Setup Watson Studio

In this exercise you will setup and configure Watson Studio to use the Health and Predict notebook templates. The templates can be used for detecting anomalies and predicting asset failures of assets and for other purposes using Health and Predict - Utilities.

• Get URL and User credentials to access Watson Studio.
• Create a project and add the setup notebook to your project in Watson Studio.
• Setup Database Connection to allow Watson Studio to access DB.
• Get URL to Download the Healh and Predict Notebooks.
• Create am environment to edit and run your notebooks.
• Add Notebook From File to Watson Studio Project.
• Download the Predict pmlib Python Library Documentation used for working with Predict algorithms and automating device and data tasks in Monitor.

Get URL and User credentials to access Watson Studio

Ask your Maximo Application Suite administrator to get your user name and password for Watson Studio, API keys and URL that you will use to connect to Watson Studio. You or he can use these steps to get them.

1. To get the Watson Studio URL, login to Maximo Application Suite. Click on Adminstration

2. Click on Configurations and Watson Studio

3. Click on System After you create the Watson Studio User be sure to make note of the URL, user name and password. You will use it to login to Cloud Pak for Data later with it.

4. Open a browser to the URL and use the credentials from the previous steps to login to Cloud Pak for Data. After logging in you see your Watson Studio Overview page.

Create a Project in Watson Studio

Watson Studio projects are where you: - Keep your model templates - Data Assets - Train deploy and many the Models - Create and manage environment for training and deploying your models.

Create a project so that you can add and setup Health, Predict and Utilities notebooks using Watson Studio.

1. Click on 'All Projects' where you will then create a new project.

2. Click 'New Project'.

3. Accept default and click 'Next'.

4. Click 'New Empty Project'.

5. Enter a project name and description.

6. Click Add to Project.

This is how you will add the Predict notebook templates to your project that you download later in this exercise.

Setup Database Connection in Watson Studio

Setup database security to allow Watson Studio to access the Maximo Application Suite databases.

1. Follow the instructions in the documentation to download the database security certificate file Database PEM file If your database was deployed within CP4D use the directions below.

2. Open a separate browser window and login to Cloud Pak For Data Console to get the database pem file. Click on the Hamburger menu, Instances Select the database name used by Maximo Monitor. The database name should include IOT in the name.

3. Click on Console to download the database pem file. If you receive an error saying the console hasn't been provisioned ask your IT Administrator to install it.

4. Click on the Download SSL Certificate

5. Return to the browser window that has Watson Studio Project you created earlier. Add the database pem file as a data asset. Drag and drop the database pem file into the data assets.

!!! If your database was deployed outside of CP4D you will have to use the directions below.

1. Login to Openshift Cluster.

2. Select Project db2u, select workloads, select Secrets, search for db2u-certificate. Select it. In Details tab Data section choose ca.crt copy certificate. Save it in a text based file named database.pem on your location machine. You will upload this file to CP4D Watson Studio project later as a data asset.

Get URL to Download the Health and Predict and Utilities Notebooks

These notebooks templates are what you use to calculate asset health and risk scores, asset end of life probability, anomaly detection, predict EOL failure dates and more.

The Health and Predict notebooks require programmatic access and credentials to access Maximo Application Suite. You will need these values to create the URL that allows you to download the Health, Predict and Utilities notebooks zip file. The URL looks like:

https://`EXTERNAL_APM_API_BASEURL`/ibm/pmi/service/rest/ds/`APM_ID`/`APM_API_KEY`/file/download

Environment Variable	Find It In	Used For
APM_ID	The value for the mxe.PMIId system property.	Used to train and score all notebooks
APM_API_BASEURL	The root of the URL value for the PREDICTAPI endpoint.	Used to train and score all notebooks
EXTERNAL_APM_API_BASEURL	The route location for the Predict project retrieved from the Red Hat® OpenShift® Container Platform.	Used to download the notebooks
APM_API_KEY	The API key from MAS Integration Application to programatically access Predict APIs.	Used to download and use the notebooks

Get mxe.PMIId value used for APM_ID

1. To get the value of mxe.PMIId, open a browser and access the Health and Predict application. See below example of where you can find these values in Health and Predict system properties. Click on Application Administration

2. Click on System Configurations, Platform Configurations and System Properties. Click on Funnel icon to filter properties that begin with property name PMI.

It should look like: APM_ID = mxe.PMIId = f3fake48

Get PREDICTAPI endpoint used for APM_API_BASEURL

1. To get APM_API_BASEURL from the value from PREDICTAPI endpoint. You can find endpoints in Health and Predict Adminstration application by searching on End Points. Click on Application Administration

2. Click in search field and search application for End Points

3. Note the URL for the PREDICTAPI endpoint. -

It should look like: APM_API_BASEURL = PREDICTAPI endpoint = https://main.predict.ivt11rel87.ivt.suite.maximo.com

Get root location for the EXTERNAL_APM_API_BASEURL

1. Open a browser and access the Openshift Console MAS is deployed on. For IBM Cloud deployments goto https://cloud.ibm.com, select your account from the drop down menu and click Clusters

2. Under Resources click on your cluster resource.

3. Click on Openshift Web Console

4. Click on Project. Search on Predict and Click on Project

5. Search Predict

6. Navigate to Routes

7. Search for masdev-masdemo-predict-api The URL should be under the location tab and look similar as the one below.

   https://masdev.predict.devtest.hpdevcloud-6xxxxxxxxxxxxxx91ab-0000.us-south.containers.appdomain.cloud/ibm/pmi/service

8. Trim the URL end to get the APM_API_BASEURL by removing /ibm/pmi/service.

It should look like: EXTERNAL_APM_API_BASEURL = https://masdev.predict.devtest.hpdevcloud-6xxxxxxxxxxxxxe91ab-0000.us-south.containers.appdomain.cloud

Get APM_API_KEY

API keys are used by the notebooks to make program calls to the Health and Predict APIs. API keys are created for specific users or programs using the Administration tools. 1. In Health and Predict and Utilities menus on the left, click on Application Administration to open the Start Center

1. Click Administration and sub menu Administration

2. Click on Integration menu to access the Start Center where you can create keys.

3. Click on API Keys menu and the Add API Key button to create an API keys or select an API key from those available.

4. Click Copy key to get APM_API_KEY string you need to create the URL to download the zip file of Predict Notebooks.

Download the model template notebooks

Using the key value pairs in the previous steps you wil create the URL to download the Predict notebooks from a browser.

Replace key variable you collected in the previous steps with the values to create the download URL:

For example:

https://`EXTERNAL_APM_API_BASEURL`/ibm/pmi/service/rest/ds/`APM_ID`/`APM_API_KEY`/file/download

EXTERNAL_APM_API_BASEURL = https://masws.predict.ivt09rel86.fake.suite.maximo.com APM_ID = f3fake48 APM_API_KEY = n7gssk_fake_l_fake_486_fake_967_fake_jm8

The final URL becomes:

https://masws.predict.ivt09rel86.fake.suite.maximo.com/ibm/pmi/service/rest/ds/f3fake48/n7gssk_fake_l_fake_486_fake_967_fake_jm8/file/download

1. Make note of the values and create the URL you can use in the browser to download the zip file. Here is an example download URL for Predict model notebook templates zip file using the values from the previous steps.

2. Open a browser and use the URL to download and save the Predict Model templates zip file locally.

3. Unzip the models file and then upload the notebooks one at a time into your Watson Studio Project Assets. In your finder or file explorer, double click on the zip file to extract the notebooks. Make note of where you unzipped the file, as you will need it for the next steps.

Be sure to prepend your name initials to any model you upload to shared project areas when doing labs so you can remember which project notebook template was yours.

Set up an Environment to train and test your models in Watson Studio

The default environment is only 1 vCPU. You must have an environment with at least 4 vCPUs in order to train the Predict Models.

1. In your Watson Studio project, create a 4 vCPU environment by clicking on environment and clicking the New enviornment definition link.

2. Enter a name for the environment. Hit the plus button to increase the vCPUs to 4. Choose Python v3.8 for the Software version. Press the 'Create' button to create then environment.

Add Notebook From File to a Watson Studio Project

You can use the steps below to add any notebook from File to your Watson Studio Project. Notebooks are used to create scores, predictions and for other algorithms in Health and Predict - Utilities. In this exercise you will add a Predict Notebooks you will use to your Project. Select the WS - Predicted Failure Date-Survive Analysis.ipynb notebook template that you will use in the next exercise to predict failure dates of the pump.

1. Add a Predict notebook by file option to your Watson Studio Project. Return to your Cloud Pak for Data project. Search for your project by name. Click on the project name link to open your project.
   

2. Add one of Predict notebook you unzipped earlier. Click Add to Project and select notebook option.

3. Click the From File tab. Drag and drop the notebook you would like to add to the area to add notebooks. To save time in the next exercise choose the notebook for WS - Predicted Failure Date-Survive Analysis replace WS with your initials when naming the notebook.

4. Prepend your name initials to the name of the model file template you uploaded. Select the runtime environment you created in the previous step that has 4 vCPUs. Drag and drop your notebook template file into the area indicated below. Click on the Create button to create the notebook.

5. Confirm that you see 4 vCPUs for the notebook as it starts.

6. If the notebook has the wrong number of vCPUs you can change the environment by clicking on the 'information' icon and selecting the right Environment from the dropdown select box.

7. If you had to change the environment, click the Confirm button to restart the notebook editor in Waston Studio

To Stop and Restart a Notebook Template in a Watson Studio Project

If your notebook environment fails to start you can restart the environment by following the steps below.

1. Add one of the Predict notebook templates to your Watson Studio Project. Click the information icon, environment tab and restart from the Runtime Status select box.

2. Confirm your selection to restart the notebook by pressing the Change button.

Download Predict pmlib Python Library Documentation

Predict pmlib Python Library is used for working with Predict algorithms and automating device and data tasks in Monitor.or open the Predicted Failure Data template notebook to your Project. Use the steps from the previous exercise Add Notebook From File to a Watson Studio Project Prepend your initials to the template. If you already have uploaded the notebook, open it with Watson Studio.

1. You can download the documentation using a URL similar to the one you create in the previous Exercise for downloading the doc-8.5.1.zip that contains the html files for the pmilib documentation.

For example:

https://`EXTERNAL_APM_API_BASEURL`/ibm/pmi/service/rest/ds/`APM_ID`/`APM_API_KEY`/doc/download

1. Unzip the doc-8.5.1.zip file you downloaded

2. Click on the file index.html file you unzipped. The browser should open with the documentation below.

Setup Environment Variables for Predict in Notebook Templates

One of the earlier steps explained how to manually find the environment variables used in Predict to do things like download the zip file with the Predict Notebooks. When using a Predict Notebook in Watson Studio you can also programatically discover these environment variables since they are set for you in the Cloud Pack for Data Environment that was installed with Maximo Application Suite. Use the code below if it isn't already included in the notebook templates to get the required Predict environment variables.

Sample Code

import json
import os

# Try to open environment variables json file if it already exists.
try:
  f = open('/project_data/data_asset/hc_Predict_Envs.json',)
except:
  f = {}
  print("No execution json found, initialising with current notebook information")

data = json.load(f)
f.close()

os.environ['APM_ID'] = data['APM_ID']
os.environ['APM_API_BASEURL'] = data['APM_API_BASEURL']
os.environ['APM_API_KEY'] = data['APM_API_KEY']

# README: Change to true if Health/Predict are deployed on different cluster from this CP4D environment
use_external = False
if use_external:
    import os
    os.environ['USER_PROVIDED_HEALTH_URL'] = data['USER_PROVIDED_HEALTH_URL']
    os.environ['USER_PROVIDED_DB_CONNECTION_STRING'] = data['USER_PROVIDED_DB_CONNECTION_STRING']
    os.environ['USER_PROVIDED_URL'] = data['USER_PROVIDED_URL']
import os

os.environ['SSL_VERIFY_APM'] = 'False'
os.environ['SSL_VERIFY_AS'] = 'False'

os.environ['TRUST_PREDICT'] = os.getenv('APM_API_BASEURL')[8:]
print(os.getenv('TRUST_PREDICT'))
main.predict.ivt11rel87.ivt.suite.maximo.com
import os
os.environ['TRUST_PREDICT'] = os.getenv('APM_API_BASEURL')[8:]
print(os.getenv('TRUST_PREDICT'))

PIP Install PMLib Predict in Notebook Templates

Install the Predict Python Software Develop Kit (SDK) called PMLib using pip install.

Sample Code

# Initialize the Maximo-Predict environment variables
import pmlib
from pmlib import api
api.init_environ()

Using Watson Studio Library

The Watson Studio Python Library named ibm_watson_studio_lib allows you to do to work with data assets in your project. Some examples you will learn later are reading and writing data from your project. Another example is getting the Watson Studio Project Information.

You will use a Watson Studio Python library named ibm_watson_studio_lib that is available in your environment by default to get your project information. See this API reference for more information.

Sample Code

#  Get the Watson Studio Project Information
from ibm_watson_studio_lib import access_project_or_space
wslib = access_project_or_space()
project_name = wslib.here.get_name()
wslib.show(project_name)
project_id = wslib.here.get_ID()
wslib.show(project_id)

Congratulations you have seen how to upload, start and restart Predict notebook in Watson Studio. You have also learned how to download and access the pmilib documentation that you will use in the next exercises. Finally you learned how to use the ibm_watson_studio_lib to work with assets in your project.

In the next exercises you will learn how to use these notebook templates to detect anomalies and predict asset failures. You will start by update and running setup notebooks to create asset types in Health and Predict.