Chapter 3. Setting up TrustyAI for your project

Procedure

1. Open a new terminal window.

2. Follow these steps to log in to your OpenShift cluster:

   1. In the upper-right corner of the OpenShift web console, click your user name and select Copy login command.
   2. After you have logged in, click Display token.

   3. Copy the Log in with this token command and paste it in the OpenShift CLI (oc).

      $ oc login --token=<token> --server=<openshift_cluster_url>

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3. Enter the following command to set a user token variable on OpenShift:

   export TOKEN=$(oc whoami -t)

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Procedure

1. Set the TRUSTY_ROUTE variable to the external route for the TrustyAI service in your project:

   TRUSTY_ROUTE=https://$(oc get route/trustyai-service --template={{.spec.host}})

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2. Send the training data to the /data/upload endpoint:

   curl -sk $TRUSTY_ROUTE/data/upload  \
     --header 'Authorization: Bearer ${TOKEN}' \
     --header 'Content-Type: application/json' \
     -d @data/training_data.json

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   The following message is displayed if the upload was successful: 1000 datapoints successfully added to gaussian-credit-model data.

Procedure

1. Set the TRUSTY_ROUTE variable to the external route for the TrustyAI service pod.

   TRUSTY_ROUTE=https://$(oc get route/trustyai-service --template={{.spec.host}})

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2. Get the inference endpoints for the deployed model, as described in Accessing the inference endpoint for a deployed model.
3. Send data to this endpoint. For more information, see the KServe v2 Inference Protocol documentation.

1. Log in to the OpenShift web console.
2. Switch to the Developer perspective.
3. In the left menu, click Observe.
4. On the Metrics page, click the Select query list and then select Custom query.
5. In the Expression field, enter trustyai_model_observations_total and press Enter. Your model should be listed and reporting observed inferences.
6. Optional: Select a time range from the list above the graph. For example, select 5m.

Procedure

1. Open a new terminal window.

2. Follow these steps to log in to your OpenShift cluster:

   1. In the upper-right corner of the OpenShift web console, click your user name and select Copy login command.
   2. After you have logged in, click Display token.

   3. Copy the Log in with this token command and paste it in the OpenShift CLI (oc).

      $ oc login --token=<token> --server=<openshift_cluster_url>

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3. In the OpenShift CLI (oc), get the route to the TrustyAI service:

   TRUSTY_ROUTE=https://$(oc get route/trustyai-service --template={{.spec.host}})

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4. To examine TrustyAI’s model metadata, query the /info endpoint:

   curl -H "Authorization: Bearer $TOKEN" $TRUSTY_ROUTE/info | jq ".[0].data"

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   This outputs a JSON file containing the following information for each model:

   • The names, data types, and positions of input fields and output fields.
   • The observed field values.
   • The total number of input-output pairs observed.

5. Use POST /info/names to apply name mappings to the fields, similar to the following example.

   Change the model-name, original-name, and Prediction values to those used in your model. Change the New name values to the labels that you want to use.

   curl -sk -H "Authorization: Bearer $TOKEN" -X POST --location $TRUSTY_ROUTE/info/names \
     -H "Content-Type: application/json"   \
     -d "{
       \"modelId\": \"model-name\",
       \"inputMapping\":
         {
           \"original-name-0\": \"New name 0\",
           \"original-name-1\": \"New name 1\",
           \"original-name-2\": \"New name 2\",
           \"original-name-3\": \"New name 3\",
         },
       \"outputMapping\": {
         \"predict-0\": \"Prediction 0\"
       }
     }"

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   For another example, see https://github.com/trustyai-explainability/odh-trustyai-demos/blob/main/2-BiasMonitoring/kserve-demo/scripts/apply_name_mapping.sh.