingue poddingue    617 Jun 22 16:55 create_mnist_siamese.sh
-rw-r--r-- 1 poddingue poddingue 158921 Jun 22 16:55 mnist_siamese.ipynb
-rw-r--r-- 1 poddingue poddingue   1488 Jun 22 16:55 mnist_siamese.prototxt
-rw-r--r-- 1 poddingue poddingue    810 Jun 22 16:55 mnist_siamese_solver.prototxt
drwxr-xr-x 2 poddingue poddingue   4096 Jun 22 17:39 mnist_siamese_test_leveldb
drwxr-xr-x 2 poddingue poddingue   4096 Jun 22 17:39 mnist_siamese_train_leveldb
-rw-r--r-- 1 poddingue poddingue   4907 Jun 22 16:55 mnist_siamese_train_test.prototxt
-rw-r--r-- 1 poddingue poddingue   5949 Jun 22 16:55 readme.md
-rwxr-xr-x 1 poddingue poddingue    125 Jun 22 16:55 train_mnist_siamese.sh

Next modify the configuration file lenet_solver.prototxt

vi ./examples/mnist/lenet_solver.prototxt

As needed, set the maximum number of iterations at 100 and the last line, solver_mode, to CPU:

== The train/test net protocol buffer definition
net: "examples/mnist/lenet_train_test.prototxt"
== test_iter specifies how many forward passes the test should carry out.
== In the case of MNIST, we have test batch size 100 and 100 test iterations,
== covering the full 10,000 testing images.
test_iter: 100
== Carry out testing every 500 training iterations.
test_interval: 500
== The base learning rate, momentum and the weight decay of the network.
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
== The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
== Display every 100 iterations
display: 100
== The maximum number of iterations
max_iter: 10000
== snapshot intermediate results
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"
== solver mode: CPU or GPU
solver_mode: CPU

Finally, run this example:

root@OrangePi:~/caffe# time sh examples/mnist/train_lenet.sh I0110 06:28:06.117972 25078 caffe.cpp:197] Use CPU. I0110 06:28:06.118988 25078 solver.cpp:45] Initializing solver from parameters: test_iter: 100 test_interval: 500 base_lr: 0.01 display: 100 XUNLONG Software Co,. Ltd All rights reserved 36 www.orangepi.com max_iter: 10000 lr_policy: "inv" gamma: 0.0001 power: 0.75 momentum: 0.9 weight_decay: 0.0005 snapshot: 5000 snapshot_prefix: "examples/mnist/lenet" solver_mode: CPU net: "examples/mnist/lenet_train_test.prototxt"

I0110 07:25:37.016746 25078 sgd_solver.cpp:284] Snapshotting solver state to binary proto file
examples/mnist/lenet_iter_10000.solverstate
I0110 07:25:37.146054 25078 solver.cpp:327] Iteration 10000, loss = 0.00200602
I0110 07:25:37.146195 25078 solver.cpp:347] Iteration 10000, Testing net (#0)
I0110 07:25:54.299634 25080 data_layer.cpp:73] Restarting data prefetching from start.
I0110 07:25:55.010598 25078 solver.cpp:414] Test net output #0: accuracy = 0.9913
I0110 07:25:55.010757 25078 solver.cpp:414] Test net output #1: loss = 0.0273203 (* 1 =
0.0273203 loss)
I0110 07:25:55.010777 25078 solver.cpp:332] Optimization Done.
I0110 07:25:55.010793 25078 caffe.cpp:250] Optimization Done.
real 57m48.984s
user 58m6.479s
sys 0m1.977s
⑦Operation conclusion
According to the above results, the running time of the CPU is about 58 minutes, with
an accuracy of about 99%.

🍊 ARM your continuous integration system with fruits! 🍌

Bruno Verachten — Wed, 22 Apr 2026 07:44:14 +0000

Originally published on January 11, 2021 at https://bruno.verachten.fr/2021/01/11/arm-your-ci-with-fruits/

The goal of this howto is to show you how to add your own ARM Gitlab runner to your Gitlab instance Runners herd.

Whaaaaaaaaaat?

Ok, let me introduce quickly the concepts.

Continuous integration allows the developers on a project to check that each change they made did not introduce any regression. Each commit will be followed by a build of their application, and by a run of their test suite. If every test runs still the same way after the commit, then all is fine. Gitlab is a web-based Git repository manager (with tons of features). You can use it directly online, install your own community-edition copy, or pay for top level support.

Today, we’ll be only interested in their continuous integration feature.

Starting Continuous Integration (i.e. CI) is really straightforward with Gitlab. Just create a .gitlab-ci.yml file in your repository hosted in Gitlab, populate it with the commands needed to build your application, and you’re good to go! 🎉 But wait… Where does that build and test execute? That’s where the runners enter. Runners are machines linked to Gitlab that will execute the builds. These can be real machines, virtual machines or containers. By default, you have access to shared runners on gitlab.com , or even on your company Gitlab instance.

These runners are tagged (🏷️) so that you can also tag your builds accordingly. If you need a runner that will build a Docker image, it could be tagged docker for example, and so would be your build step in your .gitlab.yml\file. Your builds can now easily be customized without ruining your own machine configuration… and even use Docker.

Why would you add a 🏃?

If you are using the main Gitlab shared instance, chances are that you will have to be patient to get the results of your build. The Shared Runner concept is really cool, as it allows to share the load, but … you’re not alone waiting in that queue… And the same could be true for your company’s Gitlab instance, as it will soon become popular. So you could add your own machine to the Gitlab Runners herd, just for your project. Pretty neat if you’re tired to wait for other people builds to finish. The problem is a new machine is not cheap. That’s way cool to have your own Gitlab Runner running in your office, but have a quick look at your pockets contents. Do you have enough cash to buy a new machine? Maybe not… But wait… In the introduction, I was talking about ARM Runners. Why would you need to add an ARM processor machine as a Gitlab Runner?

The reason why I chose to install an ARM Gitlab Runner

Well, if your code does not depend on a specific family of processors, there is no other cheaper alternative to build a new Gitlab Runner.

\$15 for an OrangePi Zero machine able to run Docker and Gitlab Runner can’t be beaten. And… if you want to develop for that kind of architecture (ARMV7, aarch64…), you will need that kind of runner. I am trying to port software to the ARM processor, and these ARM Gitlab Runners have proven to be really useful. I can develop on my favorite IDE (on a X86 machine for example), commit, and automagically, my modified code will be compiled and tested on an ARM machine. I have to admit all of this could also be done with X86 Runners, as QEMU is able to emulate ARM processors. And even the build of ARM Docker image can be done on X86 Gitlab Runners, thanks to the amazing work of guys at Balena.

But where’s the fun? And furthermore… We all have a Raspberry Pi taking layer after layer of dust. Who has not been bored after having his first led blink? Why not put it back to work?

Let’s go then for a cheap and fun solution.

Choose your preferred fruit

In this article, we’re not going to install Gitlab Runner on a 🍊 Pi, but on a Raspberry Pi (because we had one laying around), but this should work on any fruit (aka ARM Docker capable device), as 🍌 or 🍊.

image::http://www.banana-pi.org/\[\[🍌\] Pi M1, courtesy of Armbian](../assets/images/2021/02/04/bananapi.png)

This version of Gitlab Runner we’ll use needs Docker, that’s why we need a Linux distribution which supports Docker. We’re using an Hypriot distro because of the Raspberry Pi. We could have used Raspbian, but HypriotOS is the best Docker-oriented distribution for the Raspberry Pi. From downloading the image to the first docker \command will take only five minutes.

There is also a 64bits fork (as lots of Raspberry Pi distributions are not available for 64bits, that’s a nice plus). To me, that’s the first choice if you want to use Docker on a Raspberry Pi out of the box.

For all other devices, prefer Armbian if supported (like the Orange Pi Zero that can run as a Gitlab Runner too). It’s really easy to install Docker on this distribution.

First of all, let’s download the latest Hypriot distro.

Downloading and installing the Hypriot Distro

Go to the Hypriot download page and choose the latest distro.

Current image available on 02/04/2021 is Version 1.12.3 : https://github.com/hypriot/image-builder-rpi/releases/download/v1.12.3/hypriotos-rpi-v1.12.3.img.zip

On Windows

Next go to your Download folder and extract the downloaded zip file by right-clicking on the file and then clicking on 7-zip → extract here…. if you use 7-zip. Whatever your unzipping tool may be, right-click and choose the right option to uncompress the file. After 7-zip is finished with extracting you will have a file with a .img extension.

On OsX

It’s almost the same as for Windows, except that the tool could be Keka.

On Linux

Just use 7z.

Writing the image

In the next step we will flash this file onto your SD card. We have to use the finest piece of sofware able to write CORRECTLY and CHECK the written image (i.e. Etcher) on any platform. Current version is 1.4.4.

Too bad it does not exist for armv7. I do insist on the use of Etcher, because lots of supposed Linux bugs are because of a badly written image, or to a faulty SDCard. Etcher will take care of these issues. If possible, do not use any USB Dongle or gizmo to host the SDCard, prefer your laptop own SDCard reader. And for the SDCard, prefer a REAL and GENUINE SDCard.

How can you differentiate a real one from a bad copy? Well, you can’t, or not easily… Anyway, unzip the image, and write it with Etcher to your SDCard. In the latests versions of Etcher, you don’t even need to unzip the image file beforehand.

Upon completion, close Etcher and eject the SD card before removing it from its slot. In order to eject the SD card, use the secure eject function in the menu next to your system clock. This is usually in the very bottom right corner of your screen.

Now it’s time to start your Raspberry Pi with the SD card

Boot your Raspberry Pi

Connect the Raspberry Pi to your local network via an Ethernet cable
put the SD card into the designated slot
plug in the power adapter

After finishing the last step your Raspberry Pi will boot and the LEDs should start blinking. The very first boot will take one to three minutes as the file system will be resized.

Find its IP on the network

After booting, you can find the Raspberry Pi at your network with a simple ping black-pearl.local \— no more searching for IP addresses required thanks to the integrated Avahi service discovery.

https://gist.githubusercontent.com/gounthar/754f2988dc9ee643ea1d8be9a9c42223/raw/5437d39cdb07ab6bfb536ae468b483d8c197a29a/ssh%2520pirate.txt

As you can see in the following ifconfig command, the MAC Address of the Raspberry always starts with B8. That could prove useful if ever this first method does not work.

https://gist.githubusercontent.com/gounthar/ceb2680f38e613289f4e55b9e8c4f819/raw/0e7888a483783a92eba88f5d1f9996a47b54cea3/ifconfig.txt

So, what shall we do if the very first ping/ssh `does not work with `black-pearl? Well, as the Raspberry Pis MAC Address always start with B8 we can use nmap and keep only the IPs whose attached MAC Address sport a B8(yes, it’s gross). If your network starts at 10.XX.30.XX `and ends at `10.XX.50.XX, ip_start would be 30 and ip_end would be 50.

https://gist.githubusercontent.com/gounthar/2b4372abd7a623f7cf670575a76026d1/raw/e27c56e792fdf73652b4b53d07fa6bb91df1b7c7/nmap.txt

This will give you a list of Raspberry IPs on which you will then be able to try to connect with

Machine name and user password

The default user “pirate” (password “hypriot”) can be changed or removed before the first boot. Everything happens in /boot/user-data. You can add your public SSH key, disable password logins and specify a different user account before booting your Raspberry Pi. WiFi can be customized and enabled to have Docker up and running through the air without attaching a keyboard and monitor. How cool is that? You can also change the name of the machine later on with sudo raspi-config. The changes will have to be made in Network Options/hostname.

Once it’s done, reboot and enjoy your new machine name:

https://gist.githubusercontent.com/gounthar/596bc88f23d98de76bb70b11eb66e47b/raw/13f2210c233b6e1dc4d8da31f72e9063cfd18271/ssh%2520pirate%2520arm%2520runner.txt

Proxy settings

If you’re like me, stuck behind a corporate proxy, you will have to set a few things before being able to update your machine and run Docker.

Package manager

Edit /etc/apt/apt.conf.d/10proxy and add your proxy (create it if it doesn’t exist yet):

https://gist.githubusercontent.com/gounthar/e3288a613845b18aee3fe1098422d59f/raw/2eb3c68c8f2b3473902763e2f2b22be79918eea6/acquire.txt

A sudo apt-get update should prove that the configuration is working

https://gist.githubusercontent.com/gounthar/dc7829b564944c460e16a68870c13409/raw/afb35d6fe8731476befdf5f62f5513d4372f3f6a/apt%2520update.txt

While you’re there, update the machine with sudo apt-get upgrade to have the freshest Docker environment available.

Docker

If you try to download a Docker image with docker pull … it will fail because Docker doesn’t use the same proxy settings as apt.

https://gist.githubusercontent.com/gounthar/8384bb56d7ef8a9380c1f0d43013162e/raw/4f1f2aa8837966fe040e88c9e5c1aefc48d9f17a/docker%2520pull.txt

So you will have to create a few directories and files to let Docker know it should use your proxy:

https://gist.githubusercontent.com/gounthar/67dfa1f13e3d3064a02d17cb3ed9a51d/raw/3bb338cd496e0032f2472178f23fe96416eee0a2/docker%2520http%2520proxy.txt

/etc/systemd/system/docker.service.d/https-proxy.conf will contain:

https://gist.githubusercontent.com/gounthar/d04bb35e54b87f1a97e7ef6493f746a3/raw/2af846f014e46c76df96f22f0cbdcbe70fd35926/proxy%2520conf.txt

The http `version of this file will have the same proxy settings (just remove the `S of HTTPS). Once you’re done with the modifications, you will have to tell Docker to restart to take this into account:

https://gist.githubusercontent.com/gounthar/107de82adde721e3f7803778b3e08ffb/raw/c0d8670a60efe2585880aaf07a11e398ee17d230/restart%2520docker

Then you’ll be able to check that your modifications are correct:

https://gist.githubusercontent.com/gounthar/2b1df86210ee515b1c074e7140d29b35/raw/bad834d6d2c23387ed8c5097fec0a52d29805056/properties.txt

Date

Check that the date on your machine is correct by issuing date in the terminal. If it’s not correct, you will have some troubles with getting a Docker image. So, if the date/time are not right, you can set them up with:

https://gist.githubusercontent.com/gounthar/ecc647cb01fe16bcd2f439d492b1e663/raw/15c97e75b7f8164a92f19dfb834fc9a6381fea9c/date.txt

Gitlab Runner

You already know Gitlab Runner, I’m sure you are already using some shared runners with your CI on your company’s gitlab. Most of the time, you don’t need to setup your own, but as there aren’t that many ARM gitlab runners for the time being… Gitlab Runner has been ported to ARM in 2017 by Mauricio Ugaz as it wasn’t available, and everyone needs gitlab-runner on Docker running on ARM, am I right?

Note: there are two versions available. Alpine and Ubuntu. As Alpine is the default image, when you run docker pull klud/gitlab-runner Alpine version will be downloaded.

About the image

This image is built for ARM devices, based on the official repo of the GitLab Runner. That’s a fantastic work that made my daily work really more enjoyable.

Overview

Runner container setup

You need to mount a volume into the gitlab-runner container in order to share the configuration. This could be done this way:

https://gist.githubusercontent.com/gounthar/925bd7f20136b68b7a1bf337b31ff9ac/raw/4a48f4353261d247e0696ef581cd829bc44242ad/docker%2520run%2520txt

As we want to use the Docker executor, it is necessary to mount the Docker socket this way. Don’t forget to create /etc/gitlab-runner \directory ahead of time:

https://gist.githubusercontent.com/gounthar/9c76e1523d04a29d490703fe68c32793/raw/79db3c2dd7f67d1083c77a2ac1109fc6d9b53e50/docker%2520run%2520II.txt

Register runner

Once the container is up and running, you can then register the runner on your GitLab server. Go to your project on your Gitlab instance, click on Settings, then on CI/CD, and click on Expand next to Runner settings. Scroll down to Setup a specific Runner manually, and note the information regarding the URL and the token.

Specify the following URL during the Runner setup: https://gitlab.com/.

Use the following registration token during setup: the_token_that_is_yours_and_yours_alone

So once you have the information you need (URL and token), you can then do this:

https://gist.githubusercontent.com/gounthar/968c840f9a5ef8ee98edfbd6d6154fff/raw/3ea52f940b92282d7fa97ebc5e8ab0ff3fb6219e/register.txt

Unfortunately, with the latest versions of Docker from 19.03, you may get this message.

With Docker 19.03, TLS is enabled by default, to use TLS you need to update the GitLab Runner configuration so that the certificates are shared between the service and build container. To do this, update your config.toml so that it looks like that.

Let’s check if this new runner can now be seen. Refresh your CI/CI settings, expand Runner settings, and go down. You should now be able to see your brand new Gitlab-ci runner proudly sporting its ARM tag! 🤩

<!--Image for post-→

Congrats!

You could also do it this way:

https://gist.githubusercontent.com/gounthar/d6ce6108e3f25a11a8c70bc74ab65e97/raw/aaa4af34343124e9d3dbed731cffd1dde6c33243/register%2520II.txt

So now you have your own ARM runner, ready to work.

Tip

For my case, I am using the standard image to build my Docker images with this Runner, and it works just fine. Dockerfiles and info about Docker in Docker images for ARM are here.

Troubleshooting

In case you’re using docker in docker in the runner, you may experience some issues

https://gist.githubusercontent.com/gounthar/7fd4ce41ebdd8b79bf7dfe2472af7119/raw/a3d0b0ff8891609078695ea60de0fc69e7d76fa0/daemon.txt

So in order to address this issue you need to look within the config folder you mounted in the runner container. There is a config file which is/etc/gitlab-runner/config.toml\. You need to add two lines (regarding the privileged`and the `volumes)`and then restart the runner with `docker restart arm-runner. If you can’t find the file, it is maybe in ~/.runner/config.toml/; copy it in /etc/gitlab-runner after editing it and restart docker.

https://gist.githubusercontent.com/gounthar/31d41be5afb4b2c3ce0c66dfc1f00f33/raw/b08565f575646763ca3f71321a625e47f3c3619b/config.toml

So now you can enjoy your first ARM Gitlab build!

We now have an ARM machine sporting a recent kernel, running a recent version of gitlab-runner on top of a recent version of Docker.

We have reached the first level of achievement, having Gitlab runner running on the Pi thanks to Docker. Can we go any further? Could we build Docker images thanks to Gitlab runner on the Pi?

Building Docker Images

If you’re going to build images on this runner you can use the standard Docker image, as it is multi-arch now. Just type docker at “Please enter the Docker image” in the first method or in --docker- image "image:tag" with the second method. Sometimes, you may experience some troubles when using Docker in Docker. So in order to address this issue you need to look within the config folder you mounted in the runner container. There is a config file which is /etc/gitlab-runner/config.toml. You need to add two lines (regarding the privileged and the volumes)and then restart the runner with docker restart arm-runner.

If you can’t find the file, it is maybe in ~/.runner/config.toml/ copy it in /etc/gitlab-runner after editing it and restart docker.

It looks like we’ve reached the second level of achievement, being able to build Docker images through Gitlab runner.

What about another challenge?

Super tiny Runner

Let’s try it on another SBC, much, much cheaper, with half the amount of memory. This time, we’ll change fruit, let’s leave the raspberry aside and try with an Orange… Do you think it could work?

The Orange Pi Zero is one of my preferred SBCs, and almost the cheapest to this date.

You’ll get if for about 9€, and it can run Docker with a very recent kernel thanks to the Armbian distro. This distro is a fantastic community project which allows lots of SBCs to run recent Linux kernels with optimizations.

Armbian is a lightweight Debian based distribution specialized for ARM developing boards. It’s Compiled from scratch, It has powerful build and software development tools, and a vibrant community to boot. It supports lots of chips like the Allwinner H2+, H3, H5, Amlogic S905, Rockchip RK3288 and RK3399, Samsung Exynos 5422… If we go to the download section you can see there are tons of different cards that can run Armbian.

<!-- INSERT HERE DOWNLOAD SECTION VIDEO -→

It you choose OrangePi zero, you can see the specifications. image::https://www.youtube.com/watch?v=dX9I6xDn5AE\[\]

One of them is Docker, which means you can run Docker on this board with Armbian.

<!-- INSERT HERE ORANGE PI ZERO ARMBIAN VIDEO -→

Yes, you can run Docker on a 512MB ARM machine. You can even run gitlab runner on Docker on it. And even Docker on gitlab runner on Docker on it… Il you click on Docker, you will then be able to see the list of SBCs able to run Docker with Armbian.

<!-- INSERT HERE DOCKER MACHINES ARMBIAN VIDEO -→

Impressive, isn’t it?

Docker does not come preinstalled with Armbian, so you have to install it like that.

image::../assets/images/2021/02/04/docker install on armbian.png[docker install on armbian]Note that you could get Docker preinstalled, as you can build this distro by yourself as long as you have an Ubuntu machine available. The rest of the commands to run gitlab runner are strictly the same than for Raspberry Pi, so no need to repeat them. Let’s just have a look at the result, once the runner has been installed, registered and started. Feel like taking a look ?

Here is a build of a Docker image on an Orange Pi Zero. Isn’t it fantastic? We reached the third level of Achievement…

64 bits Runner

Some of the boards handled by Armbian or HypriotOS are ARM64 also called aarch64, or ARMV8. To tell you the truth, even the Raspberry Pi 3B is aarch64… It’s just that Raspbian does not handle it for compatibility reasons with their older boards. You can find 64 bits distros for the Raspberry Pi, like ArchLinux, DietPi, Hypriot… For other cards, Armbian does exist for ARM64, so does Docker. Until 10 months ago, we were kind of stuck, as gitlab-runner for aarch64 did not exist image::https://www.youtube.com/watch?v=dfP3wjcpveY\[\] <!-- INSERT HERE MERGE REQUEST VIDEO -→

The merge request had been running for almost 3 years until it got accepted. We are now able to use gitlab-runner on our 64 bits ARM machine, and without using Mauricio Ugaz fork, just the standard Docker image directly supplied by gitlab.

To sum up, we managed to setup a gitlab-ci Runner on a Raspberry Pi, and then on an OrangePi Zero . You now know how to transform your dusty Raspi into a gitlab- runner, even able to build Docker images. In the company I work for, we have also installed it on other SBCs (Odroid XU4, Nano Pi Fire3 ), and on a ThunderX2 just for fun. We are using them everyday, as they are cheap, disposable, fun to use, and have a very small footprint, so that we install them just about anywhere. I hope to have tickled your curiosity regarding these little beasts called SBCs, and that some of you will join the peaceful ARM rebellion.

Installing asciicast2gif on aarch64 Debian bullseye

Bruno Verachten — Wed, 22 Apr 2026 07:44:12 +0000

Originally published on August 20, 2021 at https://bruno.verachten.fr/2021/08/20/installing-asciicast-on-aarch64/

asciicast2gif: one way to produce a GIF file from your terminal session

Why?

Last year, I wrote a blog post about producing SVG and GIF files from a recording of terminal sessions thanks to termtosvg. This makes sense if you want to illustrate your tutorials with nice moving pictures of what’s going on on the machine. You can see an example of the result of this process just at the beginning of the post. Why should you use a GIF file, an old (1987, same year as PowerPoint!) and bulky format with a very limited color palette instead of SVG? Well, you shouldn’t, but sometimes you have to. Or you think you have to.

Okay, sometimes you really have to, like when using a meme, or posting something on Medium (because yes, Medium can’t (or don’t want to) handle SVG). My problem is now solved, as I sort of got kicked out of Medium, without further explanation (This account is under investigation or was found in violation of the Medium Rules).

Now what? There may be other ways to generate GIF files from SVG or from the .cast recordings, or directly when recording the terminal, but today, we’ll do it with asciicast2gif.

asciicast2gif is, as its name implies, some kind of converter that will take a . cast file as an input, and produce a GIF file. Last time I tried to install asciicast2gif on an ARM machine, it was a painful experience.

The thing is, one of the most important dependency is PhantomJS whose development is suspended, so don’t expect to get a standard package available in your ARM distro anytime. I tried several times to get it to compile, but failed. Furthermore, when you manage to install it, you’d better be patient, because it is really CPU hungry, and even the smallest GIF rendering will takes ages on a powerful machine.

You’re now convinced that you need it, so why not try it together?

How?

This time, I did not try to install asciicast2gif on an ARMV7 machine, nor on a X86_64 machine, but on an ARMV8, the amazing StationP1 from Firefly. It sports a Rockchip RK3399, a 32GB eMMC, 4GB of RAM and a very sturdy box which doubles as a very efficient heatsink. I own and use a few RK3399-based machine, and it’s the most successful one. This thing just works, and it rocks!

Prerequisites

To get asciicast2gif to work, you will need a JRE, gifsicle, ImageMagick, Node.js and npm. Fortunately, everything is available straight from Armbian.

sudo apt install -y gifsicle imagemagick nodejs npm openjdk-11-jre

You will also need to install leiningen in your PATH, so that you can automate Clojure projects without setting your hair on fire.

wget https://raw.githubusercontent.com/technomancy/leiningen/stable/bin/lein && chmod +x lein && sudo mv lein /usr/local/bin/

I told you earlier that I failed several times at building PhantomJS on anything ARM. Once more, I failed today. You could hope that I would get wiser as I’m getting older, but it looks like it’s the other way around. So I took the easy way out. I downloaded a pre-build AARCH64 binary.

cd && wget https://github.com/fg2it/phantomjs-on-raspberry/releases/download/v2.1.1-jessie-stretch-arm64/phantomjs_2.1.1_arm64.tgz && tar -xvzf phantomjs_2.1.1_arm64.tgz && sudo mv phantomjs /usr/local/bin/ && rm phantomjs_2.1.1_arm64.tgz

Installation

Now that all the prerequisites are installed, let’s go straight to the point:

cd && git clone --recursive https://github.com/asciinema/asciicast2gif.git && cd asciicast2gif/
npm install && lein cljsbuild once main && lein cljsbuild once page

Looks like we’re done? On this machine, we’re not. If I had an ARM Workstation that would be ok. But as we’re working on a machine with very limited capacity (disk and RAM), we have to make a few adjustments.

== Need more room for generating GIFs
sudo vi /etc/ImageMagick-6/policy.xml

In this file, you will have to change a value of 1GiB to 8GiB so that you won’t get an error message after a long time.

[...]
== from   <policy domain="resource" name="disk" value="1GiB"/> to
<policy domain="resource" name="disk" value="8GiB"/>
[...]

You will also have to temporarily grow the size of the partition hosting /tmp if you don’t want to face convert-im6.q16: unable to write pixel cache "/tmp/magick-doISVFWvo4dMZbegtgZqkiee3mpVUZ6N": No space left on device @ error/cache.c/WritePixelCachePixels/6012.

sudo mount -o remount,size=8G,noexec,nosuid,nodev,noatime /tmp

Edit the /etc/fstab file to make this changes permanently.

vim /etc/fstab

== <file system>                                 <mount point>   <type>  <options>                                                       <dump>  <pass>
tmpfs                                           /tmp            tmpfs   size=8G,defaults,nosuid                                                 0       0
UUID=147a0457-31cc-44c0-b92a-a9a6b2305749       /               ext4    defaults,noatime,nodiratime,commit=600,errors=remount-ro,x-gvfs-hide    0       1

Now we’re good to go.

Use

./asciicast2gif -s 2 -t solarized-dark ../termtosvg/whatever-name-I-choose.cast demo.gif

Nice, isn’t it? Launch the command, and then go brew a coffee. Or even better, grab some fresh coffee seeds, sow them, wait a few years, and then look at the result while brewing a coffee with the harvested seeds. The usage is:

asciicast2gif [-t theme] [-s speed] [-S scale] [-w cols] [-h rows] <input-json-path-or-url> <output-gif-path>

I chose the solarized-dark theme, but I could have gone with several others:

-t <theme>        color theme, one of: asciinema, tango, solarized-dark, solarized-light, monokai (default: asciinema)
-s <speed>        animation speed (default: 1)
-S <scale>        image scale / pixel density (default: 2)
-w <columns>      clip terminal to specified number of columns (width)
-h <rows>         clip terminal to specified number of rows (height)

TL;DR

sudo apt install -y gifsicle imagemagick nodejs npm openjdk-11-jre
wget https://raw.githubusercontent.com/technomancy/leiningen/stable/bin/lein && chmod +x lein && sudo mv lein /usr/local/bin/
cd && wget https://github.com/fg2it/phantomjs-on-raspberry/releases/download/v2.1.1-jessie-stretch-arm64/phantomjs_2.1.1_arm64.tgz && tar -xvzf phantomjs_2.1.1_arm64.tgz && sudo mv phantomjs /usr/local/bin/ && rm phantomjs_2.1.1_arm64.tgz
cd && git clone --recursive https://github.com/asciinema/asciicast2gif.git && cd asciicast2gif/
npm install && lein cljsbuild once main && lein cljsbuild once page

Then edit sudo vi /etc/ImageMagick-6/policy.xml and replace <policy domain="resource" name="disk" value="1GiB"/> with <policy domain="resource" name="disk" value="8GiB"/>.

Then grow the tmp`partition `sudo mount -o remount,size=8G,noexec,nosuid,nodev,noatime /tmp and you’re ready to go :

./asciicast2gif -s 2 -t solarized-dark ../termtosvg/whatever-name-I-choose.cast demo.gif

Installing libltc on armhf Debian

Bruno Verachten — Wed, 22 Apr 2026 07:44:10 +0000

Originally published on August 20, 2021 at https://bruno.verachten.fr/2021/08/20/installing-libltc-on-armhf/

Installing libltc on armhf Debian

One of my (way too many) current projects is to create a device that would mimic dish.tc. I have created a repository at Gitlab so that anyone can help make this idea come true.

To sum up, we would add an audio track to every recording we make (be it audio or video or both) so that video editing software would then be able to synchronize everything without any human intervention.

I’m not sure yet of the SBC I will use for that job. For sure, I would like to use the OrangePi Zero, as it is cheap (as I am), or any other SBC, as long as it is handled by Armbian and has good audio features (yes, I know the Zero doesn’t have great audio features nor I2S, but I already have it, so…) The first step is then to install libltc which is not yet part of Debian for ARM.

Installation

If you’re lucky, the package will be available from your package repo manager:

sudo apt install libtlc11

Should you not find it, then we’ll install it from source:

git clone https://github.com/x42/libltc.git
cd libltc/
aclocal; autoheader; libtoolize --copy; autoconf; automake --gnu --add-missing --copy
./configure

[...]
 libltc configured:
 -----------------------

  version:             1.3.1
  interface revision:  12:0:1

  doxygen:             no
  installation prefix: /usr/local

 type "make" followed my "make install" as root.
 run "make check" to perform selftests.

Ok, let’s do that:

make && make check
sudo make install

Ok, perfect, the library is now installed on our armhf machine. Now what?

Yes, it’s a library and not a standalone program. You can now develop something that will use this library, or … install other tools that give you the opportunity to generate SMTPE encoded in sounds or sound files. One of them is ltc-tools.

Installing ltc-tools

Unfortunately this time, this tool hasn't made yet its way to the official Debian repositories, so you'll have to build it by yourself.

image::/assets/images/2021/09/01/ltc-tools-clone.cast.svg[ltc-tools-clone.cast]

[source,bash]
----
cd && git clone https://github.com/x42/ltc-tools.git

----

ltc-tools is already packaged to build as a Debian package, so let's install its dependencies

image::/assets/images/2021/09/01/ltc-tools-dependencies.cast.svg[ltc-tools-dependencies.cast]

[source,bash]
----
cd ltc-tools && sudo apt install debhelper dpkg-dev fakeroot libjack-jackd2-dev libltc-dev libsndfile1-dev

----

Now, let's build the package:

image::/assets/images/2021/09/01/ltc-tools-build-package.cast.svg[ltc-tools-build-package.cast]

[source,bash]
----
dpkg-buildpackage -rfakeroot -b -uc -us
ll ../*ltc*deb

----

We just have to install the package now, almost as if it was coming from the official Debian repo:

image::/assets/images/2021/09/01/ltc-tools-install-package.cast.svg[ltc-tools-install-package.cast]

[source,bash]
----
sudo apt install -f ../ltc-tools*deb

----

What are the binaries available by the way?

image::/assets/images/2021/09/01/ltc-tools-whatsin-package.cast.svg[ltc-tools-whatsin-package.cast]

We have

[source,bash]
----
jltc2mtc
jltcdump
jltcgen
jltcntp
jltctrigger
ltcdump
ltcgen

----

Let's see what each of them does.

== Using ltc-tools

=== jltc2mtc: JACK app to translate linear time code to midi time code.

[source,bash]
----
jltc2mtc --help
jltc2mtc - JACK app to translate linear time code to midi time code.

Usage: jltc2mtc [ OPTIONS ]

Options:
  -f, --fps <num>[/den]      set expected [initial] framerate (default 25/1)
  -F, --detectfps            autodetect framerate from LTC (recommended)
  -l, --ltcport <portname>   autoconnect LTC input port
  -m, --mtcport <portname>   autoconnect MTC output port
  -s, --sysex                send system-excluve seek message
                             instead of MTC quarter frames
  -h, --help                 display this help and exit
  -V, --version              print version information and exit


This tool reads LTC from a JACK-audio port and generates corresponding
MTC on a JACK-midi port.

jltc2mtc supports both forward and backwards played timecode, and compensates
for decoder and port latencies.
Note that MTC only supports 4 framerates: 24, 25, 30df and 30 fps.
Framerates other than that are announced as 25fps MTC.
Drop-frame-timecode is detected by the corresponding bit in the LTC frame,
regardless of the -F option. You can /force/ it with -f 30000/1001.

Note that MTC distinguishes between film speed and video speed only by the
rate at which timecode advances, not by the information contained in the
timecode messages; thus, 29.97 fps dropframe is represented as 30 fps
dropframe with 0.1% pulldown

----

Nice utils, but that's not what I'm looking for. Next!

=== jltcdump: JACK app to parse linear time code.

[source,bash]
----
jltcdump --help
jltcdump - JACK app to parse linear time code.

Usage: jltcdump [ OPTIONS ] [ JACK-PORTS ]

Options:
  -f, --fps  <num>[/den]     set expected [initial] framerate (default 25/1)
  -F, --detectfps            autodetect framerate from LTC
  -H  <alpha>
  --highpass <alpha>         set R/S highpass filter coefficient (dflt 0.6)
  -h, --help                 display this help and exit
  -o, --output <path>        write to file(s)
  -s, --signals              start/stop parser using SIGUSR1/SIGUSR2
  -r, --runstop              parse R/S signal on 2nd channel
  -R  <float>,
  --rsthreshold <float>      R/S signal threshold (default 0.01)
  -V, --version              print version information and exit


If both -s and -o are given, <path> is used a prefix:
The filename will be <path>YYMMDD-HHMMSS.tme.XXXXX .
If only -o is set, <path> is as filename.

In 'signal' mode, the application starts in 'idle' state
and won't record LTC until it receives SIGUSR1.

The fps option is only needed to properly track the first LTC frame,
and timecode discontinuity notification.
The LTC-decoder detects and tracks the speed but it takes a few samples
to establish initial synchronization. Setting fps to the expected fps
speeds up the initial sync process. The default is 25/1.

----

Pretty cool, but this is working at the other end of the spectrum, so to speak. I need for the time being to generate timestamps, not yet to read them through the audio input.

=== ltcgen: JACK audio client to generate linear time code in realtime.

[source,bash]
----
jltcgen --help
ltcgen - JACK audio client to generate linear time code in realtime.
Usage: jltcgen [OPTION] [JACK-PORT-TO-CONNECT]*

Options:
 -d, --date datestring      set date, format is either DDMMYY or MM/DD/YY
 -f, --fps fps              set frame-rate NUM[/DEN][ndf|df] default: 25/1ndf
 -h, --help                 display this help and exit
 -g, --volume float         set output level in dBFS default -18db
 -l, --localtime            when using current time, do it in local TZ (not UTC)
 -m, --timezone tz          set timezone in minutes-west of UTC
 -r, --auto-resync          automatically resync if drift is more than 100ms
 -t, --timecode time        specify start-time/timecode [[[HH:]MM:]SS:]FF
 -u, --userbits bcd         specify fixed BCD user bits (max. 8 BCD digits)
                            CAUTION: This ignores any date/timezone settings!
 -w, --wait                 wait for a key-stroke before starting.
 -V, --version              print version information and exit
 -z, --timezone tz          set timezone +HHMM

Unless a timecode (-t) is given, the current time/date are used.
Date (-d) and timezone (-z, -m) are only used if a timecode is given.
The timezome may be specified either as HHMM zone, or in minutes-west of UTC.

SIGINT (CTRL+C) prints current clock-drift (audio-clock - system-clock).
SIGQUIT (CTRL+\) terminates the program.
SIGHUP initialize a re-sync to system clock (unless -t is given).

----

Yes, that's what I was looking for! Spoiler alert: it just won't work on my tiny machine because I haven't yet installed Jack, and because I haven't added the official Zero audio link:https://fr.aliexpress.com/item/32770665186.html?spm=a2g0o.store_pc_groupList.8148356.1.7f667fddpaPeIL[hat] yet on this machine...
I will have to give it another try once I get everything installed (where the hell did I "store" that audio hat?).

[source,bash]
----
jltcgen
Cannot connect to server socket err = No such file or directory
Cannot connect to server request channel
exec of JACK server (command = "/usr/bin/jackd") failed: No such file or directory
Cannot connect to server socket err = No such file or directory

----

You know what, the goal of this blog post was to install libltc, and we already succeeded, so let's call it a day, or close let's just explore the rest of the binaries before going to bed.

=== jltcntp: JACK LTC parser with NTP SHM support.

----
jltcntp --help
jltcntp - JACK LTC parser with NTP SHM support

Usage: jltcntp [ options ] [ JACK-ports ]

Options:
  -f, --fps  <num>[/den]     set expected framerate (default 25/1)
  -u, --unit <u>             send LTC to NTP SHM driver unit <u> (default none)
  -n, --no-date              ignore date received via LTC
  -v, --verbose              output data to stdout
  -h, --help                 display this help and exit
  -V, --version              print version information and exit


----

SHM: _shared memory driver_

NTP: _Network Time Protocol_

Not (yet?) what I'm looking for...

=== jltctrigger: JACK app to trigger actions on given LTC.

[source,bash]
----
jltctrigger --help
jltctrigger - JACK app to trigger actions on given LTC.

Usage: jltctrigger [ OPTIONS ] <cfg-file> ...

Options:
  -c, --connect <port>       auto-connect to given jack-port
  -f, --fps <num>[/den]      set expected [initial] framerate (default 25/1)
  -F, --detectfps            autodetect framerate from LTC
  -h, --help                 display this help and exit
  -p, --print                output decoded LTC (live)
  -v, --verbose              be verbose
  -V, --version              print version information and exit



Actions are defined in a config file, one per line.
  Timecode <Space> Command
Multiple config files can be given.
The fps parameter is used when parsing the config file,
...
The fps option is also used properly track the first LTC frame,
and timecode discontinuity notification.
The LTC-decoder detects and tracks the speed but it takes a few samples
to establish initial synchronization. Setting fps to the expected fps
speeds up the initial sync process. The default is 25/1.

----

Wow, pretty cool, you can set up an action where "hearing" a specific timestamp... What about getting a `crontab` this way? ;-) Okay, that would work only once...

=== ltcdump: parse linear time code from a audio-file.

[source,bash]
----
ltcdump --help
ltcdump - parse linear time code from a audio-file.

Usage: ltcdump [ OPTIONS ] <filename>

Options:
  -a                         write audacity label file-format
  -c, --channel <num>        decode LTC from given audio-channel (first = 1)
  -d, --decodedate           decode date from LTC frame
  -f, --fps  <num>[/den]     set expected [initial] framerate
  -F, --detectfps            autodetect framerate from LTC (recommended)
  -h, --help                 display this help and exit
  -V, --version              print version information and exit


Channel count starts at '1', which is also the default channel to analyze.

The fps option is only needed to properly track the first LTC frame,
and timecode discontinuity notification.
The LTC-decoder detects and tracks the speed but it takes a few samples
to establish initial synchronization. Setting fps to the expected fps
speeds up the initial sync process. The default is 25/1.

----

As I'm not yet able to generate analog audio with the Zero, that could prove handy if I find another util that could generate an LTC audio file. Could that be the very last util?

=== ltcgen: generate linear time code audio-file.

Yes, we got this!

[source,bash]
----
ltcgen --help
ltcgen - generate linear time code audio-file.
Usage: ltcgen [OPTION] <output-file>

Options:
 -d, --date datestring      set date, format is either DDMMYY or MM/DD/YY
 -f, --fps fps              set frame-rate NUM[/DEN][ndf|df] default: 25/1ndf
 -g, --volume float         set output level in dBFS default -18db
 -h, --help                 display this help and exit
 -l, --duration time        set duration of file to encode [[[HH:]MM:]SS:]FF.
 -m, --timezone tz          set timezone in minutes-west of UTC
 -r, --reverse              encode backwards from start-time
 -s, --samplerate sr        specify samplerate (default 48000)
 -t, --timecode time        specify start-time/timecode [[[HH:]MM:]SS:]FF
 -u, --userbits bcd         specify fixed BCD user bits (max. 8 BCD digits)
                            CAUTION: This ignores any date/timezone settings!
 -V, --version              print version information and exit
 -z, --timezone tz          set timezone +HHMM

Unless a timecode (-t) is given, the current time/date are used.
Date (-d) and timezone (-z, -m) are only used if a timecode is given.
The timezome may be specified either as HHMM zone, or in minutes-west of UTC.

If the duration is <=0, ltcgen write until it receives SIGINT.

The output file-format is WAV, signed 16 bit, mono.

----

Let's see if my machine has an almost correct time, despite not having any RTC yet:

[source,bash]
----
date
lundi 6 septembre 2021, 18:50:33 (UTC+0200)

----

My PC is at the same time as the Zero, so all is good (thank you NTP I guess).

[source,bash]
----
ltcgen timecoded-audio.wav
writing to 'timecoded-audio.wav'
samplerate: 48000, duration 60000.0 ms
cfg LTC:   06/09/21 (DD/MM/YY) 16:52:34:03 +0000
wrote 2880192 audio-samples

----

Let's decode the encoded audio now:

image::/assets/images/2021/09/01/ltc-tools-decode-audio.cast.svg[ltc-tools-decode-audio.cast]

[source,bash]
----
ltcdump timecoded-audio.wav |more
ltcdump timecoded-audio.wav |tail

----

We have one second of encoded timestamps. Nice!

Want to hear it? link:/assets/sounds/2021/09/07/timecoded-audio.wav[Here] you go! Yes, you've heard better music in the past.

== Bonus? Packaging the official libltc Debian version, but for armhf

Should you make any change to the source code, it would come in handy to rebuild the Debian package. In order to build a libltc Debian package from source, we need to install necessary build tools:

`debhelper dpkg-dev fakeroot`

To install what's necessary, do

[source,bash]
----
sudo apt install debhelper dpkg-dev fakeroot

----

=== Enable source repositories

Make sure you have enabled the source repositories.

To do so, open _/etc/apt/sources.list_ file:

[source,bash]
----
sudo vi /etc/apt/sources.list

----

You will see some lines in it like below:

[source,bash]
----
deb http://deb.debian.org/debian buster main contrib non-free
#deb-src http://deb.debian.org/debian buster main contrib non-free
deb http://deb.debian.org/debian buster-updates main contrib non-free
#deb-src http://deb.debian.org/debian buster-updates main contrib non-free
[...]

----

In the above file, all the lines beginning with one or two hashes (#) are just comments, for information only. And the lines without hashes are the apt repositories.
Uncomment the `"deb-src` lines by removing the hashes. Then, issue a `sudo apt update`.

=== Install build dependencies for the package

Next, we need to install the dependencies for that package as well. To do so, run:

[source,bash]
----
sudo apt build-dep libltc11

----

image::/assets/images/2021/09/01/ltc-build-dep.cast.svg[ltc-build-dep.cast]

=== Download source packages

Download the source of the libltc package:

[source,bash]
----
apt source libltc

----

image::/assets/images/2021/09/01/ltc-download-package-source.cast.svg[ltc-download-package-source.cast]

=== Build the package

[source,bash]
----
cd libltc-1.3.0/
dpkg-buildpackage -rfakeroot -b -uc -us
ll ../libltc*deb

----

image::/assets/images/2021/09/01/ltc-build-package-source.cast.svg[ltc-build-package-source.cast]

[source,bash]
----
-rw-r--r-- 1 poddingue poddingue 12940 sept.  6 22:20 ../libltc11_1.3.0-1_armhf.deb
-rw-r--r-- 1 poddingue poddingue 22020 sept.  6 22:20 ../libltc11-dbgsym_1.3.0-1_armhf.deb
-rw-r--r-- 1 poddingue poddingue 13420 sept.  6 22:20 ../libltc-dev_1.3.0-1_armhf.deb
-rw-r--r-- 1 poddingue poddingue 14468 sept.  6 22:20 ../libltc-doc_1.3.0-1_all.deb

----

termtosvg on aarch64 Debian

Bruno Verachten — Wed, 22 Apr 2026 07:44:08 +0000

Originally published on August 20, 2021 at https://bruno.verachten.fr/2021/08/20/installing-termtosvg-on-aarch64/

Installing termtosvg on an aarch64 machine running bullseye.

What is termtosvg and what can I do with it?

Term to what?

termtosvg is an abandonned utility that allows to record terminal sessions as standalone SVG animations. Those animations come pretty handy if you want to spice up your documentation. Up to now it wasn’t available as a package in Debian, so we had to build it on the machine. Fortunately, with recent and top notch distros like Armbian, we can install it with a simple

``` sudo apt install termtosvg----
You may already know how much I love Armbian, and how much I love the link:http://bruno.verachten.fr/2021/02/18/How-to-Install-Armbian-on-the-Station-P1/[Station P1], so the combination of the two is just like heaven on earth.

image::/assets/images/2021/08/20/kardashian-sassy.gif[kardashian-sassy]

I already had written a blog post for termtosvg for the OrangePi link:/2020/01/03/Terminal-recording-on-the-OrangePi-Zero/[Zero].

image::/assets/images/2021/08/20/whatever-name-I-choose.svg[whatever-name-I-choose]

=== How to record my terminal

The animated image just on top of this text has been generated thanks to two commands, the first one being:

----bash

termtosvg record -g 200x60 whatever-name-I-choose.cast

The -g is there to specify the geometry of your target terminal. The recordpart is there to tell you want to get a .cast file at the end. That's not mandatory, you could get your command as simple as termtosvg and get the resulting SVG file as soon as you enter the exit() command. We'll see in a few paragraphs why I want to generate an intermediary .cast file.

Pretty cool, heh?

Okay, my terminal settings are not that fancy, so let's throw glitter in your eyes.

image::/assets/images/2021/08/20/glitter.svg[glitter]

Now we're talking! This "cast" has been generated with the very same command. So the command once you enter exit()will create a .cast file which will then be used to generate a SVG file (with termtosvg) or a GIF file with the asciicast2gif command.

=== How to generate the resulting SVG file

Next mandatory step: generate the SVG file. Commands do not come any simpler:

----termtosvg render glitter.cast glitter.svg----

Afterwards, you just have to move your file wherever you need it and link/import it into your documentation.
You're welcome.

Now, why the hell did I generate an intermediary .castfile? Well, that format can be used as an input for another project, link:https://github.com/asciinema/asciicast2gif[asciicast2gif], which is part of link:https://github.com/asciinema/asciinema[asciinema]. Asciinema uses the same .cast format as termtosvg, so we can generate SVG from asciinema cast files, GIF files from termtosvg castfiles, and the other way around.

Now, what if you're out of luck, and termtosvg is not available on your distro? Let's try to build it by ourselves:

== Compile and install termtosvg

----bash
git clone https://github.com/nbedos/termtosvg.git && cd termtosvg/
== Prerequisites, use apt, apt-get, yum, whatever package manager you have on your system
sudo apt-get install -y libxml2-dev libxslt-dev python3 python3-dev python3-pip python3-setuptools
make build
== If you ever have an error saying that python is not available, you could make an horrible hack like
== sudo ln -s /usr/bin/python3 /usr/bin/python or anything smarter
== Now, let's install it. Note that 1.1.0-py3 is the version that was available at the time I wrote this blog post, your
== mileage may vary
pip3 install dist/termtosvg-1.1.0-py3-none-any.whl




<figure>
<img src="https://bruno.verachten.fr/assets/images/2021/08/20/great%20success.gif" alt="great success" />
</figure>

Now it’s your turn to produce good looking shell recordings with termtosvg.

Using jenkins to build Android applications

Bruno Verachten — Wed, 22 Apr 2026 07:44:05 +0000

Originally published on August 2, 2022 at https://bruno.verachten.fr/2022/08/02/naively-building-android-apps-with-jenkins/

Come join me at DevOps World 2022 for "Naively Building Android Apps with Jenkins. Not Natively, Naively.", a talk about my journey with Jenkins while trying to build and publish Android applications without prior Jenkins knowledge.

I’ve been tinkering with various CI/CD tools for years (Gitlab CI, Circle CI, Travis CI, Shippable, Github Actions, …) but not with Jenkins for whatever reason.

I’ve been supporting since 2014 mobile application developers through the use of gitlab-ci.

For DevOpsWorld 2022, I’ve been given a challenge: what if I tried to replicate that well oiled gitlab-centric machinery that I had built for years this time with Jenkins? At least… Could I mimic a subset of the functionality of my previous work without knowing anything about Jenkins?

To spice things up a bit, I might as well refrain from looking at what has already been done elsewhere; thus, I may end up with a shaky solution, or even a functional system that can be replicated at will.

Topics will include:

What does one need to build an Android application?
When your only tool is a hammer, everything looks like a nail (an ode to containerization)
Credentials and authentication
From Docker to the Cloud?
Quality Assurance, Tests, and Deployment

This is not a definitive guide to build an Android application with Jenkins, more like a roadmap to my journey with Jenkins… with takeaways from my experience.

Come join me for the presentation in Orlando!

Create a new Jenkins node, and run your Jenkins agent as a service

Bruno Verachten — Wed, 22 Apr 2026 07:44:03 +0000

Originally published on August 2, 2022 at https://bruno.verachten.fr/2022/08/02/run-your-jenkins-agent-as-a-service/

In this tutorial, we will see how to start a Jenkins agent as a Linux service with systemd. Most of the time, I use Docker for my agents, and I enter the right options on the command line so that they restart automatically. But sometimes, especially when you want to use the famous Dockerfile: true option, you need to start the agent manually with a java command and not with Docker (for various security reasons). And then you need to restart it manually if you have to reboot, or if you forget to use nohup to start it in the background and then close the terminal.

Prerequisites

Let’s say we’re starting with a fresh Ubuntu 22.04 Linux installation. What do we need to get an agent working? Java, that’s for sure, but also Docker if we want to use Docker for our agents instead of installing everything directly on the machine.

Java

Nowadays, openjdk 11 is recommended, and openjdk 17 is supported. Let’s go with openjdk 17:

sudo apt-get update
sudo apt install -y --no-install-recommends openjdk-17-jdk-headless

Let’s now verify if java works for us:

java -version
openjdk version "17.0.3" 2022-04-19
OpenJDK Runtime Environment (build 17.0.3+7-Ubuntu-0ubuntu0.22.04.1)
OpenJDK 64-Bit Server VM (build 17.0.3+7-Ubuntu-0ubuntu0.22.04.1, mixed mode, sharing)

Jenkins user

As we’re creating an agent, we’d better separate rights, permissions, and ownership. Let’s create a user for Jenkins:

sudo adduser --group --home /home/jenkins --shell /bin/bash jenkins

Docker

Now, to get a recent version of Docker, we should install the docker-ce package and a few others with a particular repo. First, let’s add the needed dependencies to add the repo:

sudo apt-get install ca-certificates curl gnupg lsb-release

In my case, these packages were already installed and up to date. The next step is to add Docker’s official GPG key:

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

Then, we can set up the repo:

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

The last thing to do is to update the list of available packages, and then install the latest version of Docker:

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

If you’re - like me - running a recent version of Ubuntu, you won’t need to create the docker group, because it has been created with the installation of Docker. On the contrary, you can then issue a sudo groupadd docker command to create the docker group.

Now, let’s add our current user to the docker group:

sudo usermod -aG docker $USER

And if you’re not using the default user, but jenkins, you can do the same:

sudo usermod -aG docker jenkins
sudo usermod -aG sudo jenkins

Now log out, and log back in so that your group membership is updated. If you get any error, just reboot the machine, this sometimes happens. ¯_(ツ)_/¯

Mandatory "Hello World!" Docker installation test:

docker run hello-world
Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
2db29710123e: Pull complete
Digest: sha256:53f1bbee2f52c39e41682ee1d388285290c5c8a76cc92b42687eecf38e0af3f0
Status: Downloaded newer image for hello-world:latest

Hello from Docker!
This message shows that your installation appears to be working correctly.

To generate this message, Docker took the following steps:
 . The Docker client contacted the Docker daemon.
 . The Docker daemon pulled the "hello-world" image from the Docker Hub.
    (amd64)
 . The Docker daemon created a new container from that image which runs the
    executable that produces the output you are currently reading.
 . The Docker daemon streamed that output to the Docker client, which sent it
    to your terminal.

To try something more ambitious, you can run an Ubuntu container with:
 $ docker run -it ubuntu bash

Share images, automate workflows, and more with a free Docker ID:
 https://hub.docker.com/

For more examples and ideas, visit:
 https://docs.docker.com/get-started/

Nice!

Create a new node in Jenkins

Quoting the official documentation,

Nodes are the "machines" on which build agents run.

and also:

Agents manage the task execution on behalf of the Jenkins controller by using executors. An agent is actually a small (170KB single jar) Java client process that connects to a Jenkins controller and is assumed to be unreliable. An agent can use any operating system that supports Java. Tools required for builds and tests are installed on the node where the agent runs; they can be installed directly or in a container (Docker or Kubernetes).

To conclude:

In practice, nodes and agents are essentially the same but it is good to remember that they are conceptually distinct.

We will now create a new node in Jenkins, using our Ubuntu machine as the node, and then launch an agent on this node.

Node creation in the UI

Go to your Jenkins dashboard
Go to Manage Jenkins option in the main menu
Go to Manage Nodes and clouds item

Go to New Node option in the side menu
Fill in the Node name (My New Ubuntu 22.04 Node with Java and Docker installed for me) and type (Permanent Agent for me)

Click on the Create button
In the Description field, enter if you want a human-readable description of the node (My New Ubuntu 22.04 Node with Java and Docker installed for me)
Let 1 as the number of executors for the time being. A good value to start with would be the number of CPU cores on the machine (unfortunately for me, it’s 1)
As Remote root directory, enter the directory where you want to install the agent (/home/jenkins for me)

An agent should have a directory dedicated to Jenkins. It is best to use an absolute path, such as /var/jenkins or c:\jenkins. This should be a path local to the agent machine. There is no need for this path to be visible from the controller.

Regarding the Labels field, enter the labels you want to assign to the node (ubuntu linux docker jdk17 for me, which makes four labels. This will help you group multiple agents into one logical group)
For the Usage now, choose Use this node as much as possible for the time being, you will be able to restrict later on the kind of jobs that can be run on this node.
The last thing to set up now: choose Launch agent by connecting it to the controller . That means that you will have to launch the agent on the node itself and that the agent will then connect to the controller. That’s pretty handy when you want to build Docker images, or when your process will use Docker images… You could also have the controller launch an agent directly via Docker remotely, but then you would have to use Docker in Docker, which is complicated and insecure.

Node configuration

The Save button will create the node within Jenkins, and lead you to the Manage nodes and clouds page. Your new node will appear brown in the list, and you can click on it to see its details. The details page displays your java command line to start the agent. image::/assets/images/2022/08/03/java-command-to-launch-the-agent.png[Command to launch the agent]

This command looks like that for me:

curl -sO http://my_ip:8080/jnlpJars/agent.jar
java -jar agent.jar -jnlpUrl http://my_ip:8080/computer/My%20New%20Ubuntu%2022%2E04%20Node%20with%20Java%20and%20Docker%20installed/jenkins-agent.jnlp -secret my_secret -workDir "/home/jenkins"

You can now go back into Jenkins' UI, click on the Back to List menu item on the left and see that your new agent is doing fine.

Now what? Are we done yet? I’m afraid we’re not. Whenever you close the terminal you launched the agent with, the agent will stop. If you ever have to reboot the machine after a kernel update, you will have to restart the agent manually too. Therefore, I suggest you keep the agent running by declaring it as a service.

Run your Jenkins agent as a service

Create a directory called jenkins or jenkins-service in your home directory or anywhere else where you have access. It could be in /usr/local/jenkins-service for example. If the new directory does not belong to the current user home, give it the right owner and group after creation. For me, it would look like the following:

sudo mkdir -p /usr/local/jenkins-service
sudo chown jenkins /usr/local/jenkins-service

Move the agent.jar file that you downloaded earlier with the curl command to this directory.

mv agent.jar /usr/local/jenkins-service

Now (in /usr/local/jenkins-service) create a start-agent.sh file with the Jenkins java command we’ve seen earlier as the file’s content.

#!/bin/bash
cd /usr/local/jenkins-service
== Just in case we would have upgraded the controller, we need to make sure that the agent is using the latest version of the agent.jar
curl -sO http://my_ip:8080/jnlpJars/agent.jar
java -jar agent.jar -jnlpUrl http://my_ip:8080/computer/My%20New%20Ubuntu%2022%2E04%20Node%20with%20Java%20and%20Docker%20installed/jenkins-agent.jnlp -secret my_secret -workDir "/home/jenkins"
exit 0

Make the script executable by executing chmod +x start-agent.sh in the directory.

Now create a /etc/systemd/system/jenkins-agent.service file with the following content:

[Unit]
Description=Jenkins Agent

[Service]
User=jenkins
WorkingDirectory=/home/jenkins
ExecStart=/bin/bash /usr/local/jenkins-service/start-agent.sh
Restart=always

[Install]
WantedBy=multi-user.target

We still have to enable the daemon with the following command:

sudo systemctl enable jenkins-agent.service

Let’s have a look at the system logs before starting the daemon:

journalctl -f &

Now start the daemon with the following command.

sudo systemctl start jenkins-agent.service

I can see some interesting logs in the journalctl output:

Aug 03 19:37:27 ubuntu-machine systemd[1]: Started Jenkins Agent.
Aug 03 19:37:27 ubuntu-machine sudo[8821]: pam_unix(sudo:session): session closed for user root
Aug 03 19:37:28 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:28 PM org.jenkinsci.remoting.engine.WorkDirManager initializeWorkDir
Aug 03 19:37:28 ubuntu-machine bash[8826]: INFO: Using /home/jenkins/remoting as a remoting work directory
Aug 03 19:37:28 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:28 PM org.jenkinsci.remoting.engine.WorkDirManager setupLogging
Aug 03 19:37:28 ubuntu-machine bash[8826]: INFO: Both error and output logs will be printed to /home/jenkins/remoting
Aug 03 19:37:28 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:28 PM hudson.remoting.jnlp.Main createEngine
Aug 03 19:37:28 ubuntu-machine bash[8826]: INFO: Setting up agent: My New Ubuntu 22.04 Node with Java and Docker installed
Aug 03 19:37:28 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:28 PM hudson.remoting.Engine startEngine
Aug 03 19:37:28 ubuntu-machine bash[8826]: INFO: Using Remoting version: 3046.v38db_38a_b_7a_86
Aug 03 19:37:28 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:28 PM org.jenkinsci.remoting.engine.WorkDirManager initializeWorkDir
Aug 03 19:37:28 ubuntu-machine bash[8826]: INFO: Using /home/jenkins/remoting as a remoting work directory
Aug 03 19:37:29 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:29 PM hudson.remoting.jnlp.Main$CuiListener status
Aug 03 19:37:29 ubuntu-machine bash[8826]: INFO: Locating server among [http://controller_ip:58080/]
Aug 03 19:37:29 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:29 PM org.jenkinsci.remoting.engine.JnlpAgentEndpointResolver resolve
Aug 03 19:37:29 ubuntu-machine bash[8826]: INFO: Remoting server accepts the following protocols: [JNLP4-connect, Ping]
Aug 03 19:37:29 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:29 PM hudson.remoting.jnlp.Main$CuiListener status
Aug 03 19:37:29 ubuntu-machine bash[8826]: INFO: Agent discovery successful
Aug 03 19:37:29 ubuntu-machine bash[8826]:   Agent address: controller_ip
Aug 03 19:37:29 ubuntu-machine bash[8826]:   Agent port:    50000
Aug 03 19:37:29 ubuntu-machine bash[8826]:   Identity:      31:c4:f9:31:46:c3:eb:72:64:a3:c7:d6:c7:ea:32:2f
Aug 03 19:37:29 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:29 PM hudson.remoting.jnlp.Main$CuiListener status
Aug 03 19:37:29 ubuntu-machine bash[8826]: INFO: Handshaking
Aug 03 19:37:29 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:29 PM hudson.remoting.jnlp.Main$CuiListener status
Aug 03 19:37:29 ubuntu-machine bash[8826]: INFO: Connecting to controller_ip:50000
Aug 03 19:37:29 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:29 PM hudson.remoting.jnlp.Main$CuiListener status
Aug 03 19:37:29 ubuntu-machine bash[8826]: INFO: Trying protocol: JNLP4-connect
Aug 03 19:37:29 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:29 PM org.jenkinsci.remoting.protocol.impl.BIONetworkLayer$Reader run
Aug 03 19:37:29 ubuntu-machine bash[8826]: INFO: Waiting for ProtocolStack to start.
Aug 03 19:37:30 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:30 PM hudson.remoting.jnlp.Main$CuiListener status
Aug 03 19:37:30 ubuntu-machine bash[8826]: INFO: Remote identity confirmed: 31:c4:f9:31:46:c3:eb:72:64:a3:c7:d6:c7:ea:32:2f
Aug 03 19:37:30 ubuntu-machine bash[8826]: Aug 03, 2022 7:37:30 PM hudson.remoting.jnlp.Main$CuiListener status
Aug 03 19:37:30 ubuntu-machine bash[8826]: INFO: Connected

We can now check the status with the command below and the output should be similar to what you can see below.

sudo systemctl status jenkins-agent.service
● jenkins-agent.service - Jenkins Agent
     Loaded: loaded (/etc/systemd/system/jenkins-agent.service; enabled; vendor preset: enabled)
     Active: active (running) since Wed 2022-08-03 19:37:27 UTC; 4min 0s ago
   Main PID: 8825 (bash)
      Tasks: 22 (limit: 1080)
     Memory: 63.1M
        CPU: 9.502s
     CGroup: /system.slice/jenkins-agent.service
             ├─8825 /bin/bash /usr/local/jenkins-service/start-agent.sh
             └─8826 java -jar agent.jar -jnlpUrl http://controller_ip:8080/computer/My%20New%20Ubuntu%2022%2E04%20Node%20with%20Java%20and%20Docker%20installed/jenkins-agent.jnlp -secret my_secret>

Just for fun, we can now reboot the machine and see on the UI if the agent is still running once the boot is finished.

Build a Jenkins MSI on your own Windows machine

Bruno Verachten — Wed, 22 Apr 2026 07:44:01 +0000

Originally published on December 1, 2022 at https://bruno.verachten.fr/2022/12/01/building-the-jenkins-msi-on-your-windows-machine/

Should you ever need to rebuild a Jenkins MSI on your Windows machine, here is a way to do it.

Pre-requisites

Jenkins WAR file

First of all, you should get the Jenkins war file that will be inside that MSI file. You can get it from the official Jenkins website or from the Jenkins update center. I will use the official Jenkins website in this article.

Check Jenkins download page and download the latest weekly version of Jenkins for example.

Git tool

You have a few options here: * Install Git for Windows * Use your IDE to clone the repository * Use GitHub Desktop * Use SourceTree * Use Cygwin integrated Git command * […]

Install MSBuild

You can install MSBuild from Visual Studio or from the Build Tools for Visual Studio.

This command line tool is used to build the MSI file.

Install .NET Framework 3.5

You may already have it installed on your machine, but not activated. You can activate it from the Windows Features dialog box.

To access this dialog box, smash the WinKey plus R, then enter the command appwiz.cpl and push enter. Search for

Turn Windows features on or off.

Tick the .NET Framework 3.5 entry and install.

Important: now run Windows Update to check for security updates.

If it is not installed yet, you can install .NET Framework 3.5 from the Windows Features.

Check you have Powershell installed

In recent versions of Windows, PowerShell is already installed and accessible through the terminal application. At the time of writing, the pre-installed version is 5.1.22621.963. You can also install the latest version from the Microsoft Store (7.3.2 at the time of writing).

Clone the Jenkins repository and build the MSI

Clone the Jenkins packaging repository

Choose your git tool and clone the Jenkins packaging repository on your machine.

Prepare the build

Open a terminal and go to the folder where you cloned the repository. For me it is C:\dev\jenkins\git\ci\packaging\. You now have to declare where you downloaded the Jenkins war file so that the build can find it.

$env:War = "$env:USERPROFILE\jenkins.war"

If you ever moved it into your repository clone folder, you can use this command instead:

$env:War = "C:\dev\jenkins\git\ci\packaging\msi\build\jenkins.war"

Build the MSI

Enter the subfolder msi\build and run the following command:

.\build.ps1

For me, this was the output:

Extracting components
JenkinsVersion = 2.392
Restoring packages before build
All packages listed in packages.config are already installed.
Building MSI
MSBuild version 17.4.0+18d5aef85 for .NET Framework
Build started 01/12/2022 20:53:30.
Project "C:\dev\jenkins\git\ci\packaging\msi\build\jenkins.wixproj" on node 1 (default targets).
SetConstants:
  EncodedVersion = 2.255.3920
Compile:
Skipping target "Compile" because all output files are up-to-date with respect to the input files.
AssignCultures:
  Culture: en-US
Link:
  C:\dev\jenkins\git\ci\packaging\msi\build\packages\WiX.3.11.1\build\..\tools\Light.exe -out C:\dev\jenkins\git\ci\packaging\msi\build\bi
  n\Release\en-US\jenkins-2.392.msi -pdbout C:\dev\jenkins\git\ci\packaging\msi\build\bin\Release\en-US\jenkins-2.392.wixpdb -sw1076 -cultures:en-US -ext C:\S
  upport\users\jenkins\git\ci\packaging\packaging\msi\build\packages\WiX.3.11.1\build\..\tools\\WixUIExtension.dll -ext C:\dev\jenkins\git\ci\packaging\msi\bu
  ild\packages\WiX.3.11.1\build\..\tools\\WixNetFxExtension.dll -ext C:\dev\jenkins\git\ci\packaging\msi\build\packages\WiX.3.11.1\build\..\tools\\WixUtilExte
  nsion.dll -ext .\msiext-1.5\WixExtensions\WixCommonUIExtension.dll -ext C:\dev\jenkins\git\ci\packaging\msi\build\packages\WiX.3.11.1\build\..\tools\\WixFir
  ewallExtension.dll -fv -loc jenkins_en-US.wxl -spdb -contentsfile obj\Release\jenkins.wixproj.BindContentsFileListen-US.txt -outputsfile obj\Release\jenkins.wixproj.BindOutputs
  FileListen-US.txt -builtoutputsfile obj\Release\jenkins.wixproj.BindBuiltOutputsFileListen-US.txt -wixprojectfile C:\dev\jenkins\git\ci\packaging\msi\build\
  jenkins.wixproj obj\Release\jenkins.wixobj
  Windows Installer XML Toolset Linker version 3.11.1.2318
  Copyright (c) .NET Foundation and contributors. All rights reserved.

  jenkins -> C:\dev\jenkins\git\ci\packaging\msi\build\bin\Release\en-US\jenkins-2.392.msi
Done Building Project "C:\dev\jenkins\git\ci\packaging\msi\build\jenkins.wixproj" (default targets).


Build succeeded.
    0 Warning(s)
    0 Error(s)

Time Elapsed 00:00:08.26

Find the MSI file

The MSI file is located in the .\bin\Release\en-US\ folder. You will find there the generated MSI file and its sha256 file.

 ls

    Directory: C:\dev\jenkins\git\ci\packaging\msi\build\bin\Release\en-US


Mode                 LastWriteTime         Length Name
----                 -------------         ------ ----
-a----        01/12/2022     20:53      105107456 jenkins-2.392.msi
-a----        01/12/2022     20:53             84 jenkins-2.392.msi.sha256