Trained model api (kubeflow#1009)

* trained model api

* remove frameworks' version information

pkg/apis/serving/v1beta1/trained_model.go

@@ -0,0 +1,40 @@
+package v1beta1
+
+import (
+	"k8s.io/apimachinery/pkg/api/resource"
+	metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
+)
+
+// +k8s:openapi-gen=true
+// +kubebuilder:object:root=true
+type TrainedModel struct {
+	metav1.TypeMeta   `json:",inline"`
+	metav1.ObjectMeta `json:"metadata,omitempty"`
+	Spec              TrainedModelSpec   `json:"spec,omitempty"`
+	Status            TrainedModelStatus `json:"status,omitempty"`
+}
+
+// TrainedModelList contains a list of TrainedModel
+// +kubebuilder:object:root=true
+type TrainedModelList struct {
+	metav1.TypeMeta `json:",inline"`
+	metav1.ListMeta `json:"metadata,omitempty"`
+	// +listType=set
+	Items []TrainedModel `json:"items"`
+}
+
+type TrainedModelSpec struct {
+	// Required field for parent inference service
+	InferenceService string `json:"inferenceService"`
+	// Predictor model spec
+	PredictorModel ModelSpec `json:"predictorModel"`
+}
+type ModelSpec struct {
+	// Storage URI for the model repository
+	StorageURI string `json:"storageUri"`
+	// Machine Learning <framework name>
+	// The values could be: "tensorflow","pytorch","sklearn","onnx","xgboost", "myawesomeinternalframework" etc.
+	Framework string `json:"framework"`
+	// Maximum memory this model will consume, this field is used to decide if a model server has enough memory to load this model.
+	Memory resource.Quantity `json:"memory,omitempty"`
+}

pkg/apis/serving/v1beta1/trained_model_status.go

@@ -0,0 +1,13 @@
+package v1beta1
+
+import (
+	duckv1beta1 "knative.dev/pkg/apis/duck/v1beta1"
+)
+
+type TrainedModelStatus struct {
+	// Condition for "Deployed"
+	duckv1beta1.Status `json:",inline"`
+	// Addressable endpoint for the deployed trained model
+	// http://<inferenceservice.metadata.name>/v1/models/<trainedmodel>.metadata.name
+	Address *duckv1beta1.Addressable `json:"address,omitempty"`
+}