Prepackaged Model Servers

Seldon provides several prepacked servers you can use to deploy trained models:

For these servers you only need the location of the saved model in a local filestore, Google bucket or S3 bucket. An example manifest with an sklearn server is shown below:

apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: sklearn
spec:
  name: iris
  predictors:
  - graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/sklearn/iris
      name: classifier
    name: default
    replicas: 1

The modelUri specifies the bucket containing the saved model, in this case gs://seldon-models/sklearn/iris.

modeluri supports the following three object storage providers:

  • Google Cloud Storage (using gs://)
  • S3-compatible (using s3://)
  • Azure Blob storage (using https://(.+?).blob.core.windows.net/(.+))

When using S3-compatible object storage provider, you can provide access credential and custom endpoint by creating a Secret object:

apiVersion: v1
kind: Secret
metadata:
  name: s3-secret
type: Opaque
data:
  AWS_ACCESS_KEY_ID: XXXX
  AWS_SECRET_ACCESS_KEY: XXXX
  S3_ENDPOINT: XXXX

You can create a Secret object from command line by

kubectl create secret generic s3-secret --from-literal=S3_ENDPOINT='XXXX' --from-literal=AWS_ACCESS_KEY_ID='XXXX' --from-literal=AWS_SECRET_ACCESS_KEY='XXXX'

and you can read more about interacting with Secret object.

And reference the Secret using envSecretRefName as shown below.

apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: sklearn
spec:
  name: iris
  predictors:
  - graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: s3://seldon-models/sklearn/iris
      envSecretRefName: s3-secret
      name: classifier
    name: default
    replicas: 1

Alternatively, you can also create a ServiceAccount and attach a differently formatted Secret to it similar to how kfserving does it. See kfserving documentation on this topic. Supported annotation prefix includes serving.kubeflow.org and machinelearning.seldon.io.

For GCP/GKE, go to gcloud console and create a key as json and export as a file. Then create a secret from the file using:

kubectl create secret generic user-gcp-sa --from-file=gcloud-application-credentials.json=<LOCALFILE>

The file in the secret needs to be called gcloud-application-credentials.json (the name can be configured in the seldon configmap, visible in kubectl get cm -n seldon-system seldon-config -o yaml).

Then create a service account to reference the secret:

apiVersion: v1
kind: ServiceAccount
metadata:
  name: user-gcp-sa
secrets:
  - name: user-gcp-sa

This can then be referenced in the SeldonDeployment manifest by setting serviceAccountName: user-gcp-sa at the same level as m̀odelUri e.g.

apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: sklearn
spec:
  name: iris
  predictors:
  - graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/sklearn/iris
      serviceAccountName: user-gcp-sa
      name: classifier
    name: default
    replicas: 1

If you want to customize the resources for the server you can add a skeleton Container with the same name to your podSpecs, e.g.

apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: sklearn
spec:
  name: iris
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - name: classifier
          resources:
            requests:
              memory: 50Mi
    graph:
      children: []
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/sklearn/iris
      name: classifier
    name: default
    replicas: 1

The image name and other details will be added when this is deployed automatically.

Next steps:

If your use case does not fall into the above standard servers then you can create your own component using our wrappers.