Raspbian Desktop LoveRPi US Edition for Raspberry Pi Boards

Love Our Pi

The folks over at LoveRPi announced their customized Raspbian Desktop images for Raspberry Pi boards. This release is based on the 2018-11-13 release of Raspbian Desktop Full with our modifications to improve performance and stability.




Modifications:

  • Larger FAT32 partition starting at standard 1MB offset
  • BTRFS root filesystem with compression, checksumming, and metadata duplication
  • Automatic kernel initrd generation and pruning on upgrade (apt-get and rpi-update)
  • Automatic filesystem resize to disk size without reboot
  • Automatic swap partition generation on first start
  • Disable overscan by default
  • Rotate LCD for correct orientation on Raspberry Pi Touchscreen Display
  • Change radio frequency support, locale, and timezone to US
  • Include touchscreen keyboard and vim by default
  • Remove unnecessary piwiz autostart
  • Reduced image size by 1GB for faster flashing
  • Desktop link assets for quick reference (removable)

Supported Boards:

  • Raspberry Pi 3 Model B+
  • Raspberry Pi 3 Model B
  • Raspberry Pi 2 Model B
  • Raspberry Pi Model B
  • Raspberry Pi Zero W
  • Raspberry Pi Zero

DOWNLOAD LINK: http://share.loverpi.com/board/raspberry-pi/raspbian/2018-11-13-raspbian-stretch-desktop-loverpi.zip
FILENAME: 2018-11-13-raspbian-stretch-desktop-loverpi.zip
SHA512SUM: 7dcd6402e52fd3981d150185368bde018c46963d629f93fe6059b71ef033066a5ddcc95589e32033769722c40df7dcd0b9456c54d8704949698c5603c5b211c5

Lite image available here.

Jetson Nano Brings AI Computing to Everyone

NVIDIA announced the Jetson Nano Developer Kit at the 2019 NVIDIA GPU Technology Conference (GTC), a $99 computer available now for embedded designers, researchers, and DIY makers, delivering the power of modern AI in a compact, easy-to-use platform with full software programmability. Jetson Nano delivers 472 GFLOPS of computing performance with a quad-core 64-bit ARM CPU and a 128-core integrated NVIDIA GPU. It also includes 4GB LPDDR4 memory in an efficient, low-power package with 5W/10W power modes and 5V DC input, as shown below.


Jetson Nano Developer Kit (80x100mm), available now for $99

The Jetson Nano Developer Kit fits in a footprint of just 80x100mm and features four high-speed USB 3.0 ports, MIPI CSI-2 camera connector, HDMI 2.0 and DisplayPort 1.3, Gigabit Ethernet, M.2 Key-E module, MicroSD card slot, and 40-pin GPIO header. The ports and GPIO header works out-of-the-box with a variety of popular peripherals, sensors, and ready-to-use projects, such as the 3D-printable deep learning JetBot that NVIDIA has open-sourced on GitHub.

The devkit boots from a removable MicroSD card which can be formatted and imaged from any PC with an SD card adapter. The devkit can be conveniently powered via either the Micro USB port or a 5V DC barrel jack adapter. The camera connector is compatible with affordable MIPI CSI sensors including modules based on the 8MP IMX219, available from Jetson ecosystem partners. Also supported is the Raspberry Pi Camera Module v2, which includes driver support in JetPack. Table 1 shows key specifications.

Jetson Nano specifications

The devkit is built around a 260-pin SODIMM-style System-on-Module (SoM), shown in figure 2. The SoM contains the processor, memory, and power management circuitry. The Jetson Nano compute module is 45x70mm and will be shipping starting in June 2019 for $129 (in 1000-unit volume) for embedded designers to integrate into production systems. The production compute module will include 16GB eMMC onboard storage and enhanced I/O with PCIe Gen2 x4/x2/x1, MIPI DSI, additional GPIO, and 12 lanes of MIPI CSI-2 for connecting up to three x4 cameras or up to four cameras in x4/x2 configurations. Jetson’s unified memory subsystem, which is shared between CPU, GPU, and multimedia engines, provides streamlined ZeroCopy sensor ingest and efficient processing pipelines.

Deep Learning Inference Benchmarks

Jetson Nano can run a wide variety of advanced networks, including the full native versions of popular ML frameworks like TensorFlow, PyTorch, Caffe/Caffe2, Keras, MXNet, and others. These networks can be used to build autonomous machines and complex AI systems by implementing robust capabilities such as image recognition, object detection and localization, pose estimation, semantic segmentation, video enhancement, and intelligent analytics.

Figure 3 shows results from inference benchmarks across popular models available online. The inferencing used batch size 1 and FP16 precision, employing NVIDIA’s TensorRT accelerator library included with JetPack 4.2. Jetson Nano attains real-time performance in many scenarios and is capable of processing multiple high-definition video streams.

Multi-Stream Video Analytics

Jetson Nano processes up to eight HD full-motion video streams in real-time and can be deployed as a low-power edge intelligent video analytics platform for Network Video Recorders (NVR), smart cameras, and IoT gateways. NVIDIA’s DeepStream SDK optimizes the end-to-end inferencing pipeline with ZeroCopy and TensorRT to achieve ultimate performance at the edge and for on-premises servers. The video below shows Jetson Nano performing object detection on eight 1080p30 streams simultaneously with a ResNet-based model running at full resolution and a throughput of 500 megapixels per second (MP/s).

2018 LinuxQuestions.org Members Choice Award Winners

ermy posted the 2018 LinuxQuestions.org Members Choice Award Winners list over at the Linux Questions website. Click the link below to be taken to the closed poll.

Linux Questions

https://www.linuxquestions.org/questions/linux-news-59/2018-linuxquestions-org-members-choice-award-winners-4175648153/

jermy says:

The polls are closed and the results are in. We once again had some extremely close races and the large number of new categories this year certainly kept things interesting. Congratulations to each and every nominee. The official results:

  • Audio Media Player Application of the Year – VLC (24.10%)
  • Backup Application of the Year – rsync (43.36%)
  • Browser of the Year – Firefox (57.63%)
  • Browser Privacy Solution of the Year – uBlock Origin (31.21%)
  • Container of the Year – Docker (57.63%)
  • Database of the Year – MariaDB (44.59%)
  • Desktop Distribution of the Year – Linux Mint (14.93%)
  • Desktop Environment of the Year – Plasma Desktop (KDE) (29.43%)
  • Digital Audio Workstation of the Year – Ardour (33.33%)
  • Email Client of the Year – Thunderbird (61.54%)
  • File Manager of the Year – Dolphin (25.68%)
  • Host Security Application of the Year – AppArmor (31.25%)
  • IDE of the Year – Visual Studio Code (19.08%)
  • IRC Client of the Year – HexChat (47.67%)
  • Linux Desktop Vendor of the Year – System76 (55.17%)
  • Linux Server Vendor of the Year – Dell (32.69%)
  • Linux/Open Source Podcast of the Year – GNU World Order (20.00%)
  • Live Distribution of the Year – antiX (24.70%)
  • Log Management Tool of the Year – Logwatch (43.75)
  • Network Monitoring Application of the Year – Nagios XI (30.51%)
  • Network Security Application of the Year – Wireshark (20.25%)
  • Open Source File Sync Application of the Year – Nextcloud / Syncthing tie (25.93%)
  • Open Source Game of the Year – SuperTuxKart / 0 A.D. tie (16.51%)
  • Orchestrator of the Year – Kubernetes (74.19%)
  • Privacy Solution of the Year – GnuPG (27.88%)
  • Programming Language of the Year – Python (32.51%)
  • Raster Graphics Editor of the Year – GIMP (79.49%)
  • Secure Messaging Application of the Year – Signal (40.00%)
  • Server Distribution of the Year – Slackware (25.69%)
  • Single Board Computer of the Year – Raspberry Pi 3 Model B+ (58.43%)
  • Text Editor of the Year – vim (24.92%)
  • Universal Packaging Format of the Year – Appimage (38.89%)
  • Video Authoring Application of the Year – KDEnlive (41.67%)
  • Video Media Player of the Year – VLC (65.00%)
  • Video Messaging Application of the Year – Skype (44.90%)
  • Virtualization Application of the Year – VirtualBox (56.79%)
  • Window Manager of the Year – Openbox (24.64%)
  • X Terminal Emulator of the Year – Konsole (20.94%)

jermy also says:

If you have any questions or suggestions on how we can improve the MCA’s next year, do let us know. Visit https://www.linuxquestions.org/quest…ce-awards-128/ to view the individual polls, which contain the complete results. Visit http://www.linuxquestions.org/questions/2018mca.php for a visual representation of each category on a single page.

–jeremy

DropBox Uploader

Frustratingly for Raspberry Pi users, there is no Dropbox client available. While you can access the popular cloud storage solution via the Chromium browser (and alternatives are available) a handy command line script might just come to your rescue.

Created by Andrea Fabrizi, this can be installed via the Terminal (or remotely via SSH) with:

Dropbox
git clone https://github.com/andreafabrizi/Dropbox-Uploader.git

Once the GIT file has downloaded, make the script executable and run it:

cd Dropbox-Uploader
sudo chmod +x dropbox_uploader.sh
sudo ./dropbox_uploader.sh

You’ll then be prompted to enter a unique key. This is where things get a little complicated.

Add your access token:
  1. Visit www.dropbox.com/developers and log in with your Dropbox account.
  2. Click Create your app, select Dropbox API, and Full Dropbox, then give the app a unique name (“pi-sync” preceded by your initials, for example) and agree to the Terms and Conditions.
  3. Click Create app to proceed, then copy the App key and App secret strings.
  4. Copy the key into the Terminal window where prompted, and you’ll be able to upload your files to Dropbox.

Use commands formatted like this:

sudo ./dropbox_uploader.sh upload /home/pi/screenplay.odt /docs/screenplay.odt

To summarize, this command calls the Dropbox Uploader script, uses the “upload” command, and syncs the screenplay.odt from its location on the Pi to a new location in the “docs” directory in Dropbox.

3D Xmas Tree for Raspberry Pi

This was a very nice weekend project that I highly recommend that was created by the folks over at the PiHut. I have the working video above showing you how it works, the AsciiCast to show you how to set this up via the command just below this text.

Here is a direct link to the AsciiCast.

As a bonus, I have another video at the extreme bottom that will show you how to do this from the GUI on a Raspberry Pi. So many ways to set this up to cater to all of the different people out there. 🙂

The easiest way to control your 3D Christmas board is with Thonny. This is pre-installed in Raspbian Stretch.

So you can click on the Raspberry icon > Programming > Thonny.

Once Thonny is open paste the following code into it and then click on “Run”

from gpiozero import LEDBoard
from gpiozero.tools import random_values
from signal import pause
tree = LEDBoard(*range(2,28),pwm=True)
for led in tree:
 led.source_delay = 0.25
 led.source = random_values()
pause()

Once you have done that it will prompt you to give the code you just pasted a file name (e.g. xmas.py).

It will save the code as that file name and it will then run the code. You can then start and stop the code as you wish.

7-Zip benchmark on Raspberry Pi

The 7-Zip Benchmark command

7zip

Measures speed of the CPU and checks RAM for errors.

You can install 7-Zip from the Raspbian Desktop – this is how:

  • Click on the Raspberry in the top left of your screen:
  • Go down to “Preferences” –> and click on “Add / Remove Software”:
  • When the new window opens, type “p7zip” in the search box and hit enter
  • Click both of the checkboxes for “p7zip” (they should be the last 2 choices)

You can also install 7-Zip from the command line:

sudo apt-get install p7zip

Syntax

b [number_of_iterations] [-mmt{N}] [-md{N}] [-mm={Method}]

There are two tests:

  1. Compressing with LZMA method
  2. Decompressing with LZMA method

The benchmark shows a rating in MIPS (million instructions per second). The rating value is calculated from the measured CPU speed and it is normalized with results of Intel Core 2 CPU with multi-threading option switched off. So if you have Intel Core 2 Duo, rating values must be close to real CPU frequency.

You can change the upper dictionary size to increase memory usage by -md{N} switch. Also, you can change the number of threads by -mmt{N} switch.

The Dict column shows the dictionary size. For example, 21 means 2^21 = 2 MB.

The Usage column shows the percentage of time the processor is working. It’s normalized for a one-thread load. For example, 180% CPU Usage for 2 threads can mean that average CPU usage is about 90% for each thread.

The R / U column shows the rating normalized for 100% of CPU usage. That column shows the performance of one average CPU thread.

Avr shows averages for different dictionary sizes.

Tot shows averages of the compression and decompression ratings.

Compression speed and rating strongly depend on memory (RAM) latency.

Decompression speed and rating strongly depend on the integer performance of the CPU. For example, the Intel Pentium 4 has big branch misprediction penalty (which is an effect of its long pipeline) and pretty slow multiply and shift operations. So, the Pentium 4 has pretty low decompressing ratings.

You can run a CRC calculation benchmark by specifying -mm=crc. That test shows the speed of CRC calculation in MB/s. The first column shows the size of the block. The next column shows the speed of CRC calculation for one thread. The other columns are results for multi-threaded CRC calculation.

With -mm=* switch you can run a complex benchmark. It tests hash calculation methods, compression and encryption codecs of 7-Zip. Note that the tests of LZMA have a big weight in “total” results. And the results are normalized with AMD K8 CPU in a complex benchmark.

Examples:

#Runs the benchmark once - takes about 75 seconds on my
#Raspberry Pi 3B+ so please be patient...
7zr b
#You can run and save the output to a file if you wish
#You will not see it running this time while the benchmark
#is running - again please be patient for about 75 seconds
7zr b > 7zip-basic-benchmark-example.txt
#To view the output later or to share it with others
cat 7zip-basic-benchmark-example.txt
#Runs the benchmark twice and give you an average of the
#2 tests - this takes about 150 seconds for this test
7zr b ; 7zr b
#Runs the complete 7-zip benchmark - please be patient...
#There is more information @ http://www.single-board.com 
7zr b -mm=*
#Runs the benchmark 30 times and gives you an average
#This test takes a very long time on the Raspberry Pi
#Watch my YouTube video to see all the cores working on
#Conky - and I am using SimpleScreenRecorder and 
#Asciinema to record everything your seeing today.
7zr b 30

Asciinema
Asciinema

Click here for a direct link to the Asciicast in a new window.

To learn how to install Asciinema click here.

Here is the Asciicast:

NOTE: first 70 seconds don’t show anything as I was showing how to install 7-Zip through the Raspberry Pi GUI. You can see that in the YouTube video below.

To watch this YouTube video of the whole process in a new window, click here.

SimpleScreenRecorder
Simple Screen Recorder

Otherwise, click on the video below and enjoy!

NOTE 1:

I use several different software programs and hardware at the same time in this video. This is a culmination of hardware and software that I have used in my previous Asciicast, blogs, and videos. If you want to ask me specific questions I am always available via email, just be patient 🙂

NOTE 2:

If you are interested in testing Single Board Computers like I am, you might just want to head over to “Performance Analysis Methodology” and read what is there. It is very interesting and worth the time if you’re serious about accurate results and not just a stack of data.

Conky Desktop Widget for Raspberry Pi

So what is Conky? Conky is a free, light-weight system monitor for X, that displays any kind of information on your desktop and works on Raspberry Pi. It is highly configurable and is able to monitor many system variables including the status of the CPU, memory, swap space, disk storage, temperatures, processes, and much more.

Features:

Conky can display more than 300 built-in objects, including support for:

  • A plethora of OS stats (uname, uptime, CPU usagemem usage, disk usage, “top” like process stats, and network monitoring, just to name a few).
  • Built-in IMAP and POP3 support.
  • Built-in support for many popular music players (MPDXMMS2BMPxAudacious).
  • Can be extended using built-in Lua support, or any of your own scripts and programs (more).
  • Built-in Imlib2 and Cairo bindings for arbitrary drawing with Lua (more).
  • Runs on Linux, FreeBSD, OpenBSD, DragonFlyBSD, NetBSD, Solaris, Haiku OS, and macOS and much much more.

There is a great installation guide over at Nova Spirit Tech. I have copied it for your convenience below.

How to Install Conky:

I am sorry to say that there is no GUI install for Conky so we are going to have it install it from the command line. Open a terminal window and copy and paste the BLUE CODE below:

sudo apt-get install conky -y

Now download the conky configuration file

wget -O /home/pi/.conkyrc https://raw.githubusercontent.com/novaspirit/rpi_conky/master/rpi3_conkyrc

To autostart conky on boot we will need to create 2 files:

1. will be a shell script to delay the boot process of conky.

2. will be the conky desktop files to allow lxdesktop to start the shell script

To create the shell script

sudo nano /usr/bin/conky.sh

Paste this into the conky.sh file

#!/bin/sh
(sleep 4s && conky) &
exit 0

Now create the conky.desktop file for the autostart process

sudo nano /etc/xdg/autostart/conky.desktop

Then paste this into the file

[Desktop Entry]
Name=conky
Type=Application
Exec=sh /usr/bin/conky.sh
Terminal=false
Comment=system monitoring tool.
Categories=Utility;

The last thing to do is to reboot to make sure everything is working. As you can see from the following picture it is!

I have included the video below for your step by step installation. In the above picture, I installed it on my Raspberry Pi 3B+. In the video below I installed it on my Raspberry Pi Zero WH.

New version of Raspbian is now available

New distribution images for the Raspberry Pi operating system, Raspbian, are available. The latest version includes bug fixes, security updates, and new features.

Raspbian-only version.

Raspbian

 

 

NOOBS (Raspbian and More) version.

NOOBS

 

 

I have listed the patch notes below, and BOLDED the ones  found of interest:

  • In startup wizard, assign keyboard to a country as per Debian installer recommendations
  • In startup wizard, add an option to use US keyboard in preference to country-specific option
  • In startup wizard, show IP address on the first page
  • In startup wizard, check for existing wifi network connection and show it if there is one
  • In startup wizard, install language support packages for LibreOffice and other applications
  • In startup wizard, improve operation with keyboard only and no mouse
  • Password change in Raspberry Pi Configuration and startup wizard now works properly if passwords contain shell characters
  • Battery indicator plugin modified to cope with Pi-top hardware monitor crashing
  • Networking plugin hides wifi password characters by default
  • In Scratch 2 GPIO plugin, set pin from a dropdown list rather than free text
  • In Scratch 2 SenseHAT plugin, swap x and y-axis values for the LED array
  • Include the latest Adobe Flash player (31.0.0.108)
  • Include latest RealVNC Server (6.3.1)
  • Include libav-tools
  • Include ssh-import-id
  • Removed Mathematica (made the download image smaller and can be installed later if the user needs it)
  • Merge in the latest third-party code for Bluetooth ALSA interface
  • Add ability to prevent software update changing configuration files, by creating ~/.config/.lock file
  • Various other small bug fixes, tweaks, and changes to text
  • Make dhcpcd work with 3G devices
  • Add hw acceleration to ffmpeg
  • Improved WiFi-BT coexistence parameters
  • Run fake-hwclock before systemd-fsck-root
  • Raspberry Pi PoE HAT support
  • Linux kernel 4.14.71
  • Raspberry Pi firmware 5b49caa17e91d0e64024380119ad739bb201c674

I installed the latest version of NOOBS today on my Raspberry Pi Zero WH with fantastic results. I will be installing everything on it like I have shown you in past videos and posts and expect one here very soon.

gPodder works well on Raspberry Pi

If you listen to podcasts like I do or watch regular screencasts or would like to do so in the future you should consider using gPodder. gPodder is a simple, open source podcast client written in Python using GTK+. In development since 2005 with a proven, mature codebase.

While I relax and even sometimes when I work I want to listen to something in the background. The music fits my needs most times and when it doesn’t there are Podcasts to occupy my mind and imagination. It works on Windows, macOS, Linux and even on the Raspberry Pi, quite nicely in fact.

You can create an account at http://www.gPodder.net, it’s free, and you can sync what you listen to across all of your devices. I have my work Windows system, my at home Linux Mint system and my Raspberry Pi 3B+ all synced together and running gPodder. It’s easy to install and you can watch the video below for step by step of the setup process so you can do the same. Before you know it you will also be listening to things that could capture your imagination. So let’s get started!

NOTE:
Today’s video was recorded with the RESPEAKER and SimpleScreenRecorder.

Final Thoughts:

As you can see this works very well on the Raspberry Pi. I use this software daily on many different platforms and the fact that it works so well makes it easy to recommend to all of you.

So that’s it for another blog & video. I hope you have enjoyed what your have seen. If you’re watching this video on YouTube please press the like button, if you haven’t subscribed please subscribe, and I hope to talk to you again very soon!

VYM – View Your Mind on Raspberry Pi

 

VYM (View Your Mind) is a tool to generate and manipulate maps which show your thoughts. In this video, I will show you how to install and use this incredible program on your Raspberry Pi.

I have included the completed map that I created in the YouTube video below so you can see the value of creating these kinds of Mind Maps.

Final Thoughts:

I have been drawing mind maps most of my life. Some people call it “Theory Crafting”, some people call it “Story Boarding” and they are all related somewhat in concept and design. What makes VYM, View Your Mind so useful is that it is FREE, it works very well on Raspbian and the Raspberry PI and you can share it with others. It is another program that makes the whole Raspberry Pi experience great!

Have fun installing it, using it and taking your ideas and sharing them with others in a whole new way! Catch you next week!