Merge pull request #3 from mindsphere/docker

chore: docker

Author: sn0wcat

.dockerignore

@@ -0,0 +1,2 @@
+temp/*.json
+.git

.markdownlint.json

@@ -0,0 +1,9 @@
+{
+    "MD007": false,
+    "MD033": false,
+    "MD013": false,
+    "MD041": false,
+    "MD024": false,
+    "MD025": false,
+    "MD030": false
+}

Dockerfile

@@ -0,0 +1,11 @@
+FROM jupyter/scipy-notebook
+USER root
+RUN apt-get update
+RUN apt-get install -y supervisor
+USER jovyan
+RUN mkdir /home/jovyan/work/temp
+COPY --chown=jovyan:users . /home/jovyan/work
+RUN mv /home/jovyan/work/service.conf /home/jovyan
+RUN jupyter trust work/*.ipynb
+RUN npm install -g @mindconnect/mindconnect-nodejs
+CMD ["supervisord","-c","/home/jovyan/service.conf"]

README.md

@@ -1,3 +1,5 @@
+<!-- @format -->
+
 # MindSphere Analytics Examples
 
 <!-- markdownlint-disable MD033 -->
@@ -12,6 +14,7 @@ Examples how to use the mindSphere analytics APIs.
 
 ## Jupyter Notebooks demonstrating the use of the MindSphere Analytics APIs
 
+
 ### Trend Prediction API
 
 The Trend Prediction API predicts future values for time series using linear and nonlinear regression models. It is a forecasting framework, that has many useful applications in the area of Process & Condition Monitoring.
@@ -30,6 +33,46 @@ Spectrum Analysis API allows users to perform time domain and frequency domain a
 
 Example: [Spectrum Analysis API](spectrum-analysis.ipynb)
 
+## Running the notebook
+
+The easiest way to run the notebook is to use `mindspheredemos/analytics-examples` docker container.
+
+You can either build the container locally
+
+```bash
+docker build .
+```
+
+or you can pull the container from docker hub.
+
+```bash
+docker pull mindspheredemos/analytics-examples
+```
+
+You can run the container using following command:
+
+```bash
+docker run -it -p:8888:8888 -p:4994:4994 --name examples mindspheredemos/analytics-examples
+```
+
+The container will offer two endpoints:
+
+-   MindSphere CLI [http://localhost:4994](http://localhost:4994)
+-   Jupyter Lab [http://localhost:8888](http://localhost:8888)
+
+Please configure the CLI with app credentials [as described here](https://opensource.mindsphere.io/docs/mindconnect-nodejs/cli/setting-up-the-cli.html)
+
+After that you can start using the jupyter lab with the notebooks. Just copy the token from the container output. The notebooks can be found in `work` folder.
+
+![jupyter lab](images/notebook.png)
+
+(If you have started the container in the background you can get the token by running `docker logs examples` command.)
+
+## Docker Base Image
+
+The docker image is based on [jupyter/scipy-notebook docker image](https://hub.docker.com/r/jupyter/scipy-notebook/).
+
+
 ## Siemens API Notice
 
 This project has been released under an [Open Source license](./LICENSE.md). The release may include and/or use APIs to Siemens’ or third parties’ products or services. In no event shall the project’s Open Source license grant any rights in or to these APIs, products or services that would alter, expand, be inconsistent with, or supersede any terms of separate license agreements applicable to those APIs. “API” means application programming interfaces and their specifications and implementing code that allows other software to communicate with or call on Siemens’ or third parties’ products or services and may be made available through Siemens’ or third parties’ products, documentations or otherwise.

images/notebook.png

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+[supervisord]
+nodaemon=true 
+
+[program:mc] 
+command=mc sc  
+autorestart=true 
+stderr_logfile=/dev/stdout 
+stderr_logfile_maxbytes = 0
+stdout_logfile=/dev/stdout 
+stdout_logfile_maxbytes = 0
+
+[program:jupyter] 
+command=jupyter lab
+autostart=true
+autorestart=true
+stderr_logfile=/dev/stdout
+stderr_logfile_maxbytes = 0
+stdout_logfile=/dev/stdout
+stdout_logfile_maxbytes = 0