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Example model explanations with Seldon and v2 Protocol - Incubating

In this notebook we will show examples that illustrate how to explain models using [MLServer] (

MLServer is a Python server for your machine learning models through a REST and gRPC interface, fully compliant with KFServing’s v2 Dataplane spec.

Running this Notebook

This should install the required package dependencies, if not please also install:

  • install and configure mc, follow the relevant section in this link

  • run this jupyter notebook in conda environment

    $ conda create --name python3.8-example python=3.8 -y
    $ conda activate python3.8-example
    $ pip install jupyter
    $ jupyter notebook
  • instal requirements

  • alibi package

  • sklearn

[ ]:
!pip install sklearn alibi

Setup Seldon Core

Follow the instructions to Setup Cluster with Ambassador Ingress and Install Seldon Core.

Then port-forward to that ingress on localhost:8003 in a separate terminal either with:

  • Ambassador: kubectl port-forward $(kubectl get pods -n seldon -l -o jsonpath='{.items[0]}') -n seldon 8003:8080

  • Istio: kubectl port-forward $(kubectl get pods -l istio=ingressgateway -n istio-system -o jsonpath='{.items[0]}') -n istio-system 8003:8080

Setup MinIO

Use the provided notebook to install Minio in your cluster and configure mc CLI tool. Instructions also online.

Train iris model using sklearn

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import os
import shutil

from joblib import dump
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

Train model

[ ]:
iris_data = load_iris()

clf = LogisticRegression(solver="liblinear", multi_class="ovr"),

Save model

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modelpath = "/tmp/sklearn_iris"
if os.path.exists(modelpath):
modelfile = os.path.join(modelpath, "model.joblib")

dump(clf, modelfile)

Create AnchorTabular explainer

Create explainer artifact

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from alibi.explainers import AnchorTabular

explainer = AnchorTabular(clf.predict, feature_names=iris_data.feature_names), disc_perc=(25, 50, 75))

Save explainer

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explainerpath = "/tmp/iris_anchor_tabular_explainer_v2"
if os.path.exists(explainerpath):

Install dependencies to pack the enviornment for deployment

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pip install conda-pack mlserver==0.6.0.dev2 mlserver-alibi-explain==0.6.0.dev2

Pack enviornment

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import conda_pack

env_file_path = os.path.join(explainerpath, "environment.tar.gz")

Copy artifacts to object store (minio)

Configure mc to access the minio service in the local kind cluster

note: make sure that minio ip is reflected properly below, run: - kubectl get service -n minio-system - mc config host add minio-seldon [ip] minioadmin minioadmin

[ ]:
target_bucket = "minio-seldon/models"
os.system(f"mc rb --force {target_bucket}")
os.system(f"mc mb {target_bucket}")
os.system(f"mc cp --recursive {modelpath} {target_bucket}")
os.system(f"mc cp --recursive {explainerpath} {target_bucket}")

Deploy to local kind cluster

Create deployment CRD

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%%writefile iris-with-explainer-v2.yaml
kind: SeldonDeployment
  name: iris
  protocol: kfserving  # Activate v2 protocol / mlserver usage
  name: iris
  annotations: "100000"
  - graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: s3://models/sklearn_iris
      envSecretRefName: seldon-rclone-secret
      name: classifier
      type: AnchorTabular
      modelUri: s3://models/iris_anchor_tabular_explainer_v2
      envSecretRefName: seldon-rclone-secret
    name: default
    replicas: 1


!kubectl apply -f iris-with-explainer-v2.yaml created
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!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=iris -o jsonpath='{.items[0]}')

Test explainer

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!pip install numpy requests
[ ]:
import json

import numpy as np
import requests
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endpoint = "http://localhost:8003/seldon/seldon/iris-explainer/default/v2/models/iris-default-explainer/infer"

test_data = np.array([[5.964, 4.006, 2.081, 1.031]])

inference_request = {
    "parameters": {"content_type": "np"},
    "inputs": [
            "name": "explain",
            "shape": test_data.shape,
            "datatype": "FP32",
            "data": test_data.tolist(),
            "parameters": {"content_type": "np"},
response =, json=inference_request)

explanation = json.loads(response.json()["outputs"][0]["data"])
print("Anchor: %s" % (" AND ".join(explanation["data"]["anchor"])))
print("Precision: %.2f" % explanation["data"]["precision"])
print("Coverage: %.2f" % explanation["data"]["coverage"])
[ ]:
!kubectl delete -f iris-with-explainer-v2.yaml
[ ]: