Innovation Craftsman
This morning I passed the AZ-300 exam. To be honest, I was confident that I failed the exam. Especially because I ran out of time with only 80 – 90 %. In this blog post, I will explain you the good and the bad of this exam and the exam experience.
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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
Go to [Device Templates] and create a new template
For Raspberry Pi, I’ll name this [Raspberry Pi Dev]
So now, I can add a new real device, in the Devices section from the left menu
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
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.
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.
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]
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.
Very cool! Next steps will be to integrate this with some image recognition scenarios.
Happy Coding!
Greetings @ Burlington
El Bruno
References
Ufff! Sí, más de un año desde el último post y, viendo las estadísticas del blog (Claramente) la gente que lo consulta es casi nula. Pero, los que me han seguido sabrán que mudé toda la creación de contenido a mi canal de YouTube: https://youtube.com/user/lacosagorda
En el canal subo un video (Como mínimo) por semana así que todo sigue activo. De cualquier manera, vuelvo por acá y no es que vaya a decir lo que suelo y luego me muerdo a mi mismo porque no puedo cumplirlo. Sí, lo que voy a decir es que iré dejando por acá esas cosas que hago de vez en cuando por puro gusto y que, por algún motivo, no he puesto en videos.
Como ya saben, me gusta explorar más allá de las herramientas que podamos usar. No quiero quedarme con el hecho de poder saber usar tal o…
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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
Happy coding!
Greetings @ Toronto
El Bruno
References
Hi!
… read the tittle.
Los retos (Challenges) han sido una de las gratas sorpresas en la comunidad de Xamarin en este año. No solo hemos tenido varios retos, además han sido recibidos y apoyados de una forma muy positiva.
¿Por qué no tener otro reto en el mes de Octubre?.
De esta forma nos llega de la mano de Claudio Sanchez el reto de Octubre basado en buenas prácticas. Hablamos de buenas prácticas en todos los ámbitos del desarrollo Xamarin: estructura del proyecto, DevOps, Testing, creación de APIs, etc.
Como otros retos anteriores, la idea es llegar a contar con un artículo por cada día del mes. En esta ocasión todo esta centralizado en un repositorio en GitHub. En el repo puedes ver dos aspectos fundamentales:
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Y vuelve el evento técnico Xamarin que estabas esperando, Monkey Conf 2019!.
Monkey Conf 2019
Tras el éxito del evento del año pasado, regresamos con una nueva edición de la Monkey Conf. Volverá a ser un evento gratuito, en el que trataremos temas relacionados con desarrollo móvil, Xamarin, Xamarin.Forms, App Center, testing y más…
Será el próximo Sábado, 30 de Noviembre de 09:30h a 18:30h (GMT+1). Tendremos dos tracks en paralelo con diferentes sesiones técnicas de 50 minutos de duración cada una. Además contaremos con regalos y sorpresas!.
NOTA: Las entradas del evento son gratuitas pero limitadas!
El evento se celebrará en Liferay. Dirección detallada:
Paseo de la Castellana, 280, 28046 Madrid, Madrid
Oficinas de Liferay
¿Has desarrollado una aplicación con Xamarin?, ¿quieres hablar acerca de como usas App Center?, ¿revisar las próximas novedades de Xamarin.Forms?…
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Hi!
So today post is not a post, just a selfish reminder of the steps I do when I setup a new dev machine
Need to be open as Admin in order to install updates
conda update conda
conda update –all
conda create -n tfenv tensorflow
conda activate tfenv
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
pip install ipykernel
ipython kernel install --user --name=tfEnv
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
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.
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
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.
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.
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
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:
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!
Now TensorFlow is installed and it’s time to move forward with a new development environment.
Happy Coding!
Greetings @ Burlington
El Bruno
References
