ibm-watson-machine-learning
¶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.
You will use a Iris data set, which details measurements of iris perianth. Use the details of this data set to predict iris species.
The learning goals of this notebook are:
This notebook contains the following parts:
Before you use the sample code in this notebook, you must perform the following setup tasks:
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.
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'
}
!pip install -U ibm-watson-machine-learning
from ibm_watson_machine_learning import APIClient
client = APIClient(wml_credentials)
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.
space_id
and paste it belowTip: 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)
In this section you will learn how to upload the model to the Cloud.
Action: Download sample PMML model from git project using wget.
!pip install wget
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)
ls pmml_sample_model
Store downloaded file in Watson Machine Learning repository.
sw_spec_uid = client.software_specifications.get_uid_by_name("pmml-3.0_4.3")
meta_props = {
client.repository.ModelMetaNames.NAME: "pmmlmodel",
client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: sw_spec_uid,
client.repository.ModelMetaNames.TYPE: 'pmml_4.2.1'}
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.
client.repository.list_models()
You can use commands bellow to create online deployment for stored model (web service).
model_uid = client.repository.get_model_id(published_model)
deployment = client.deployments.create(
artifact_uid=model_uid,
meta_props={
client.deployments.ConfigurationMetaNames.NAME: "Test deployment",
client.deployments.ConfigurationMetaNames.ONLINE:{}}
)
You can send new scoring records to web-service deployment using score
method.
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)
print(predictions)
As we can see this is Iris Setosa flower.
If you want to clean up all created assets:
please follow up this sample notebook.
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.
Lukasz Cmielowski, PhD, is a Software Architect and Data Scientist at IBM.
Copyright © 2020 IBM. This notebook and its source code are released under the terms of the MIT License.