Skip to content

scaling.md

Latest commit

 

History

History
193 lines (164 loc) · 5.74 KB

scaling.md

File metadata and controls

193 lines (164 loc) · 5.74 KB

Scaling Replicas

Replica Settings

Replicas settings can be provided at several levels with the most specific taking precedence, from most general to most specific as shown below:

  • .spec.replicas
  • .spec.predictors[].replicas
  • .spec.predictors[].componentSpecs[].replicas

If you use the annotation seldon.io/engine-separate-pod you can also set the number of replicas for the service orchestrator in:

  • .spec.predictors[].svcOrchSpec.replicas

As illustration, a contrived example showing various options is shown below:

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: test-replicas
spec:
  replicas: 1
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier_rest:1.3
          name: classifier
    - spec:
        containers:
        - image: seldonio/mock_classifier_rest:1.3
          name: classifier2
      replicas: 3
    graph:
      endpoint:
        type: REST
      name: classifier
      type: MODEL
      children:
      - name: classifier2
        type: MODEL
        endpoint:
          type: REST
    name: example
    replicas: 2
    traffic: 50
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier_rest:1.3
          name: classifier3
    graph:
      children: []
      endpoint:
        type: REST
      name: classifier3
      type: MODEL
    name: example2
    traffic: 50
  • classfier will have a deployment with 2 replicas as specified by the predictor it is defined within
  • classifier2 will have a deployment with 3 replicas as that is specified in its componentSpec
  • classifier3 will have 1 replica as it takes its value from .spec.replicas

For more details see a worked example for the above replica settings.

Scale replicas

Its is possible to use the kubectl scale command to set the replicas value of the SeldonDeployment. For simple inference graphs this can be an easy way to scale them up and down. For example:

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: seldon-scale
spec:
  replicas: 1  
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier_rest:1.3
          name: classifier
    graph:
      children: []
      endpoint:
        type: REST
      name: classifier
      type: MODEL
    name: example

One can scale this Seldon Deployment up using the command:

kubectl scale --replicas=2 sdep/seldon-scale

For more details you can follow a worked example of scaling.

Autoscaling Seldon Deployments

To autoscale your Seldon Deployment resources you can add Horizontal Pod Template Specifications to the Pod Template Specifications you create. There are two steps:

  1. Ensure you have a resource request for the metric you want to scale on if it is a standard metric such as cpu or memory. This has to be done for every container in the seldondeployment, except for the seldon-container-image and the storage initializer. Some combinations of protocol and server type may spawn additional support containers; resource requests have to be added to those containers as well.
  2. Add a HPA Spec referring to this Deployment.

We presently support the autoscaling/v2beta1 definition in the existing metrics field as well as the autoscaling/v2 definition in the metricsv2 field of the SeldonDeployment hpaSpec. In both cases they will create a K8s autoscaling/v2 HPA which means you will need to be running a Kubernetes cluster of >= 1.23.

To illustrate this we have an example Seldon Deployment below with the v2 definition:

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: seldon-model
spec:
  name: test-deployment
  predictors:
  - componentSpecs:
    - hpaSpec:
        maxReplicas: 3
        metricsv2:
        - resource:
            name: cpu
            target:
              type: Utilization
              averageUtilization: 70
          type: Resource
        minReplicas: 1
      spec:
        containers:
        - image: seldonio/mock_classifier:1.5.0-dev
          imagePullPolicy: IfNotPresent
          name: classifier
          resources:
            requests:
              cpu: '0.5'
        terminationGracePeriodSeconds: 1
    graph:
      children: []
      name: classifier
      type: MODEL
    name: example

The key points here are:

  • We define a CPU request for our container. This is required to allow us to utilize cpu autoscaling in Kubernetes.
  • We define an HPA associated with our componentSpec which scales on CPU when the average CPU is above 70% up to a maximum of 3 replicas.

Once deployed, the HPA resource may take a few minutes to start up. To check status of the HPA resource, kubectl describe hpa -n <podname> may be used.

An example using the v2beta1 definition is shown below:

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: seldon-model
spec:
  name: test-deployment
  predictors:
  - componentSpecs:
    - hpaSpec:
        maxReplicas: 3
        metrics:
        - resource:
            name: cpu
            targetAverageUtilization: 70
          type: Resource
        minReplicas: 1
      spec:
        containers:
        - image: seldonio/mock_classifier:1.5.0-dev
          imagePullPolicy: IfNotPresent
          name: classifier
          resources:
            requests:
              cpu: '0.5'
        terminationGracePeriodSeconds: 1
    graph:
      children: []
      name: classifier
      type: MODEL
    name: example

For worked examples see this notebook.