Seldon Core extends Kubernetes with its own custom resource SeldonDeployment where you can define your runtime inference graph made up of models and other components that Seldon will manage.
A SeldonDeployment is a JSON or YAML file that allows you to define your graph of component images and the resources each of those images will need to run (using a Kubernetes PodTemplateSpec). The parts of a SeldonDeployment are shown below:
A minimal example for a single model, this time in YAML, is shown below:
apiVersion: machinelearning.seldon.io/v1alpha2 kind: SeldonDeployment metadata: name: seldon-model spec: name: test-deployment predictors: - componentSpecs: - spec: containers: - name: classifier image: seldonio/mock_classifier:1.0 graph: children:  endpoint: type: REST name: classifier type: MODEL name: example replicas: 1
The key components are:
- A list of Predictors, each with a specification for the number of replicas.
- Each defines a graph and its set of deployments. Multiple predictors is useful when you want to split traffic between a main graph and a canary or for other production rollout scenarios.
- For each predictor a list of componentSpecs. Each componentSpec is a Kubernetes PodTemplateSpec which Seldon will build into a Kubernetes Deployment. Place here the images from your graph and their requirements, e.g. Volumes, ImagePullSecrets, Resources Requests etc.
- A graph specification that describes how your components are joined together.
To understand the inference graph definition in detail see here