0 / 0


To use Watson Machine Learning with the Python client library or the REST API you must authenticate to secure your work. Learn about the different ways to authenticate and how to apply them to the service of your choosing.

You use IBM Cloud® Identity and Access Management (IAM) to make authenticated requests to public IBM Watson™ services. With IAM access policies, you can assign access to more than one resource from a single key. In addition, a user, service ID, and service instance can hold multiple API keys.

Security overview

Refer to the section that describes your security needs.

Authentication credentials

These terms relate to the security requirements described in this topic.

  • API keys allow you to easily authenticate when using the Python client or APIs that can be used across multiple services. API Keys are considered confidential since they are used to grant access. Treat all API keys as you would a password since anyone with your API key can impersonate your service.
  • An IAM token is an authentication token required to access IBM Cloud services. You can generate a token using your API key in the token request. For details on using IAM tokens see Authenticating to Watson Machine Learning API.

To authenticate to a service through its API, pass your credentials to the API. You can pass either a bearer token in an authorization header or an API key.

Generating an API key

To generate an API key from your IBM Cloud user account, go to Manage access and users - API Keys and create or select an API key for your user account.

Authenticate with an IAM token

IAM tokens are temporary security credentials that are valid for 60 minutes. When a token expires, you generate a new one. Tokens can be useful for temporary access to resources. For more information, see Generating an IBM Cloud IAM token by using an API key.

Getting a service-level token

You can also authenticate with a service-level token. To generate a service-level token:

  1. Refer to the IBM Cloud instructions for creating a Service ID.
  2. Generate an API key for that Service ID
  3. Open the space where your deployable assets will reside.
  4. On the Access control tab, add the Service ID and assign an access role of Admin or Editor.

You can use the service-level token with your API scoring requests.



Python client

Refer to: Watson Machine Learning Python client external link

To create an instance of the Watson Machine Learning Python client object, you need to pass your credentials to Watson Machine Learning API client.

wml_credentials = {
                  "url": " https://HIJKL"
from ibm_watson_machine_learning import APIClient
wml_client = APIClient(wml_credentials)

Note: Even though you do not explicitly provide an instance_id, it will be picked up from the associated space or project for billing purposes. For details on plans and billing for Watson Machine Learning services, see Watson Machine Learning plans and runtime usage.

Refer to sample notebooks for examples of how to authenticate and then score a model using the Python client.


Refer to: Watson Machine Learning REST API external link

To use the Watson Machine Learning REST API, you need to obtain an IBM Cloud Identity and Access Management (IAM) token. In this example, you would just supply your API key in place of the example key.

cURL example

curl -k -X POST \
--header "Content-Type: application/x-www-form-urlencoded" \
--header "Accept: application/json" \
--data-urlencode "grant_type=urn:ibm:params:oauth:grant-type:apikey" \
--data-urlencode "apikey=123456789" \

The obtained IAM token needs to be prepended with the word Bearer, and it needs to be passed in the Authorization header for API calls.

Python example

import requests

# Paste your Watson Machine Learning service apikey here

apikey = "123456789"

# Get an IAM token from IBM Cloud
url     = "https://iam.cloud.ibm.com/identity/token"
headers = { "Content-Type" : "application/x-www-form-urlencoded" }
data    = "apikey=" + apikey + "&grant_type=urn:ibm:params:oauth:grant-type:apikey"
response  = requests.post( url, headers=headers, data=data, auth=( apikey )
iam_token = response.json()["access_token"]

Node.js example

var btoa    = require( "btoa" );
var request = require( 'request' );

// Paste your Watson Machine Learning service apikey here
var apikey = "123456789";

// Use this code as written to get an access token from IBM Cloud REST API
var IBM_Cloud_IAM_uid = "bx";
var IBM_Cloud_IAM_pwd = "bx";

var options = { url     : "https://iam.cloud.ibm.com/identity/token",
                headers : { "Content-Type"  : "application/x-www-form-urlencoded",
                            "Authorization" : "Basic " + btoa( IBM_Cloud_IAM_uid + ":" + IBM_Cloud_IAM_pwd ) },
                body    : "apikey=" + apikey + "&grant_type=urn:ibm:params:oauth:grant-type:apikey" };

request.post( options, function( error, response, body )
    var iam_token = JSON.parse( body )["access_token"];
} );

Refer to sample notebooks for examples of how to authenticate and then score a model using the REST API.

Parent topic: First steps