Skip to content

Latest commit

 

History

History
100 lines (67 loc) · 4.4 KB

README.md

File metadata and controls

100 lines (67 loc) · 4.4 KB
Raw

dataclay-class

Welcome! Here you will find some information in order to bootstrap a "dataClay environment" You can use it to familiarize that with dataClay, to explore the different components, to run some demo applications based on the CLASS project, etc.

Preflight check

You need:

Also:

  • A python virtualenv for the previous interpreter is strongly suggested, but not required.

Initializing the dataClay services

A docker-compose.yml is provided to ease the process. You can simply do:

$> cd dockers
$> docker-compose down # stop and clean previous containers
$> docker-compose up

That will download and orchestrate the different images needed. Note that, by default, the dataClay LogicModule will be listening to port 11034.

Do not Ctrl+C the process. If you want the docker-compose to be in the background, add the flag -d (detached mode). Otherwise, just leave that opened and proceed into a new terminal. For further options on docker-compose process, read its documentation.

Docker images for ARM

You can find two docker composes to use dataClay in ARM, one will be using Python 2.7.x and the other one Python>=3.5.x Simply do:

$> cd dockers
$> docker-compose -f docker-compose-arm.yml down # stop and clean previous containers
$> docker-compose -f docker-compose-arm.yml up

Executing dataClay CLASS demo examples

Preparing the environment

dataClay CLASS demo needs two nodes so the first step will be to start dataClay in both nodes (see previous section). Also, we must have SSH access configured (without querying password) between nodes in order to execute the demo automatically.

Releasing extended dataClay docker images for CLASS

In order to avoid registering accounts, model, contracts, interfaces in each demo, we release in docker Hub an extension of a dataClay image with already registered models and accounts.

$> cd model
$> ./release.sh --push

The script will release docker images for CLASS named dataclayclass/logicmodule, dataclayclass/dsjava dataclayclass/dspython:py2.7, dataclayclass/dspython:py3.6, dataclayclass/logicmodule:arm, dataclayclass/dsjava:arm dataclayclass/dspython:arm-py2.7 and dspython:arm-py3.6

Make sure you have access to dataclayclass repository before running the script. If you would like to build the images without pushing them to DockerHub just call release.shwithout arguments. Remember that same image should be running in all nodes (with same accounts, contracts and models...)

Running the demo

Once dataClay is running, we execute the run.sh script located in the root directory of the repository.

$node1> run.sh user@remote-node localAddr remoteAddr localDockerFile remoteDockerFile virtualEnv ssl?

where:

  • user@remote-node: Remote node ssh access
  • localIP: local IP address with dataClay logicmodule exposed port. ex 84.88.184.228:11034
  • remoteIP: remote IP addres with dataClay logicmodule exposed port. ex 84.88.184.227:11034
  • localDockerFile: path of docker file to be used in local. Ex: dockers/docker-compose.yml
  • remoteDockerFile: path of docker file to be used in local. Ex: dockers/docker-compose-arm.yml
  • virtualEnv: Python virtual environment with installed dataclay version. REMEMBER that python version and dataclay version should be consistent with docker images being used.
  • ssl?: can be true or false Indicates we want to use secure connections.

This script will use extended dataclay docker images for CLASS named dataclayclass/logicmodule, dataclayclass/dsjava and dataclayclass/dspython and run the application located at app/src

Repository structure

  • app: contains the demo application for CLASS
  • dockers: contains docker-compose files to orchestrate dataClay
  • model: contains docker files to build a specific CLASS docker image with dataClay
  • tools: contains set of tools to register models and get stubs in dataClay
  • security: contains scripts to generate SSL certificates for secure dataClay connections

dataClay documentation

Further information about dataClay (usage, management, help) can be found at https://www.bsc.es/dataclay

Acknowledgements

This work has been supported by the EU H2020 project CLASS, contract #780622.