Pre-work
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Before we get started, we will download some assets and complete some setup for our workshop. This section is broken up into the following steps:
Various parts of this workshop will require the attendee to upload files or run scripts. These artifacts have been collected in the following two zip files which you can download using the links below.
For each line below, click on the [Download] link to get the file. If the link isn't working for you, try clicking the [Mirror] to get it from out backup servers. You'll need these files in the next sections.
Launch a browser and navigate to one of the following links based on what day you're joining us and if you already have an account:
You can then log into your IBM Cloud account using your IBMid or create a new one. Note that the correct region should already be selected for you.
If you are a new user, use the Create a new IBM Cloud Account section.
If you are a returning user, click on the Log in with your IBMid link.
The services required for IBM Cloud Pak for Data will be automatically provisioned for you. Once you see a message that says that the apps are ready to use, click on Go to IBM Cloud Pak for Data.
In Cloud Pak for Data, we use the concept of a project to collect / organize the resources used to achieve a particular goal (resources to build a solution to a problem). Your project resources can include data, collaborators, and analytic assets like notebooks and models, etc.
Go the (☰) navigation menu, expand Projects and click on the View all projects link.
Click on the New + button on the top.
We are going to create a project from an existing file (which contains the assets we will use throughout this workshop), as opposed to creating an empty project. Select the Create a project from a sample or file option.
Click on the browse link and in the file browser popup, navigate to where you downloaded the files for this lab. Then select the CreditRiskProject.zip file.
Give the project a name. You also need to provide an object storage instance for this project. If you haven't already created a Cloud Object Storage instance in your IBM Cloud account, you can create one now by clicking Add.
A new tab opens up, where you can create the Cloud Object Service. By default, a Lite (Free) plan will be selected. Scroll down and update the name of your Cloud Object Storage service if you wish, and click Create.
The browser tab will automatically close when the Cloud Object Storage instance has been created. Back on IBM Cloud Pak for Data as a Service, click Refresh.
The newly created Cloud Object Storage instance will now be displayed under "Storage". Click Create to finish creating the project.
You can see a progress bar that says your project is being created. Once the project is succesfully created, on the pop up window click on the View new project button.
Clicking on the Assets tab will show all the assets that were imported into the project when it was created.
You will need to associate a Watson Machine Learning service instance to your project in order to run Machine Learning experiments.
Go to the Settings tab of your project and look for the Associated services section. Click on Add service and in the menu that opens up, click on Watson.
Click the checkbox next to the Watson Machine Learning service instance that was created for you when you signed up for Cloud Pak for Data as a Service. Click Associate service.
Note: If you have multiple WatsonMachineLearning services, make sure you select the one that is in the same regions as is your Cloud Pak for Data as a service.
Note: Also make sure that the Name of the instance matches the name of the WatsonMachineLearning that you added in the earlier steps
You will see a notification that the WatsonMachineLearning service was successfully associated with your project. Click on the X in the right top corner to close the pop up modal and go back to your project.
Cloud Pak for Data uses the concept of Deployment Spaces to configure and manage the deployment of a set of related deployable assets. These assets can be data files, machine learning models, etc. For this workshop, we need to create one.
Go the (☰) navigation menu, expand Deployment spaces and then select View all spaces.
Click on the New deployment space button.
We will create an empty deployment space, so click on the Create an empty space option.
Give your deployment space a unique name and optional description. Provide the Cloud Object Storage instance that you had created when you were creating the project and select the Machine Learning Service instance associated with your IBM Cloud Pak for Data as a Service instance, then click the Create button.
Once the deployment space is created, you can click on View new space.
In some parts of this workshop, you will be executing Jupyter notebooks which use the Watson Machine Learning API to perform operations on your Watson Machine Learning instance. For the Jupyter notebooks to gain access to your Watson Machine Learning instance, you will need to provide them with the API key for your IBM Cloud account as well as the location of the WML service instance.
Use one of the following methods to retrieve the IBM Cloud Platform API key:
Once the IBM Cloud CLI is installed, run the following command in your terminal to log into your IBM Cloud account. Running this command will prompt you to enter your email address and password.
Once you have successfully logged in, generate an API key using the following command. Replace API_KEY_NAME with a unique name.
Get the value of API Key from the result of the command. This is the api_key value that you will need to provide in your Jupyter notebooks for accessing the Watson Machine Learning service instance.
Alternatively, you can use the IBM Cloud Console to generate the IBM Cloud API key.
Select My IBM Cloud API keys in the View dropdown and then click Create an IBM Cloud API key +.
Give your API key a unique name and click Create. You should see a message that says API key successfully created. Click Copy to copy the generated API key.
This is the api_key value that you will need to provide in your Jupyter notebooks for accessing the Watson Machine Learning service instance.
Option 1: You can select the Watson Machine Learning location code from the table below if you are sure where you've deployed your instance.
Region
Region Codes
Dallas
us-south
Tokyo
jp-tok
Option 2: Alternatively, if you prefer to use the CLI, you can use the API key to obtain the location of the Watson Machine Learning Service instance associated with your IBM Cloud Pak for Data as a Service instance.
Run the following command in a terminal to log into IBM Cloud using the API Key you had generated earlier. Remember to update API_KEY with your api key.
Run the following command to retrieve information about the Watson Machine Learning service instance. Remember to update WML_INSTANCE_NAME with the name of the Watson Machine Learning instance associated with your IBM Cloud Pak for Data as a Service instance.
Get the value of Location from this result. This is the location value that you will need to provide in your Jupyter notebooks for accessing the Watson Machine Learning service instance.
At this point we are done with this section. We have completed creating an IBM Cloud account, a Cloud Pak for Data as a Service instance, and the project and deployment space that we will use in the rest of this workshop. We have also obtained the IBM Cloud API key and the Watson Machine Learning service instance location region code that we will use in the Jupyter notebooks section.
Q1: I'm having issues downloading the files
A: For each file that you need to download, we have provided multiple options: the [Download] button as well as the [Mirror] links. Try downloading the file from any of those links. If neither works for you, reach out to the instructors and they can provide the files to you.
Q1: I don't have all the services needed.
A: In some rare cases, the services will not auttomatically provision for you. You can do that manually by following these instructions:
Once you are on IBM Cloud Pak for Data, on the top right corner click on your avatar, and then click on Profile and settings. Go to the Services tab.
Q2: I get the That email address is already registered to an IBM Cloud account. messsage.
A: You must already have an IBMid account. Follow the login link provided in the error message to login to your existing account.
Q3: I get the Your Watson Studio, Watson Knowledge Catalog, and Watson Machine Learning Lite services must be created in the same service region. error.
CP4DaaS Project |
Python Application |
Note: If you are a returning user and you have watson services in a different region than the pre-selected one, you will see an error message telling you to select that region instead. See Q3 in the section for help.
If you have any issues, please see the .
Install the using the instructions in the link.
Go to the .
Install the using the instructions in the link.
If you have any questions, see the section. Otherwise, you can go to the next lab.
If the Machine Learning service instance is not listed under You Cloud Pak for Data Services then find it in the Try our Available Services section and click on the Add button.
Next, note down the name of the Machine Learning service instance in your Cloud Pak for Data section. This is the blue hyperlink underneeth the Machine Learning card title. You will need to provide this name in future steps.
A: This means you have previously created some Watson services in a different region. To resolve this, go to the page, select the region you had previously used and then login using the login link at the bottom right. Alternatively, you can create a new account and proceed as a new user to follow along.
