Use R Shiny app to create SIR model.ipynb

Kernel: Python 3 (ipykernel)

Use R Shiny app to create SIR model with ibm-watson-machine-learning

This notebook contains steps and code to demonstrate support of external R Shiny application code with ibm-watson-machine-learning library available in PyPI repository.

Some familiarity with Python is helpful. This notebook uses Python 3.

Learning goals

The learning goals of this notebook are:

• Persist a R Shiny app in in Watson Machine Learning asset repository.

• Deploy application for online scoring using client library.

• Score sample records using client library.

Contents

This notebook contains the following parts:

1. Setup

2. Application upload

3. Web service creation

4. Clean up

5. Summary and next steps

1. Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

• Contact with your Cloud Pack for Data administrator and ask him for your account credentials

Connection to WML

Authenticate the Watson Machine Learning service on IBM Cloud Pack for Data. You need to provide platform url, your username and api_key.

username = 'PASTE YOUR USERNAME HERE'
api_key = 'PASTE YOUR API_KEY HERE'
url = 'PASTE THE PLATFORM URL HERE'

wml_credentials = {
    "username": username,
    "apikey": api_key,
    "url": url,
    "instance_id": 'openshift',
    "version": '4.7'
}

Alternatively you can use username and password to authenticate WML services.

wml_credentials = {
    "username": ***,
    "password": ***,
    "url": ***,
    "instance_id": 'openshift',
    "version": '4.7'
}

Install and import the ibm-watson-machine-learning package

Note: ibm-watson-machine-learning documentation can be found here.

!pip install -U ibm-watson-machine-learning

from ibm_watson_machine_learning import APIClient

client = APIClient(wml_credentials)

Working with spaces

First of all, you need to create a space that will be used for your work. If you do not have space already created, you can use {PLATFORM_URL}/ml-runtime/spaces?context=icp4data to create one.

• Click New Deployment Space

• Create an empty space

• Go to space Settings tab

• Copy space_id and paste it below

Tip: You can also use SDK to prepare the space for your work. More information can be found here.

Action: Assign space ID below

space_id = 'PASTE YOUR SPACE ID HERE'

You can use list method to print all existing spaces.

client.spaces.list(limit=10)

To be able to interact with all resources available in Watson Machine Learning, you need to set space which you will be using.

client.set.default_space(space_id)

'SUCCESS'

2. Application upload

In this section you will learn how to store downloaded application as a WML asset.

Download R Shiny app from git project using wget

Hint: To install wget execute !pip install wget.

import os
import wget

filename = "app.R.zip"
url = "https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd4.6/models/r_shiny/app.R.zip"
if not os.path.isfile(filename):
    filename = wget.download(url)

Upload application as data asset

shiny_details = client.shiny.store(
    meta_props={
        client.shiny.ConfigurationMetaNames.NAME: "R Shiny app - SIR",
    },
    file_path=filename
)
shiny_asset_id = client.shiny.get_id(shiny_details)

Creating Shiny asset...
SUCCESS

Note: You can see that application is saved in Watson Machine Learning Service.

client.shiny.list(limit=5)

3. Create online deployment

You can use commands bellow to create online deployment for stored application (web service).

deployment = client.deployments.create(
    artifact_uid=shiny_asset_id,
    meta_props={
                client.deployments.ConfigurationMetaNames.NAME: "Deployment R Shiny's SIR",
                client.deployments.ConfigurationMetaNames.R_SHINY: {"authentication" : "anyone_with_url" },
                client.deployments.ConfigurationMetaNames.HARDWARE_SPEC: {"name":"S", "num_nodes":1},
    }
)
deployment_id = client.deployments.get_id(deployment)

#######################################################################################

Synchronous deployment creation for uid: '0f73fdef-1fea-46c7-96b0-7f91208b931c' started

#######################################################################################


initializing.................................
ready


------------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_uid='2c85fe90-b624-4546-adbb-d4a2408db20a'
------------------------------------------------------------------------------------------------

Get deployments details

deployment_id = client.deployments.get_id(deployment)
client.deployments.get_details(deployment_id)

{'entity': {'asset': {'id': '0f73fdef-1fea-46c7-96b0-7f91208b931c'},
  'custom': {},
  'deployed_asset_type': 'r_shiny',
  'description': 'deployment rshiny deployment',
  'hardware_spec': {'id': 'e7ed1d6c-2e89-42d7-aed5-863b972c1d2b',
   'name': 'S',
   'num_nodes': 1},
  'name': 'deployment_rshiny',
  'r_shiny': {'authentication': 'anyone_with_url'},
  'space_id': '757f4731-bb33-48d9-a871-3fad5c16c954',
  'status': {'rshiny_url': {'url': 'https://wmlgmc-cpd-wmlgmc.apps.wmlautoai.cp.fyre.ibm.com/ml/v4/deployments/2c85fe90-b624-4546-adbb-d4a2408db20a/r_shiny'},
   'state': 'ready'}},
 'metadata': {'created_at': '2021-02-16T12:12:50.695Z',
  'description': 'deployment rshiny deployment',
  'id': '2c85fe90-b624-4546-adbb-d4a2408db20a',
  'modified_at': '2021-02-16T12:12:50.695Z',
  'name': 'deployment_rshiny',
  'owner': '1000330999',
  'space_id': '757f4731-bb33-48d9-a871-3fad5c16c954'}}

4. Clean up

If you want to clean up all created assets:

• experiments

• trainings

• pipelines

• model definitions

• models

• functions

• deployments

please follow up this sample notebook.

5. Summary and next steps

You successfully completed this notebook! You learned how to use Watson Machine Learning for SPSS model deployment and scoring.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Author

Amadeusz Masny, Python Software Developer in Watson Machine Learning at IBM Jan Sołtysik, Intern in Watson Machine Learning at IBM

Copyright © 2020-2025 IBM. This notebook and its source code are released under the terms of the MIT License.