Use PMML to predict iris species with ibm-watsonx-ai¶

This notebook contains steps from storing sample PMML model to starting scoring new data.

Some familiarity with python is helpful. This notebook uses Python 3.11.

You will use a Iris data set, which details measurements of iris perianth. Use the details of this data set to predict iris species.

Learning goals¶

The learning goals of this notebook are:

  • Working with the WML instance
  • Online deployment of PMML model
  • Scoring of deployed model

Contents¶

This notebook contains the following parts:

  1. Setup
  2. Model upload
  3. Web service creation
  4. Scoring
  5. Clean up
  6. 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).

Install and import the ibm-watsonx-ai and dependecies¶

Note: ibm-watsonx-ai documentation can be found here.

In [ ]:
!pip install wget
!pip install -U ibm-watsonx-ai | tail -n 1

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.

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.

In [1]:
api_key = 'PASTE YOUR PLATFORM API KEY HERE'
location = 'PASTE YOUR INSTANCE LOCATION HERE'
In [1]:
from ibm_watsonx_ai import Credentials

credentials = Credentials(
    api_key=api_key,
    url='https://' + location + '.ml.cloud.ibm.com'
)
In [2]:
from ibm_watsonx_ai import APIClient

client = APIClient(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 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

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

Action: Assign space ID below

In [3]:
space_id = 'PASTE YOUR SPACE ID HERE'

You can use list method to print all existing spaces.

In [ ]:
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.

In [4]:
client.set.default_space(space_id)
Out[4]:
'SUCCESS'

2. Upload model¶

In this section you will learn how to upload the model to the Cloud.

Action: Download sample PMML model from git project using wget.

In [5]:
import os
from wget import download

sample_dir = 'pmml_sample_model'
if not os.path.isdir(sample_dir):
    os.mkdir(sample_dir)
    
filename=os.path.join(sample_dir, 'iris_chaid.xml')
if not os.path.isfile(filename):
    filename = download('https://raw.githubusercontent.com/IBM/watson-machine-learning-samples/master/cloud/models/pmml/iris-species/model/iris_chaid.xml', out=sample_dir)

Store downloaded file in Watson Machine Learning repository.

In [6]:
sw_spec_id = client.software_specifications.get_id_by_name("pmml-3.0_4.3")

meta_props = {
    client.repository.ModelMetaNames.NAME: "pmmlmodel",
    client.repository.ModelMetaNames.SOFTWARE_SPEC_ID: sw_spec_id,
    client.repository.ModelMetaNames.TYPE: 'pmml_4.2.1'}
In [7]:
published_model = client.repository.store_model(model=filename, meta_props=meta_props)

Note: You can see that model is successfully stored in Watson Machine Learning Service.

In [ ]:
client.repository.list_models()

3. Create online deployment¶

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

In [8]:
model_id = client.repository.get_model_id(published_model)
deployment = client.deployments.create(
    artifact_id=model_id,
    meta_props={
        client.deployments.ConfigurationMetaNames.NAME: "Test deployment",
        client.deployments.ConfigurationMetaNames.ONLINE:{}}
)


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

Synchronous deployment creation for uid: '9674cc1d-2556-4928-a7d1-f0a70967bc31' started

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


initializing
Note: online_url and serving_urls are deprecated and will be removed in a future release. Use inference instead.

ready


------------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_uid='d2d6e1ff-5d37-42b1-b37b-495294213af3'
------------------------------------------------------------------------------------------------

4. Scoring¶

You can send new scoring records to web-service deployment using score method.

In [11]:
import pprint
deployment_id = client.deployments.get_id(deployment)

scoring_data = {
    client.deployments.ScoringMetaNames.INPUT_DATA: [
        {
            'fields': ['Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width'],
            'values': [[5.1, 3.5, 1.4, 0.2]]
        }]
}

predictions = client.deployments.score(deployment_id, scoring_data)
pprint.pprint(predictions)

{'predictions': [{'fields': ['$R-Species',
                             '$RC-Species',
                             '$RP-Species',
                             '$RP-setosa',
                             '$RP-versicolor',
                             '$RP-virginica',
                             '$RI-Species'],
                  'values': [['setosa', 1.0, 1.0, 1.0, 0.0, 0.0, '1']]}]}

As we can see this is Iris Setosa flower.

5. 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.

6. Summary and next steps¶

You successfully completed this notebook! You learned how to use Watson Machine Learning for PMML model deployment and scoring. Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Authors¶

Lukasz Cmielowski, PhD, is a Software Architect and Data Scientist at IBM.

Mateusz Szewczyk, Software Engineer at Watson Machine Learning

Copyright © 2020-2024 IBM. This notebook and its source code are released under the terms of the MIT License.