Space management

This notebook contains steps and code to demonstrate how to manage spaces in context of Watson Machine Learning service. It facilitates ibm-watson-machine-learning library available in PyPI repository. It introduces commands for creating, updating & deleting spaces, getting list and detailed information about them.

Some familiarity with Python is helpful. This notebook uses Python 3.8.

Learning goals

The learning goals of this notebook are:

• Create new space
• List existing spaces
• Get spaces details
• Set default space
• Update exisitng space
• Delete space

Contents

This notebook contains the following parts:

1. Set up the environment
2. Create new space
3. List all existing spaces
4. Get details about space
5. Set default space
6. Update space metadata
7. Delete existing space
8. 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:

• Create a Watson Machine Learning (WML) Service instance (a free plan is offered and information about how to create the instance can be found here).

Connection to WML

Authenticate the Watson Machine Learning service on IBM Cloud. You need to provide platform api_key and instance location.

You can use IBM Cloud CLI to retrieve platform API Key and instance location.

API Key can be generated in the following way:

ibmcloud login
ibmcloud iam api-key-create API_KEY_NAME

In result, get the value of api_key from the output.

Location of your WML instance can be retrieved in the following way:

ibmcloud login --apikey API_KEY -a https://cloud.ibm.com
ibmcloud resource service-instance WML_INSTANCE_NAME

In result, get the value of location from the output.

In the output, you can also get:

• name of the service instance
• crn of the service instance (can be found as ID value)

that can be used in next steps.

Tip: Your Cloud API key can be generated by going to the Users section of the Cloud console. From that page, click your name, scroll down to the API Keys section, and click Create an IBM Cloud API key. Give your key a name and click Create, then copy the created key and paste it below. You can also get a service specific url by going to the Endpoint URLs section of the Watson Machine Learning docs. You can check your instance location in your Watson Machine Learning (WML) Service instance details.

You can also get service specific apikey by going to the Service IDs section of the Cloud Console. From that page, click Create, then copy the created key and paste it below.

Action: Enter your api_key and location in the following cell.

api_key = 'PASTE YOUR PLATFORM API KEY HERE'
location = 'PASTE YOUR INSTANCE LOCATION HERE'

wml_credentials = {
    "apikey": api_key,
    "url": 'https://' + location + '.ml.cloud.ibm.com'
}

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)

2. Create new space

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 Deployment Spaces Dashboard to create one.

• Click New Deployment Space
• Create an empty space
• Select Cloud Object Storage
• Select Watson Machine Learning instance and press Create
• Copy space_id and paste it below

You can also use ibm_watson_machine_learning SDK to prepare the space for your work. The steps to perform it are described below.

First you need to define space metadata. You will need Watson Machine Learning instance name, crn and Cloud Object Storage crn. You can get your WML instance name and crn by following the instructions from Setup.

You can get Cloud Object Storage crn by following steps:

• Go to IBM Cloud website
• Choose storage from your Dashboard
• Select your cloud object storage
• Choose Service Credentials from the Menu on the left
• Create new credentials by clicking New Credentials or open existing credentials with Writer priviledges
• Copy resource_instance_id field and paste it below as resource_crn

Tip: If you already have a space and you want to create a new one, you can get metadata required for space creation from your existing space details by running client.spaces.get_details(your_space_id).

space_metadata = {
    'name': 'PUT_YOUR_SPACE_NAME_HERE',
    'description': 'PUT_YOUR_DESCRIPTION_HERE',
    'storage': {
        'type': 'bmcos_object_storage',
        'resource_crn': 'PUT_YOUR_COS_CRN'
    },
    'compute': {
        'name': 'PUT_YOUR_WML_INSTANCE_NAME_HERE',
        'crn': 'PUT_YOUR_WML_INSTANCE_CRN'
    }
}

Next you can create space by following cell execution.

space_details = client.spaces.store(space_metadata)
print(space_details)

You can get space id by executing the following cell.

space_id = client.spaces.get_id(space_details)
print(space_id)

Tip In order to check if the space creation is completed succesfully change next cell format to code and execute it. It should return 'active'.

client.spaces.get_details(space_id)['entity']['status']['state']

Action: If you didn't create new space in this notebook by ibm_watson_machine_learning, please assign space ID below and change cell format to code.

space_id = 'PASTE YOUR SPACE ID HERE'

3. List all existing spaces

You can use list method to print all existing spaces.

client.spaces.list()

4. Get details about space

You can use get_details method to print details about given space. You need to provide space_id of desired space.

client.spaces.get_details(space_id)

5. Set default space

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)

6. Update space metadata

You can update your space by reassigning space metadata and executing: client.spaces.update(space_id, space_metadata).

updated_space_metadata = {
    client.spaces.ConfigurationMetaNames.NAME: "Updated space name"
}

client.spaces.update(space_id, updated_space_metadata)

7. Delete existing space

You can use the command below to delete existing space. You need to provide space_id of the space you want to delete.

client.spaces.delete(space_id)

8. Summary and next steps

You successfully completed this notebook! You learned how to use ibm-watson-machine-learning client for Watson Machine Learning instance space management and clean up. Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Authors

Szymon Kucharczyk, Software Engineer at IBM.
Daniel Ryszka, Software Engineer at IBM.

Copyright © 2020, 2021, 2022 IBM. This notebook and its source code are released under the terms of the MIT License.