s[0].hostname}')

Now register the model deployer:Note: If you chose to configure your own custom credentials to authenticate to the persistent storage service where models are stored, as covered in the Advanced: Configuring a Custom Seldon Core Secret section, you will need to specify a ZenML secret reference when you configure the Seldon Core model deployer below:

zenml model-deployer register seldon_deployer --flavor=seldon \

--kubernetes_context=<KUBERNETES-CONTEXT> \

--kubernetes_namespace=<KUBERNETES-NAMESPACE> \

--base_url=http://$INGRESS_HOST \

--secret=<zenml-secret-name>

# Register the Seldon Core Model Deployer

zenml model-deployer register seldon_deployer --flavor=seldon \

--kubernetes_context=<KUBERNETES-CONTEXT> \

--kubernetes_namespace=<KUBERNETES-NAMESPACE> \

--base_url=http://$INGRESS_HOST \

We can now use the model deployer in our stack.

zenml stack update seldon_stack --model-deployer=seldon_deployer

See the seldon_model_deployer_step for an example of using the Seldon Core Model Deployer to deploy a model inside a ZenML pipeline step.

Configuration

Within the SeldonDeploymentConfig you can configure:

model_name: the name of the model in the Seldon cluster and in ZenML.

replicas: the number of replicas with which to deploy the model

implementation: the type of Seldon inference server to use for the model. The implementation type can be one of the following: TENSORFLOW_SERVER, SKLEARN_SERVER, XGBOOST_SERVER, custom.

parameters: an optional list of parameters (SeldonDeploymentPredictorParameter) to pass to the deployment predictor in the form of:nametypevalue

resources: the resources to be allocated to the model. This can be configured by passing a SeldonResourceRequirements object with the requests and limits properties. The values for these properties can be a dictionary with the cpu and memory keys. The values for these keys can be a string with the amount of CPU and memory to be allocated to the model.