IBM/node-red-dsx-workflow

This journey helps to build a complete end-to-end analytics solution using IBM Watson Studio. This repository contains instructions to create a custom web interface to trigger the execution of Python code in Jupyter Notebook and visualise the response from Jupyter Notebook on IBM Watson Studio.

Orchestrate data science workflows using Node-RED

Create a web interface using Node-RED to trigger an IBM Watson Studio analytics workflow

Data Science Experience is now Watson Studio. Although some images in this code pattern may show the service as Data Science Experience, the steps and processes will still work.

IBM Watson Studio can be used to analyze data using Jupyter notebooks. There is no mechanism exposed by Watson Studio to trigger execution of the notebook cells from outside. If this capability is added, we can build a complete end to end analytics solution using IBM Watson Studio.

The below two requirements are addressed by this journey to help build a complete analytics solution with IBM Watson Studio.

• Trigger the execution of Python code in a Jupyter Notebook on IBM Watson Studio from a custom web user interface
• Visualize the response from the Python code execution in a Jupyter Notebook on IBM Watson Studio on the custom web user interface

We will use Node-RED to invoke the analytics workflows in Jupyter notebooks on IBM Watson Studio and also to render a custom web user-interface with minimal programming.

What is Node-RED?

Node-RED is a tool for wiring together APIs and online services on IBM Cloud. The APIs and online services are configured as nodes that can be wired to orchestrate a workflow. It is also a web server where the UI solution can be deployed. It has nodes that support integration with many database services, watson services and analytics services.

Node-RED reduces a lot of development effort. It is easy to improve the solution using other services with Node-RED. It opens a world of possibilities for developers.

When the reader has completed this journey, they will understand how to:

• Create and run a Jupyter notebook in Watson Studio.
• Use Object Storage to access data files.
• Use Python Pandas to derive insights on the data.
• Develop a custom web user interface using Node-RED.
• Triggering an analytics workflow on Watson Studio from the UI using Node-RED.

The intended audience for this journey are developers who want to develop a complete analytics solution on Watson Studio with a custom web user interface.

1. The Object storage stores the data.
2. Data is utilized as csv files.
3. The Jupyter notebook processes the data and generates insights.
4. The Jupyter notebook is powered by Spark.
5. The Node-RED hosts a websocket server that is a medium of communication between the Jupyter notebook on IBM Watson Studio and Web UI.
6. The Node-RED hosts a web server that renders the Web UI.

Included components

• Node-RED: Node-RED is a programming tool for wiring together APIs and online services.

• IBM Watson Studio: Analyze data using RStudio, Jupyter, and Python in a configured, collaborative environment that includes IBM value-adds, such as managed Spark.

• IBM Cloud Object Storage: An IBM Cloud service that provides an unstructured cloud data store to build and deliver cost effective apps and services with high reliability and fast speed to market.

• Jupyter Notebooks: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.

Featured technologies

• Data Science: Systems and scientific methods to analyze structured and unstructured data in order to extract knowledge and insights.

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Steps

Follow these steps to setup and run this developer journey. The steps are described in detail below.

1. Sign up for Watson Studio
2. Create IBM Cloud services
3. Import the Node-RED flow
4. Note the websocket URL
5. Update the websocket URL in HTML code
6. Create the notebook
7. Add the data
8. Update the notebook with service credentials and websocket URL
9. Run the notebook
10. Analyze the results

1. Sign up for Watson Studio

Sign up for IBM's Watson Studio. By signing up for Watson Studio, an Object Storage service will be created in your IBM Cloud account.

2. Create IBM Cloud services

• Create the Node-RED Starter application.

• Choose an appropriate name for the Node-RED application - App name:.

• Click on Create.

  • Node-RED Starter

  

  • On the newly created Node-RED application page, Click on Visit App URL to launch the Node-RED editor once the application is in Running state.
  • On the Welcome to your new Node-RED instance on IBM Cloud screen, Click on Next.
  • On the Secure your Node-RED editor screen, enter a username and password to secure the Node-RED editor and click on Next.
  • On the Browse available IBM Cloud nodes screen, click on Next.
  • On the Finish the install screen, click on Finish.
  • Click on Go to your Node-RED flow editor.

3. Import the Node-RED flow

• Clone this repo.
• Navigate to the orchestrate_dsx_workflow.json.
• Open the file with a text editor and copy the contents to Clipboard.
• On the Node-RED flow editor, click the Menu and select Import -> Clipboard and paste the contents.

Deploy the Node-RED flow by clicking on the Deploy button

4. Note the websocket URL

The websocket URL is ws://<NODERED_BASE_URL>/ws/orchestrate where the NODERED_BASE_URL is the marked portion of the URL in the above image.

Note:

An example websocket URL for a Node-RED app with name myApp is ws://myApp.mybluemix.net/ws/orchestrate, where myApp.mybluemix.net is the NODERED_BASE_URL.

The NODERED_BASE_URL may have additional region information i.e. eu-gb for the UK region. In this case NODERED_BASE_URL would be: myApp.eu-gb.mybluemix.net.

5. Update the websocket URL in HTML code

Click on the node named HTML.

Click on the HTML area and search for ws: to locate the line where the websocket URL is specified. Update the websocket URL with the base URL that was noted in the Section 4:

var websocketURL = "ws://NODERED_BASE_URL/ws/orchestrate";

Click on Done and re-deploy the flow.

6. Create the notebook

• In Watson Studio, click on Create notebook to create a notebook.
• Create a project if necessary, provisioning an object storage service if required.
• In the Assets tab, select the Create notebook option.
• Select the From URL tab.
• Enter a name for the notebook.
• Optionally, enter a description for the notebook.
• Enter this Notebook URL: https://github.com/IBM/node-red-dsx-workflow/blob/master/notebooks/node_red_dsx_workflow.ipynb
• Select the free Anaconda runtime.
• Click the Create button.

7. Add the data

Add the data to the notebook

• Please download the files - summer.csv and dictionary.csv from: https://www.kaggle.com/the-guardian/olympic-games.
• Rename the file summer.csv to olympics.csv
• From your project page in Watson Studio, click Find and Add Data (look for the 10/01 icon) and its Files tab.
• Click browse and navigate to where you downloaded olympics.csv and dictionary.csv on your computer.
• Add the files to Object storage.

8. Update the notebook with service credentials and websocket URL

Add the Object Storage credentials to the notebook

• Select the cell below 2.1 Add your service credentials for Object Storage section in the notebook to update the credentials for Object Store.
• Use Find and Add Data (look for the 10/01 icon) and its Files tab. You should see the file names uploaded earlier. Make sure your active cell is the empty one created earlier.
• Select Insert to code below olympics.csv.
• Click Insert Crendentials from the drop down menu.
• If the credentials are written as credential_2 change them to credentials_1.

Update the websocket URL in the notebook

• In the cell below 6. Expose integration point with a websocket client, update the websocket url noted in section 4 in the start_websocket_listener function.

9. Run the notebook

When a notebook is executed, what is actually happening is that each code cell in the notebook is executed, in order, from top to bottom.

Each code cell is selectable and is preceded by a tag in the left margin. The tag format is In [x]:. Depending on the state of the notebook, the x can be:

• A blank, this indicates that the cell has never been executed.
• A number, this number represents the relative order this code step was executed.
• A *, this indicates that the cell is currently executing.

There are several ways to execute the code cells in your notebook:

• One cell at a time.
  • Select the cell, and then press the Play button in the toolbar.
• Batch mode, in sequential order.
  • From the Cell menu bar, there are several options available. For example, you can Run All cells in your notebook, or you can Run All Below, that will start executing from the first cell under the currently selected cell, and then continue executing all cells that follow.
• At a scheduled time.
  • Press the Schedule button located in the top right section of your notebook panel. Here you can schedule your notebook to be executed once at some future time, or repeatedly at your specified interval.

For this Notebook, you can simply Run All cells. The websocket client will be started when you run the cell under 7. Start websocket client. This will start the communication between the UI and the Notebook.

10. Analyze the results

The UI can be accessed at the URL: http://<NODERED_BASE_URL>/dsxinsights. The <NODERED_BASE_URL> is the base URL noted in section Note the websocket URL.

Troubleshooting

See DEBUGGING.md.

License

This code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer Certificate of Origin, Version 1.1 (DCO) and the Apache Software License, Version 2.

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