Adding a ks0212 relay board to the mqtt universe

Weatherstation with raspi
Weatherstation with raspi

Adding the 4 channel relay board ks0212 to the MQTT universe

We just hacked a trotec dehumidifier for Herwigs Observatory. The idea was to additionally activate the dehumidifier when the difference between outside and inside humidity is above 10%. Normally there is a fan taking care of it but sometimes the differents gets to high. As there is already a raspberry pi running in the observatory for the weatherstation and the flightradar24 installation we just added the 4 channel relay board ks0212 from keyestudio. Not touching the 220V part we directly used the relay to “press” the TTL switch on the board for 0.5 seconds to turn on and off the dehumidifier. Here are the code snipped we used for this. The control is completely handled via MQTT.

Installing necessary programs and libraries

For the sake of simplicity we used python and the GPIO library wiringpi. Therefore we first install the python development parts and them the python libraries for wiringpi and MQTT. As this is a dedicated hardware installation we don’t use virtualenv and directly install the library as root system wide.

The python program

Again, a very simple python script, basically attaching to a (you need to change the code, there is no config) mqtt server and subscribes itself to a certain topic. Then it waits for messages and cuts off the last part of the topic to identify the relay. The naming convention is based on the relay name printed on the ks0212 pcb. As payload you can send “on“, “off” and “press“. “press” switches the relay on for half a second in order to simulate a button press as we need it for our dehumidifier.

Adding a systemd service

In order to keep the wantabe daemon up and running and also start it automatically at system start we add this service configuration file in “/lib/systemd/system/relayboard.service“:

Activating the service

The following lines activate the service:

Checking the status can be done with:

ks0212 Pinout

If you want to do some hacking with the ks0212 relay board on your own here is the pin mapping table. I used the very cool side https://pinout.xyz/pinout/wiringpi for getting the numbers:

Relay WiringPi BCM GPIO Link
J2 7 4 7 https://pinout.xyz/pinout/pin7_gpio4
J3 3 22 15 https://pinout.xyz/pinout/pin15_gpio22
J4 22 6 31 https://pinout.xyz/pinout/pin31_gpio6
J5 25 26 37 https://pinout.xyz/pinout/pin37_gpio26

 

 

Workload container for autoscaling test with kubernetes

Workload
Workload

The Idea

Every now and then you want to test your installation, your server or your setup. Specially when you want to test auto scaling functionalities. Kubernetes has an out of the box auto scaler and the official descriptions recommends a test docker container for testing with a apache and php installation. This is really great for testing a web application where you have some workload for a relatively short time frame. But I would also like to test a scenario where the workload runs for a longer time in the kubernetes setup and generates way more cpu workload then a web application. Therefore I hacked a nice docker container based on a c program load generator.

The docker container

The docker container is basically a very very simple Flask server with only one entry point “/”. The workload itself can be configured via two parameters:

  • percentage How much cpu load will be generated
  • seconds How long will the workload be active

The docker container itself uses nearly no CPU cycles as Flask is the only python process being active and waits for calls to start using CPU cycles.

lookbusy

I use a very nice open source tool called lookbusy from Devin Carraway which consumes memory and cpu cycles based on command line parameters. Unfortunately the program has no parameter to configure the time span it shout run. Therefore I call it the unix command timeout to terminate its execution after the given amount of seconds.

The Flask python wrapper

The only program is a python Flask one, very short and only takes the get call to its root folder, checks for the two parameters and starts a thread with the subprocess. The get call immediately returns as it also supports long run workload simulations.

The Dockerfile

The docker container is based on python latest (at this time 3.6.4). I put all the curl, make, install and rm calls into a single line in order to have a minimal footprint for the docker layer as we do not need the source code any more. As Flask is the only requirements I also call it directly without the requirements.txt file. The “-u” parameter for the python call is necessary to prevent python from buffering the output. Otherwise it can be quite disturbing when trying to read the debug log file.

Building and pushing the docker container

Building and pushing it to hub.docker.com is straightforward and nothing special.

Testing it on a kubernetes cluster

I have chosen the IBM cloud to test my docker container.

Requesting a kubernetes cluster

Requesting a kubernetes cluster can be done after login with

This command uses the bluemix CLI with the cluster plugin to control and configure kubernetes on the IBM infrastructure. The parameters are

  • –name to give your cluster a name (will be very important later on)
  • –location which datacenter to use (in this case dallas). Use “bx cs locations” to get your possible locations for the chosen region
  • –workers how many worker nodes are requested
  • –kube-version which kubernetes version should be used. Use “bx cs kube-versions” to get the available versions. “(default)” is not part of the parameter call.
  • –private-vlan which vlan for the private network should be used. Use “bx cs vlans <location>” to get the available public and private vlans
  • –public-vlan see private vlan
  • –machine-type which kind of underlying configuration you want to use for your worker node. Use “bx cs machine-types <location>” to get the available machine types. The first number after the “.” is the amount of cores and one after “x” the the amount of RAM in GB.

This command takes some time (~1h) to generate the kubernetes cluster. BTW my bluemix cli docker container has all necessary tools and also a nice script called “start_cluster.sh” to query all parameters and start a new cluster. After the cluster is up and running we can get the kubernetes configuration with

Starting a pod and replica set

We start the pod and replica set without a yaml file because the request is very straight forward. Important here is the parameter “–requests“. Without it the autoscaler can not measure the cpu load and it never triggers.

Exposing the http port

Again because the call is so simple we directly call kubectl without a yaml file to expose the Port 80. We can check for the public IP with

In case the cloud runs out of public IP addresses and the “EXTERNAL_IP” is still pending after several minutes we can use one of the workers public ip addresses and the dynamic assigned port. The port is visible with “kubectl get svc” at the “PORTS” section. The syntax is as always in docker internalport:externalport. The workers public IP can be checked with

So instead of calling our service with a official public ip address on port 80 we can use

Autoscaler

Kubernetes has a build in horizontal autoscaler which can be started with

In this case it measures the cpu load and starts new pods when the load is over 50%. The autoscaler in this configuration never starts more than 10 and never less than 2 pods. The current measurements and parameters can be checked with

So right now the cpu load is 0 and only one replica is running.

Loadtest

Time to get call our container and start the load test. Depending on the URL we an use curl to start the test with

and check the result after some time with

As we see the load increases and autoscaler kicks in. More details can obtained with the “kubectl proxy” command.

Deleting the kubernetes cluster

To clean up we could either delete all pods and replica sets and services but we could also delete the complete cluster with

 

Setting up SDRplay remote on a raspberry pi

SDRplay
SDRplay

SDRPlay

I recently bought myself a SDRPlay receiver to play with this technology and maybe build a ground station or meteor scatter detector. The original plan is to setup a receiver on the Motionlab roof with an raspberry pi and send the IQ data via network down to a local server and extract the interesting information. One great software to work remotely with an SDR receiver is the Soapy project.

Install the raspberry pi part

Build system

Install the latest raspberry pi lite version from raspberrypi.org

Core system

The soapy part consist of 3 parts. The Core system must be installed first.

SDRplay

The SDRplay part consist of two parts one are the proprietary binary libraries from SDRplay itself the the other part is the soapy wrapper for SDRplay.

Binary Libraries

The driver can be downloaded from the SDRplay homepage https://www.sdrplay.com/rpi2dl.php

The SDRplay Soapy wrapper

Test the Soapy access

Soapy Server for Remote Access

Run the server

Accessing IBM Object Store from Python

IBM Object Store

SWIFT Object StoreIBM offers a S3 compatible Object Store as a file storage. Beside S3 the storage can also be accessed via the SWIFT protocol by selecting a different deploy model. As the cost for this storage is extremely low compared to Database storage it is perfect for storing sensor data or other kind of data for machine learning.

I use the storage for example to host my training data or trained model for Tensorflow. Access and payment for the Object Store is managed via IBM Cloud aka Bluemix. And as this offering is included in the Lite offering the first 25GB are for free. 🙂

As there is a problem getting the S3 credentials right now I use the SWIFT access model. Please make sure when you request the Object Store service to access the SWIFT version to select the right access model.

Python libs

As the SWIFT protocol is part of openstack, the python access client can be found at https://docs.openstack.org/python-swiftclient. Depending on the security access model you also need the openstack Identity API (Keystone). Both libs are on github (swiftclient and keystone) and also available via pip.

Access storage

Inside the IBM Cloud web interface you can create or read existing credentials. If your program runs on IBM Cloud (Cloudfoundry or Kubernetes) the credentials are also available via the VCAP environment variable. In both cases they look like mine here:

Important informations are the projectId, region, userId and password. The access with keystone the swift python client looks like this:

Important is the version information, also as part of the authurl.

Accessing data

Objects can be read and written, containers (aka buckets) can we read and modified as described in the documentation. For example:

 

 

Bash script for automatic picture enhancement and upload to Watson Visual Recognition classifier

Visual Recognition
Visual Recognition Tool

pushVisualRecognition.sh

I hacked a nice script for the Watson Visual Recognition service. There is already a very helpful page available here but many people (including me) like command line tools or scripts to automate processes. The script does the following processes to each picture:

  1. Resize to max 500×500 pixel. Watson internally use only ±250 pixels, so this saves a lot of upload time.
  2. Enhance the image (normalisation) for better results.
  3. Autorotate the images based on the EXIF data from your camera. Watson ignores EXIF data.

The tool expects this directory structure and reads all necessary information from it:

  • Classifiername
    • Classname
      • <more then 10 files>.jpg

The Visual Recognition key is read from the “VISUAL_KEY” environment variable.

How to install it

The pushVisualRecognition.sh script is part of the bluemixcli docker container as described here. It basically only needs imagemagick and zip installed so you can also run it without the docker container and download the script directly from github with this link. If you want to run it with docker the command is

How to run it

Simply call pushVisualRecognition.sh in your directory, all necessary information will be retrieved from the directory structure and the environment variable.

Example

Create a directory structure like this one:

Calling pushVisualRecognition.sh will result in: