# Canary Promotion

This demo helps you learn about:

* Deploying a pretrained sklearn iris model
* Load testing the model
* Observing requests and metrics
* Deploying a canary XGBoost model
* Load testing the canary model
* Observing requests and metrics for both models
* Promoting the canary model

### Iris Model

Iris is the genus of flower which contains three species, namely setosa, versicolor, and virginica. This demo is based on iris classification model based on flower properties like sepal length, sepal width, petal length, and petal width. The species are also the classes that are used for the classification.

### Create a Seldon ML Pipeline

1. In the **Overview** page, select **Create new deployment**.
2. In the **Deployment Creation Wizard**, enter the deployment details as follows:

   * Name: `iris-classifier`
   * Namespace: `seldon`
   * Type: `Seldon ML Pipeline`

   ![Deployment details](/files/3Qg1mUlbbbKVRhL3NPDD)
3. Configure the default predictor as follows:

   * Runtime: `Scikit Learn`
   * Model URI: `gs://seldon-models/scv2/samples/mlserver_1.6.0/iris-sklearn`
   * Model Project: `default`
   * Storage Secret: (leave blank/none)

   ![Default predictor spec](/files/2eAJK8xtIwlQvbrhljc9)
4. Click **Next** for the remaining steps, then click **Launch**.

Afer your deployment is launched successfully, the status of the deployment reads `Available` in the **Overview** page.

### Start Load Test

1. Click the deployment that you created. The **Dashboard** page for the deployment is displayed.
2. In the **Requests Monitor** section, click **Start Load Test** to start a load test.
3. In the **Load Test Wizard** configure the following details:

   * Connections(total): `1`
   * Load Parameter: `Duration(seconds)`
   * Value: `120`
   * Json payload:

     ```json
     {
         "inputs": [
             {
                 "name": "predict",
                 "data": [
                     0.38606369295833043,
                     0.006894049558299753,
                     0.6104082981607108,
                     0.3958954239450676
                 ],
                 "datatype": "FP64",
                 "shape": [
                     1,
                     4
                 ]
             }
         ]
     }
     ```

   ![Load test](/files/DRMLemJO4eeZ7wCutVkX)

After you click **Run Test** it spawns a Kubernetes job that send continuous prediction requests for the specified seconds to the SKLearn model in the deployment.

### Observe requests and metrics

After the load test has started, you can monitor the upcoming requests, their responses and [metrics](/seldon-enterprise-platform/production-environment/observability-alerting.md) in the `Requests` page the deployment. If this doesn't work, consult the [request logging](/seldon-enterprise-platform/production-environment/knative.md) docs section for debugging.

![logs](/files/jZIu0QVmkBMZj9GNB8tr)

You can also see core metrics in the `Dashboard` page.

![metrics](/files/97k8qUddqoUnKBQO0IZa)

### Deploy a Canary model

The next step is to create an XGBoost canary model.

1. In the **Dashboard** of the deployment and click **Add Canary**.
2. In the `Canary Configuration Wizard`, configure the default predictor as follows:

   * Runtime: `XGBoost`
   * Model URI: `gs://seldon-models/xgboost/iris`
   * Model Project: `default`
   * Storage Secret: `(leave blank/none)`
   * Canary Traffic Percentage: `10`

   ![Default predictor spec](/files/HVxLKSv0kdP2G7cyyzBC)
3. Click **Next** for the remaining steps in the wizard and then click **Launch**.

When the canary model is launched successfully, the deployment remains in an `Available` status.

This creates a new canary deployment with the `XGBoost` model and 10% of the traffic is sent to this deployment.

{% hint style="info" %}
**Note**: The deployment status represents the status of the main model. If the canary model is not successfully launched, click the warning icon to see the error message.
{% endhint %}

### Load test the canary model

This time, create a new load test with the canary model running and observe the requests and metrics for both models. You can use either the same Json payload from the previous [load test](#start-load-test) or construct a new one with different values or number of predictions.

{% hint style="warning" %}
**Important**: Remember that roughly 10% of the traffic will be sent to the canary model. If, however, the canary model is not available, all the traffic will be sent to the main model.
{% endhint %}

### Observe requests and metrics for both models

After the load test has started, you can monitor the upcoming requests, their responses and [metrics](/seldon-enterprise-platform/production-environment/observability-alerting.md) in the `Requests` page the deployment. If this doesn't work, consult the [request logging](/seldon-enterprise-platform/production-environment/knative.md) docs section for debugging.

In order to see the requests for the canary model, you need to select the **iris-classifier-canary** predictor and the related **Node** in the **Node Selector** filter of the **Requests** page, as shown on the screenshot. Make a note that the number of requests for the canary model and it is 10% of the total number of requests, as was specified in the canary deployment.

![logs](/files/Rgwt49MFPkmTRY0zS9sV)

You can also see core metrics such as **Live Requests**, and **Resource Monitor** for both models in the **Dashboard** page.

![metrics](/files/13NSaxsTvUwcu2N6IA14)

### Promote the Canary model

After you evaluated the performace of the canary model you can promote the canary model as a main model.

1. In the **Dashboard** page of the deployment and click **Promote Canary**.
2. In the **Promote Canary** dialog, click **Confirm** to promote the canary model to the main model.

When the canary model is promoted successfully, the deployment remains in an `Available` status.

![metrics](/files/rKv2LZ8QwOR9hSDgISxk)


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