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* Update aif example chore: Update aif explainer example. - Bump KServer to 0.13.0, it will bring some library updates, plus, it fixes a few security alerts in this example. - update the scikit-learn package name Signed-off-by: Spolti <[email protected]> * move the local instructions to the README Signed-off-by: Spolti <[email protected]> * empty commit Signed-off-by: Spolti <[email protected]> --------- Signed-off-by: Spolti <[email protected]>
Logistic Regression Model on the German Credit dataset
Build a development docker image
To build a development image first download these files and move them into the server/ folder
- https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data
- https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.doc
First build your docker image by changing directory to kfserving/python and replacing dockeruser with your docker username in the snippet below (running this will take some time).
docker build -t dockeruser/aifserver:latest -f aiffairness.Dockerfile .
Then push your docker image to your dockerhub repo (this will take some time)
docker push dockeruser/aifserver:latest
Once your docker image is pushed you can pull the image from dockeruser/aifserver:latest when deploying an inferenceservice by specifying the image in the yaml file.
Running the Container Image locally
Replace the CMD argument with the following command to run the container locally.
CMD ["python", "model.py",
"-m", "aifserver",
"--predictor_host", "german-credit-predictor-default.default.svc.cluster.local",
"--model_name", "german-credit",
"--feature_names", "age", "sex", "credit_history=Delay", "credit_history=None/Paid", "credit_history=Other", "savings=500+", "savings=<500", "savings=Unknown/None", "employment=1-4 years", "employment=4+ years", "employment=unemployed",
"--label_names", "credit",
"--favorable_label", "1",
"--unfavorable_label", "2",
"--privileged_groups", "{\"age\": 1}",
"--unprivileged_groups", "{\"age\": 0}"]
Once the image is built, you can run the request to test it:
# make sure you are in the example's root directory where the `query_bias.py` script and the `input.json` file are located.
python query_bias.py http://localhost:8080/v1/models/german-credit:predict input.json