CIFAR10 Image Classifier Explanations¶

We will use a Tensorflow classifier built on CIFAR10 image dataset which is a 10 class image dataset to show the example of explanation on image data.

Create the InferenceService with Alibi Explainer¶

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "cifar10"
spec:
  predictor:
    tensorflow:
      storageUri: "gs://seldon-models/tfserving/cifar10/resnet32"
      resources:
        requests:
          cpu: 0.1
          memory: 5Gi
        limits:
          memory: 10Gi
  explainer:
    containers:
      - name: kserve-container
        image: kserve/alibi-explainer:v0.12.1
        args:
          - --model_name=cifar10
          - --http_port=8080 
          - --predictor_host=cifar10-predictor.default 
          - --storage_uri=/mnt/models 
          - AnchorImages 
          - --batch_size=40 
          - --stop_on_first=True
        env:
          - name: STORAGE_URI
            value: "gs://kfserving-examples/models/tensorflow/cifar/explainer-0.9.1"
        resources:
          requests:
            cpu: 0.1
            memory: 5Gi
          limits:
            cpu: 1
            memory: 10Gi

Note

The InferenceService resource describes:

A pretrained tensorflow model stored on a Google bucket
An AnchorImage Seldon Alibi Explainer, see the Alibi Docs for further details.

Test on notebook¶

Run this example using the Jupyter notebook.

Once created you will be able to test the predictions:

prediction

And then get an explanation for it:

explanation