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 for getting the numbers:

Relay WiringPi BCM GPIO Link
J2 7 4 7
J3 3 22 15
J4 22 6 31
J5 25 26 37



Workload container for autoscaling test with kubernetes


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.


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 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 “” 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


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.


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


Execute the Radio Meteor Observations program on mac os

MeteorLogger Screenshot
MeteorLogger Screenshot

What is it about

Wolfgang Kaufmann wrote an impressive article and even a more impressive software for the hobby radio astronomers. I highly recommend checking out the article and play with the software as there are not so many radio astronomers among the community. His software is written in python with a very clean UI. It directly connects via the computer sound card and grabs the audio signal. I shortly describe here what to install on a Mac OS to get his software up and running.

Where to get it

The software can be downloaded at Unfortunately it is not available on any online repo like github but the source code can be downloaded as a zip file.


The PyAudio package needs some libraries and direct access to the os sound system. Therefore we need it install this audio package outside of python itself

The necessary python libs can be installed via pip. I recommend doing it in a virtual environment

That is all. After installing the python libs the program starts right away


Setting up SDRplay remote on a raspberry pi



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

Core system

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


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

The SDRplay Soapy wrapper

Test the Soapy access

Soapy Server for Remote Access

Run the server

Image Recognition with Tensorflow classification on OpenWhisk

The big picture

Image classificationAs described in a previous article we (Niklas and I) are going to use Tensorflow to classify images into pre-trained categories. The previous artikel was about  on how to train a model with Tensorflow on Kubernetes. This article here now describes how to use the pre trained model which is stored on Object Storage. Similar to the training we will also use docker to host our program but this time we will use OpenWhisk as a platform.

Like the first part I also use the Google training Tensorflow for Poets. This time not the code itself but I copied the important classification parts from their script into my python file.

OpenWhisk with Docker

OpenWhisk is the open source implementation of an so called serverless computing platform. It is hosted by apache and maintained by many companies. IBM offers OpenWhisk on their IBM cloud and for testing and even playing around with it it the use is for free. Beside python and javascript OpenWhisk also offers the possibility to run docker containers. Internally all python and javascript code is executed anyhow on docker containers. So we will use the same official Tensorflow docker container we used to build our training docker container.

Internally OpenWhisk has three stages for docker containers. When we register a new method the execution instruction is only stored in a database and as soon as the first call approaches OpenWhisk the docker container is pulled from the repository, then initialised by an REST call to ‘\init‘ and then executed by calling the REST interface ‘\run‘. The docker container keeps active and each time the method is called only the ‘\run‘ part is executed. After some time of inactivity the container is destroyed and needs to be called with ‘\init‘ again. After even more time of inactivity even the image is removed and need to be pulled again.

The setup

The code itself is stored on github. Let’s have a look first on how we build the Docker container:


As you can see this Docker is now really simple. It basically installs the python requirements to access the SWIFT Object Store and starts the python program. The python program keeps running until the OpenWhisk system decides the stop the container.

We make heavy use of the idea of having a init and a run part in the execute code. So the python program has two main parts. The first on is init and the second run. Let’ have a look the init part first which is basically setting up the stage for the classification itself.


Unfortunately it is not so easy to configure the init part in a dynamic way with parameters from outside. So for this demo we need to build the Object Store credentials in our source code. Doesn’t feel right but for a demo it is ok. In a later article I will describe how to change the flow and inject  the parameters in a dynamic way. So what are we doing here?

  1. 10-16 is setting up a connection to the Object Store as described here.
  2. 18-22 is reading the pre trained Tensorflow graph directly into memory. tf is a global variable
  3. 24-26 is reading the labels which are basically a string of names separated by line breaks. The labels are in the same order as the categories in the graph

By doing all this in the init part we only need to do it once and the run part can concentrate on classifying the images without doing any time consuming loading any more.

Tensorflow image manipulation and classification

How to get the image

The image is transferred base64 encoded as part of the Line 24-25 request. Part of the dictionary is the key payload. I choose this because Node-red is using the same name for some kind of most important key. Tensorflow has a function to consume base64 encoded data as well but I could not get it to run with the image encoding I use. So I took the little extra step here and write the image on file and read it back later. By directly consuming it I think we could same some milliseconds processing time.

Transfer the image

  • Line 27 reads the image back from file
  • Line 29 decode the jpeg into an internal representation format
  • Line 30 cast the values to an float32 array
  • Line 31 adds a new dimension on the beginning of the array
  • Line 32 resizes the image to 224, 244 to have a similar size with the training data
  • Line 33 normalize the image values

Classify the image

  • Line 34-35 gets the input and output layer and stores it in the variables
  • Line 36 loads the image into Tensorflow
  • Line 39 here is the magic happening. Tensorflow processes the CNN with the input and output layer connected and consumes the Tensorflow image. Furthermore numpy is squeezing out all array nesting to a single array.
  • Line 40 has an array with probabilities for each category.

Mapp the result to labels

The missing last step is now to map the label names to the results which is be done in line 43 and 44.

Build and deploy it in OpenWhisk

The docker container can be build with

and pushed with

Run it in OpenWhisk

After configuring the command line tool wsk the action itself can be created with

For testing we need an image base64 encoded as file on our local hard disk. Then we can invoke the call with

The first execution will take up to 15 seconds because the docker container will be pulled from docker hub and the graph will be loaded from the Object Store. Calls later should be around 150 milliseconds processing time. The parameter –result will force OpenWhisk to wait for the function to end and also show you the result on your command line.

If you want to get the log file and also an exact execution time try this command:

  • First call results in  “duration”: 3805. Your call itself took way longer in the first call because 3805 is only the execution of the docker container (including init) not the time it tooks OpenWhisk to pull the docker container from docker hub.
  • Second call results in  “duration”: 156.

Build a web UI

Well UI is nothing I can talk about. But have a look at Niklas blog post on how to build a web UI. An test installation can be found here:

Image Recognition with Tensorflow training on Kubernetes

The big picture

Modern Visual Recognition is done with deep neural networks (DNN). One framework (and I would say the most famous one) to build this kind of network is Tensorflow from Google. Being open source and specially awesome it is perfect to play around and build your own Visual Recognition System. As the compute power and specially the RAM memory raises there is now a chance of having much more complicated networks compared to the 90th where there where only one or two hidden layer.

One architecture is the Convolutional Neural Network (CNN). The idea is very close to brain structure. The basic idea is to intensively train a network on gazillions of images and let it learn features inside the many hidden layers. Only the last layer connects features to real categories. Similar to our brain the networks learns concepts and patterns but not really the picture groups.

After spending a lot of compute power to train these networks they can be easily reused to train new images by replacing only the last layer with a new one representing the to be trained categories. Training this network is only training the last connection between the last layer and the rest of the network. This training is extremely fast (only minutes) compared to month for the complete network. The charming effect is to train only the “mapping” from features to categories. This is what we are going now.

Basically the development of such a system can be divided into two parts. The first part (training) is described there. For the “use” aka classification have a look into the second part on my blog. I developed this system together with a good friend and colleague of mine. Check out Niklas Heidloff, here is his blog and twitter account. The described system has mainly three parts. Two docker containers described in this blog and one epic frontend described in Niklas blog. The source code can be found on github.


If you want to train a neural network (supervised learning) you need a lot of images in categories. Not ten or hundred but better hundred thousands or even 15 million pictures. A wonderful source for this is Imagenet.  >14 million pictures organized in >20k categories. So a perfect source to train this kind of network. Google has done the same and participated in the Large Scale Visual Recognition Challenge (ILSVRC). Not only Google but many other research institutes build networks on top of Tensorflow in order have a better image recognition. The outcome are pre-trained models which can be used for system like we want to build.

Tensorflow for poets

Like always it is best to stand on shoulders of giants. So in our case use the python code developed by google at the codelabs. In this very fascinating and content full online training on Tensorflow Google developed python code to retrain the CNN and also to use the new trained model to classify images. Well, actually the training part is just using the original code and wraps it into a docker container and connects this container to an Object Store. So no much new work there but a nice and handy way to use this code for an own project. I highly recommend taking the 15 minutes and take the online training to learn how to use Tensorflow and Python.

MobileNet vs. Inception

As discussed there are many trained networks available the most famous ones are Inception and MobileNet. Inception has a much higher classification rate but also needs more compute power. Both on training and on classification. While we use kubernetes on “the cloud” the training is not a big problem. But we wanted to use the classifier later on on OpenWhisk we need to take care of the RAM memory usage. (512MB). The docker container can we configured to train each model but for OpenWhisk we are limited to the MobileNet.

Build your own classifier

Visual Recognition ArchitectureAs you can see in the picture we need to build two containers. The left one is loading the training images and the categories from an Object Store, trains the neural network and uploads the trained net back to the Object Store. This container can run on your laptop or somewhere in “the cloud”. As I developed a new passion for Kubernetes I added a small minimal yaml file to start the docker container on a Kubernetes Cluster. Well not really with multiple instances as the python code only uses one container but see it as some kind of “offloading” the workload.

The second container (will be described in the next article)  runs on OpenWhisk and uses the pre-trained network downloaded from the Object Store.

Use docker / kubernetes to train your model

We use the official Tensorflow docker container with python support as published from Google and the training script from Tensorflow for poets.


The Dockerfile is straightforward. We use the Tensorflow docker image as base and install the git and zip (unpacking the training data) packages. Then we install all necessary python requirements. As all the Tensorflow related packages for Python are already installed these packages are only for accessing the Object Store (see my blog article). Then we clone the official github tensorflow-for-poets repository, add our execution shell script and finish with the CMD to call this script.

Execution Script

All important and sensitive parameters are configured via environment variables introduced by the docker container call. The basic and always the same parameters are set here. Where to do the keystone authentication and which protocol version for the Object Store. The swift commands downloads a zip file containing all training images in subfolders for each category. So you need to build a folder structure like this one:

The execution script unpacks the training data and calls the retrain script from Tensorflow-for-poets. Important parameters are how_many_training_steps (can be reduced to speed up for testing) and the architecture. As the last parameter can be changed depending on how accurate the classifier has to be and also how much memory is available for the classifier this parameter is also transferred via a command line parameter.

The image can be build with:

and pushed with:


After building the docker container and pushing it to docker hub this yaml file triggers Kubernetes to run the container with the given parameters, many taken from your Object Store credential file:

  • OS_USER_ID  -> VCAP[‘userId’]
  • OS_PASSWORD -> VCAP[‘password’]
  • OS_PROJECT_ID -> VCAP[‘projectId’]
  • OS_REGION_NAME -> VCAP[‘region’]
  • OS_BUCKET_NAME -> Up to you however you called it
  • OS_FILE_NAME -> Up to you, however you called it
  • TF_MODEL -> ‘mobilenet_0.50_{imagesize}’ or ‘inception_v3’

Use Object Store to store your trained class for later use

We decided to use Object Store to store our training data and also the re-trained network. This can be any other place as well, for example S3 on AWS or your local HDD. Just change the Dockerfile and exec file to download and upload your data correspondingly. More details on how to use the Object Store can be found in my blog article.


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 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:



Dev-Ops with OtA update for ESP8266

Over the Air update (Ota) for ESP8266


Thanks to the esp8266 project on github there is a very convenient way how an ESP can be updated over the air. There are three different ways available.

  1. The first one is via the arduino IDE itself where the esp opens a port and is available for firmware upload just like with a serial connection. Very convenient if you are in the same network.
  2. The second one is via http upload. So the esp provides a web server to upload the bin file. In this case there is no need to be in the same network but it is still a push and for each installed esp individual necessary.
  3. The third and most convenient way for a bigger installation base or in case the devices are behind a firewall (as they always should be) and no remote access is possible. In this case the device can download the firmware itself via http(s) download from a web server somewhere in the internet.

For a complete dev-ops pipeline from pushing to a repository to flashing a device the third scenario it the easiest one. So we need a place to store the binary files. For convenience I use amazon s3 to host my binary files as travis easily supports s3 upload. But it can be every internet platform where files can be stored and downloaded via http(s). The necessary code on arduino side looks like this:

This arduino function can be called from time to time (at startup or on constant running systems every now and then) to check for a new firmware version and in case there is a new version available automatic flash it and restart.

  • Line 1 is a #define with a placeholder for the current version of the installed firmware. This placeholder is replaced in the build pipeline at travis with an increasing number. So the compiled code has something like 23 or 42 instead of REPLACE_WITH_CURRENT_VERSION.
  • Line 2 is the URL for a latest version of a firmware.
  • Line 3 is the URL to a file with only one line with the latest build number in it.
  • Line 7-9 loads the version file from s3.
  • Line 12-13 converts the file into a number which can be compared with the define from line 1.
  • Line 17 is the firmware update itself. A detailed description of the ESPhttpUpdate class can be found here.

There are two ways to check if there is a new version available and only flash if there is something new. The one we use here is to have an own mechanism for it. I do it because on s3 I can only host static files and therefore I place the latest build number in a static file next to the firmware itself. The other way is build in into ESPhttpUpdate. The update function can be called with a build number which will be compared on the server and the return code will reflect if there is a new version or not. In this case we would need a script on the server to check for it.

Get an increasing build version number

With a little bash script we could load the last build number from s3 and then increase it in order to have the current number for our build.

This script loads the version file (line 3), increases the number (line 4) and patches our source code file (line 11) with this number instead of REPLACE_WITH_CURRENT_VERSION. After running this script the current source code contains the latest number and also the upload folder for s3 has a new file with the newest number in order to inform the polling ESPs.

Travis config file

Travis-ci is incredible easy to use and very reliable for continuous integration. In combination with platformio it is very easy to compile arduino code for several types of hardware. Simply configure the hardware in the platformio.ini file:

In this case we use the esp8266 feather board aka Huzzah. Just set the framework to your kind of esp.

Travis itself is configured by the .travis file in the root directory of your repository on github:

  • Line 1: Platformio is based on python so the build environment (although the code is c++) is python for maintaining platformio.
  • Line 3: Right now platformio is only available for python 2.7 so this line gets the latest stable version of python 2.7.
  • Line 5-7: Gets the latest cache files from the last build in order to save compile time and reduce the costs for travis. As this service is for free for open source projects it is always nice to save some money for the cool guys.
  • Line 10: Installs the latest version of platformio itself.
  • Line 13: Creates the upload directory which we will upload to s3 later on.
  • Line 14: Calls the build number increase and patch script.
  • Line 15-16: Patches the wireless lan config in case it is not handled inside the arduino code itself.
  • Line 17: Calls platformio to download all libraries and compile the arduino code itself.
  • Line 18: Platformio generates a lot of files for the linker and several other files. We only need the bin file later on, so we copy it here to the upload folder.
  • Line 20: Travis has a build in functionality to upload files after compilation. This is the part where we upload the files to s3.
  • Line 22: Defines the s3 bucket to upload the files.
  • Line 23-26: Provides the encrypted s3 credentials. See travis documentation on how to create these lines.
  • Line 29: Defines the local folder to be uploaded. Otherwise travis will upload everything from the current run.
  • Line 30: Defines the s3 folder in the bucket where the files will be stored.

With this files in place travis monitors your github repository and creates / uploads new firmware versions each time you push changes to your repository. The arduino code checks for new versions and patches itselfs as soon as there is a new version available. A complete project can be found here in my github repository.


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

Visual Recognition
Visual Recognition Tool

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


Create a directory structure like this one:

Calling will result in:

Docker container with Bluemix CLI tools

BluemixCLI on docker hub
BluemixCLI on docker hub

Being an developer advocate means to play always with the latest version of tools and being on the edge. But installed programs are getting out of date and so I always end up with having installed old versions of CLI tools. One reason why I love cloud (aka other people’s computers) computing so much is because I don’t need to update the software, it is done by professionals. In order to have always the latest version of my Bluemix CLI tools in hand and being authenticated I compiled a little docker container with my favourite command line tools. cf, bx, docker and wsk.

Getting the docker container

I published the docker container on the official docker hub. So getting it is very easy when the docker tools are installed. This command will download the latest version of the container and therefore the latest version of installed cli tools. We need to run this command from time to time to make sure the latest version is available on our computer.

Get the necessary parameters

For all command line tools we need username, passwords and IDs. Obviously we can not hardcode them into the docker container therefore we need to pass them along as command line parameters when starting the docker container.

  • Username (the same as we use to login to Bluemix)
  • Password (the same as we use to login to Bluemix)
  • Org (The Organisation we want to work in, must already be existing)
  • Space (The Space we want to work in, must already be created)
  • AccountID (This can we catched from the URL when we open “Manage Organisation and click on the account)
  • OpenwhiskID (Individual for org and space, get be catched here:

Run the container

The container can be started with docker run and passing all parameters with -e in:

Line 8 mounts the local directory inside the docker container under /root/host. So we can fire up the container and have a bash with the latest tools and our source code available.

Use the tools

Before we can use the tools we need to configure them and authenticate against Bluemix. The script “” which is located in “/root/” (our working directory) takes care of all logins and authentications.


The Cloudfoundry command line tool for starting, stopping apps and connecting services.


The Bluemix version of the Cloudfoundry command line tool. Including the plugin for container maintenance. By initializing this plugin we also get the credentials and settings for the docker client to use Bluemix as a docker daemon.


The normal docker client with Bluemix as daemon configured.


The OpenWhisk client already authenticated.

We can configure an alias in our .bashrc so by just typing “bxdev” we will have bash with the latest cli tools available.