Canary Promotion

Launch a serverless SKLearn model and update via canary

In this demo you will:

  • Deploy a sklearn KfServing Iris Classifer
  • Run a load test to show knative autoscaling
  • Create a canary XGBoost model
  • Promote the Canary
  • Delete the model

From the Deploy UI create a server:


Use the model url:


Run a Load Test

When the deployment is ready click on it and “Start a Load Test”.


Set the duration to 60 secs and the number of connections to 10.

Use the request.json file in this folder:

  "instances": [
    [6.8,  2.8,  4.8,  1.4],
    [6.0,  3.4,  4.5,  1.6]

You should see pod counts scale up and then down, something like:


Adding a Canary

Now follow the “Add Canary” wizard and add an XGBoost canary:


Use the XGBoost Iris model whose saved Booster is stored at:


One the canary is running you can rerun the load test and see traffic split between both.


To promote canary press the “Promote Canary” button


Delete the Deployment

Finally, you can delete the model.


Last modified September 5, 2020: Update Demos section (91a072d)