Added docs for raw deployment autoscaling.

Signed-off-by: Andrews Arokiam <[email protected]>

docs/modelserving/autoscaling/autoscaling.md

@@ -1,6 +1,8 @@
 # Autoscale InferenceService with inference workload
 
-## InferenceService with target concurrency
+## Autoscaler for kserve's Serverless
+
+### InferenceService with target concurrency
 
 ### Create `InferenceService`
 
@@ -492,4 +494,89 @@ This allows more flexibility in terms of the autoscaling configuration. In a typ
   - mnist
  ```
 Apply the `autoscale-adv.yaml` to create the Autoscale InferenceService.
-The default for scaleMetric is `concurrency` and possible values are `concurrency`, `rps`, `cpu` and `memory`.
+The default for scaleMetric is `concurrency` and possible values are `concurrency`, `rps`, `cpu` and `memory`.
+
+## Autoscaler for Kserve's Raw Deployment Mode
+
+KServe supports `RawDeployment` mode to enable `InferenceService` deployment with Kubernetes resources [`Deployment`](https://kubernetes.io/docs/concepts/workloads/controllers/deployment), [`Service`](https://kubernetes.io/docs/concepts/services-networking/service), [`Ingress`](https://kubernetes.io/docs/concepts/services-networking/ingress) and [`Horizontal Pod Autoscaler`](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale). Comparing to serverless deployment it unlocks Knative limitations such as mounting multiple volumes, on the other hand `Scale down and from Zero` is not supported in `RawDeployment` mode.
+
+### HPA in Raw Deployment
+
+When using Kserve with the `RawDeployment` mode, Knative is not installed. In this mode, if you deploy an `InferenceService`, Kserve uses **Kubernetes’ Horizontal Pod Autoscaler (HPA)** for autoscaling instead of **Knative Pod Autoscaler (KPA)**. For more information about Kserve's autoscaler, you can refer [`this`](https://kserve.github.io/website/master/modelserving/v1beta1/torchserve/#knative-autoscaler)
+
+
+=== "Old Schema"
+
+ ```yaml
+ apiVersion: "serving.kserve.io/v1beta1"
+ kind: "InferenceService"
+ metadata:
+  name: "sklearn-iris-hpa"
+  annotations:
+  serving.kserve.io/deploymentMode: RawDeployment
+  serving.kserve.io/autoscalerClass: hpa
+  serving.kserve.io/metric: cpu
+  serving.kserve.io/targetUtilizationPercentage: "80"
+ spec:
+  predictor:
+  sklearn:
+  storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
+ ```
+
+=== "New Schema"
+
+ ```yaml
+ apiVersion: "serving.kserve.io/v1beta1"
+ kind: "InferenceService"
+ metadata:
+  name: "sklearn-iris-hpa"
+  annotations:
+  serving.kserve.io/deploymentMode: RawDeployment
+  serving.kserve.io/autoscalerClass: hpa
+  serving.kserve.io/metric: cpu
+  serving.kserve.io/targetUtilizationPercentage: "80"
+ spec:
+  predictor:
+  model:
+  modelFormat:
+  name: sklearn
+  storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
+ ```
+
+### Disable HPA in Raw Deployment
+
+If you want to control the scaling of the deployment created by KServe inference service with an external tool like [`KEDA`](https://keda.sh/). You can disable KServe's creation of the **HPA** by replacing **external** value with autoscaler class annotaion that should be disable the creation of HPA
+
+=== "Old Schema"
+
+ ```yaml
+ apiVersion: "serving.kserve.io/v1beta1"
+ kind: "InferenceService"
+ metadata:
+  annotations:
+  serving.kserve.io/deploymentMode: RawDeployment
+  serving.kserve.io/autoscalerClass: external
+  name: "sklearn-iris"
+ spec:
+  predictor:
+  sklearn:
+  storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
+ ```
+
+=== "New Schema"
+
+ ```yaml
+ apiVersion: "serving.kserve.io/v1beta1"
+ kind: "InferenceService"
+ metadata:
+  annotations:
+  serving.kserve.io/deploymentMode: RawDeployment
+  serving.kserve.io/autoscalerClass: external
+  name: "sklearn-iris"
+ spec:
+  predictor:
+  model:
+  modelFormat:
+  name: sklearn
+  storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
+ ```