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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] (https://github.com/SeldonIO/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

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!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 app.kubernetes.io/name=ambassador -o jsonpath='{.items[0].metadata.name}') -n seldon 8003:8080

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

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iris_data = load_iris()

clf = LogisticRegression(solver="liblinear", multi_class="ovr")
clf.fit(iris_data.data, iris_data.target)

Save model¶

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modelpath = "/tmp/sklearn_iris"
if os.path.exists(modelpath):
    shutil.rmtree(modelpath)
os.makedirs(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)
explainer.fit(iris_data.data, disc_perc=(25, 50, 75))

Save explainer¶

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explainerpath = "/tmp/iris_anchor_tabular_explainer_v2"
if os.path.exists(explainerpath):
    shutil.rmtree(explainerpath)
explainer.save(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")
conda_pack.pack(
    output=str(env_file_path),
    force=True,
    verbose=True,
    ignore_editable_packages=False,
    ignore_missing_files=True,
)

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

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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
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: iris
spec:
  protocol: kfserving  # Activate v2 protocol / mlserver usage
  name: iris
  annotations:
    seldon.io/rest-timeout: "100000"
  predictors:
  - graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: s3://models/sklearn_iris
      envSecretRefName: seldon-rclone-secret
      name: classifier
    explainer:
      type: AnchorTabular
      modelUri: s3://models/iris_anchor_tabular_explainer_v2
      envSecretRefName: seldon-rclone-secret
    name: default
    replicas: 1

Deploy¶

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!kubectl apply -f iris-with-explainer-v2.yaml

seldondeployment.machinelearning.seldon.io/iris created

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!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=iris -o jsonpath='{.items[0].metadata.name}')

Test explainer¶

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!pip install numpy requests

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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 = requests.post(endpoint, 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"])

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!kubectl delete -f iris-with-explainer-v2.yaml

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