#Azure – Sending custom Telemetry and Event information from a #RaspberryPi device to #AzureIoT Central

Hi!

Azure IoT Central is one of the amazing services we can use on Azure. I was wondering how easy is to use a Raspberry Pi using Raspbian and Azure IoT and here is my experience.

Let’s assume we had a device up to date using Raspbian, our next step will be to create an Azure IoT Central application. The official step by step is the main resource for this

Create an
Azure IoT Central application (see references)

Once we have our application, we can quickly create a new Raspberry Pi device and use it. However, I’ll do an extra step, lessons learned as a handsome developer

Create a Device Template

Go to [Device Templates] and create a new template

azure iot central create new device template

For Raspberry Pi, I’ll name this [Raspberry Pi Dev]

azure iot central create new device template raspberry pi dev

So now, I can add a new real device, in the Devices section from the left menu

azure iot central raspberry pi dev add new real device

Once you create a new real device, is important to copy and save for later the connection information. To access this, go to the top right [Connect] button

azure iot central raspberry pi dev real device connect information

Almost there, there is an official tutorial that explain how to send random telemetry information with a Python script in a Raspberry Pi. I’ll use it as base for this scenario.

Connect a
Raspberry Pi to your Azure IoT Central application (Python) (see references)

For this demo, I’ll add a custom telemetry property and a custom event to the device. Since I won’t use the device to track temperature, accelerometer, and more, I think it make sense to track some custom information.

So, I’ll go back to my Device Template definition and I’ll add a new Telemetry, named [t1], with the following information.

azure iot central raspberry pi dev new telemetry information

And now, I can run a custom version of my script that will send new telemetry information, for [t1]. Sample in line 18

After a couple of minutes running the sample script, I can see the telemetry information for T1. In this view, I enabled [Temperature] and [T1] to display the timeline.

azure iot central raspberry pi dev real device dashboard telemetry

And, next step will be to add an event, which is also a very important uses case in Azure IoT. Back in the Device Template, I add a new event named [event1]

azure iot central raspberry pi dev new event information

And added some extra lines of code to send also an event between telemetry, Line 22

In the following image, we can see how the events appears in the timeline, and we can also get some extra details clicking on each event.

azure iot central raspberry pi dev real device dashboard telemetry and events

Very cool! Next steps will be to integrate this with some image recognition scenarios.

Happy Coding!

Greetings @ Burlington

El Bruno

References

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#VSCode – How to install #docker in a #RaspberryPi 4

Hi!

In my series of posts on how to create a development environment using a Raspberry Pi4, today is time to write about installing Docker. (see references)

I was user to download and build docker to be used on the device, however now we have an easier way to do this. Thanks to http://get.docker.com we can now install docker with a single command

curl -sSL
https://get.docker.com | sh

And then, a simple check for the docker version

raspberry pi docker version in terminal

Happy coding!

Greetings @ Toronto

El Bruno

References

#Anaconda – My steps to install a virtual environment with #TensorFlow, #Keras and more

Hi!

So today post is not a post, just a selfish reminder of the steps I do when I setup a new dev machine

  • Install Anaconda (see references). I use the default settings, and important: I don’t add Anaconda to Windows PATH.
  • Open Anaconda command prompt as administrator
open anaconda as administrator

Need to be open as Admin in order to install updates

  • Install updates with the command
conda update conda 
conda update –all
  • Create a new development environment named “tfEnv” with tensorflow. Activate the environment
conda create -n tfenv tensorflow 
conda activate tfenv
  • The command to install keras is
pip install
keras

However, if it doesn’t work, I install keras with the following packages

pip install matplotlib 
pip install pillow
pip install tensorflow==1.14
conda install mingw libpython
pip install git+git://github.com/Theano/Theano.git
pip install git+git://github.com/fchollet/keras.git
  • Finally, install Jupyter notebook kernel and create a new kernel for the current virtual environment
pip install ipykernel 
ipython kernel install --user --name=tfEnv
  • There seems to be an issue to install OpenCV using pip with the command
pip install
opencv-python

So, I Install the OpenCV nonofficial package. 1st I download a compatible package from

https://www.lfd.uci.edu/~gohlke/pythonlibs/#pyopencl

Install with

pip install
c:\temp\opencv_python-4.1.1-cp36-cp36m-win_amd64.whl

Happy coding!

Greetings @ Toronto

El Bruno

References

#Anaconda – How to create a custom #Python virtual environment and use it in #Jupyter notebooks (a kernel!)

Hi!

In yesterday post, I created a new virtual environment named [devtf] and in this environment I’ve installed a lot of tools that I need. Then I tried to launch a jupyter notebook from this environment, to use this tools and, of course, it didn’t work.

anaconda start virtual environment and error on launch jupyter notebook

It was time to read and learn how this works. So, when I finally get this I find this amazing article which really explain how this works “Using Virtual Environments in Jupyter Notebook and Python” (see references)

Jupyter Notebook makes sure that the IPython kernel is available, but you have to manually add a kernel with a different version of Python or a virtual environment. First, you need to activate your virtual environment. Next, install ipykernel which provides the IPython kernel for Jupyter. And finally, you can add your virtual environment to Jupyter.

So the commands are

pip install --user ipykernel 
python -m ipykernel install --user --name=devtf

Where “devtf” is the name of the new kernel you want to create. Now, when I launch Jupyter Notebooks, the new kernel is available to be used

jupyter notebook change kernel to one with tensorflow

When I started to use this new kernel (virtual environment) I realized that I didn’t installed TensorFlow. You know, being happy about this, naming the kernel TF but not installing the core component. And, sure, my notebooks didn’t work.

jupyter notebook with kernel without tensorflow

I went to my terminal / command prompt and installed TensorFlow. Then I only need to restart the Kernel, and everything start working. I added a extra couple of lines in my notebook just to check the TensorFlow and keras versions.

jupyter notebook tf ok and test keras version

I find similar errors with another packages, so I pip installed the packages in the terminal and restart the kernel to have the notebook OK. So, my simple reminder for myself about how to do this!

Happy coding!

Greetings @ Mississauga

El Bruno

References

#Python – Can’t install TensorFlow on Anaconda, maybe is the Visual Studio distribution

Hi!

This is the 2nd time I get a weird error when I install TensorFlow in my Anaconda distribution. And this is the 2nd time I realize that I’m using the Anaconda version that is preinstalled with Visual Studio. I’m not sure if the spaces in the path affects the creation of environments or it’s something else, however my current and big and amazing solution is:

  • Uninstall Anaconda
  • Install Anaconda again

And then, follow the simple commands in the official Anaconda and TensorFlow doc (see references)

conda create -n tensorflow_env tensorflow
conda activate tensorflow_env

Once tensorflow is installed, I usually test this in python

> Python 
import tensorflow as tf
print(tf.__version__)

Note: please ignore the typos!

anaconda start python and test anaconda version

Now TensorFlow is installed and it’s time to move forward with a new development environment.

Happy Coding!

Greetings @ Burlington

El Bruno

References

#Event – Resources used during the #GlobalAINight at @MarsDD

Hi!

It was a placer to share some amazing time with the Metro Toronto .Net User Group. Last night was also a special one, we hosted the event at the amazing @MarsDD it was great to have a huge group interested in Artificial Intelligence.

As usual, it’s time to share the resources of the event

Official Resources https://aka.ms/AA60hn1

This includes Workshops like

  • Creating applications that can see, hear, speak or understand – using Microsoft Cognitive Services
  • Learn how to train high accuracy machine learning models using automated machine learning
  • Crash course on building and accelerating deep learning solutions
  • And more.

It also includes a set of materials around Automated Machine Learning (AutoML).

And of course, my materials.

Slides

Source Code: https://github.com/elbruno/events/tree/master/2019%2009%2005%20Global%20AI%20Night

Happy Coding!

Greetings @ Toronto

El Bruno

Resources

Tweets

#VSCode – Let’s do some #Git dev in #RaspberryPi (GitHub and Azure DevOps!)

Buy Me A Coffee

Hi!

In my previous posts I wrote about how to prepare a developer station with a Raspverry Pi 4. I wrote on how to install Visual Studio Code, how to install .Net Core and how to build and run C# projects. Of course, the next step is to work with Git.

The command to install git is

sudo
apt-get install git

However, I already have Git installed. I haven’t checked, but it seems to me that the latest Raspbian distro includes by default git. I was hoping that VSCode will recognize and use this, but in order to work with Git in VSCode I need some extra work in the IDE settings.

raspberry pi git installed but not integrated in visual studio code

I was able to clone some repositories from GitHub and Azure DevOps, directly from bach

raspberry pi 4 git clone azure devops repository

So, let’s fix Visual Studio Code and Git integration. This one is very easy, I just need to go to Settings, search for Git and define the Git path for VSCode.

raspberry pi 4 visual studio code preferences git

In order to find the git path, we need to use the [which] command

pi@rpidev3:~ $ which git

/usr/bin/git

My got location is [/usr/bin/git].

I’m not a command line dude! I like User Interfaces, so now it’s time to open one of the cloned repositories in Visual Studio Code. I can see that VSCode recognices Git and I can start to commit my files.

raspberry pi 4 visual studio code using git

This is also a perfect moment to define Git user name and user email.

git config --global user.email "email@email.com" 
git config --global user.name "your name"

I like to do this in the Terminal in VSCode, just to check all is working fine.

Happy Coding!

Greetings @ Burlington

El Bruno

References

#Event – Great news for #GlobalAINight: new venue and final Agenda for Toronto

Hi !

The Global AI Night is a free evening event organized by 93 communities all over the world that are passionate about Artificial Intelligence on the Microsoft Azure. During this AI Night you will get inspired through sessions and get your hands dirty during the workshops. By the end of the night you will be able to infuse AI into your applications.

Please check the register link to find the NEW AMAZING VENUE we had: https://www.eventbrite.ca/e/global-ai-night-tickets-70625519831

And this is the final agenda

KeyNote

Creating applications that can see, hear, speak, understand and even read !

using Microsoft Cognitive Services. In this workshop you will be introduced to the Microsoft Azure Cognitive Services, a range of offerings you can use to infuse intelligence and machine learning into your applications without needing to build the code from scratch. We will cover pre-trained AI APIs, such as computer vision and text analytics, that are accessed by REST protocol. We will also feature one of the most recent and powerful Services: Form Recognizer. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to documents. Wrapping the workshop up by building our custom trained AI into an application

Introduction to ML.Net and AutoML

ML.Net is an open-source and cross-platform machine learning framework for .NET developers. Using ML.Net , developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more. AutoML (Automated Machine Learning) is a collection of new technologies from Microsoft to enhance the data science development process.
In this workshop you will be introduced to the core features and scenarios of ML.Net and also to the preview feature of Model Builder for AutoML.

Happy coding!

Greetings @ Toronto

El Bruno

#VSCode – Build and Run C# #DotNetCore projects in #RaspberryPi

Hi!

Time to move on with some lessons learned using Visual Studio Code in the Raspberry Pi 4.

One of the first issues you may find working with VSCode in the device is related to file write permissions when you are saving a file.

I raspberry pi 4 visual studio code failed to save file

So, it was a good opportunity for me to learn about files and folder permissions in Linux. I found a great starting article “How to Manage File and Folder Permissions in Linux” (see references), and it allowed me to fix this issue.

My next error was triggered when I try to build my project. Again, it was a permission related error.

/home/pi/dotnet/sdk/2.2.401/Microsoft.Common.CurrentVersion.targets(4195,5):
error MSB3021: Unable to copy file "obj/Debug/netcoreapp2.2/dotnethelloworld.dll"
to "bin/Debug/netcoreapp2.2/dotnethelloworld.dll". Access to the path
is denied. [/home/pi/dotnethelloworld/dotnethelloworld.csproj]

However, this time the fix was not related to file and folder permissions.

One of the solutions I found, was to run VSCode with admin privileges. This is probably one of the worst ideas ever, and you can find tons of articles explaining why this is bad (see references).

Anyways, I decided to give it a try. Of course, it worked. Let me share how.

I run VSCode with the following command, which runs the app in root mode.

sudo
code-oss --user-data-dir=/home/pi/dotnethelloworld
raspberry pi 4 visual studio code run as root

The VSCode team is aware of this, so you will find a warning about this scenario

raspberry pi 4 visual studio code run as root warning

Even so, you can still use VSCode to edit and build C# .Net Core Projects.

raspberry pi 4 visual studio code build code

And you can run them also

raspberry pi 4 visual studio code build and run edited code

So far, so good. Or maybe not, broken a lot of good practices. I’ll see this as an amazing chance to learn and test new stuff!

Happy coding.

Greetings @ Burlington

El Bruno

References