Allow v2 as protocol name

doc/source/analytics/explainers.md

@@ -82,13 +82,13 @@ If you were port forwarding to Ambassador on localhost:8003 then the API call wo
 http://localhost:8003/seldon/seldon/income-explainer/default/api/v1.0/explain
 ```
 
-The explain method is also supported for tensorflow and v2 kfserving protocols. The full list of endpoint URIs is:
+The explain method is also supported for tensorflow and v2 protocols. The full list of endpoint URIs is:
 
 | Protocol | URI |
 | ------ | ----- |
 | seldon | `http://<host>/<ingress-path>/api/v1.0/explain` |
 | tensorflow | `http://<host>/<ingress-path>/v1/models/<model-name>:explain` |
-| kfserving | `http://<host>/<ingress-path>/v2/models/<model-name>/infer` |
+| v2 | `http://<host>/<ingress-path>/v2/models/<model-name>/infer` |
 
 
 Note: for `tensorflow` protocol we support similar non-standard extension as for the [prediction API](../graph/protocols.md#rest-and-grpc-tensorflow-protocol), `http://<host>/<ingress-path>/v1/models/:explain`.

doc/source/analytics/logging.md

@@ -71,7 +71,7 @@ spec:
       - name: LOGGER_KAFKA_TOPIC
         value: seldon
     replicas: 1
-  protocol: kfserving
+  protocol: v2
 
 ```
 

doc/source/graph/protocols.md

@@ -6,7 +6,7 @@ Seldon Core supports the following data planes:
 
  * [REST and gRPC Seldon protocol](#rest-and-grpc-seldon-protocol)
  * [REST and gRPC Tensorflow Serving Protocol](#rest-and-grpc-tensorflow-protocol)
- * [REST and gRPC V2 KFServing Protocol](#v2-kfserving-protocol)
+ * [REST and gRPC V2 Protocol](#v2-protocol)
 
 ## REST and gRPC Seldon Protocol
 
@@ -40,17 +40,17 @@ General considerations:
   * The name of the model in the `graph` section of the SeldonDeployment spec must match the name of the model loaded onto the Tensorflow Server.
 
 
-## V2 KFServing Protocol 
+## V2 Protocol 
 
 Seldon has collaborated with the [NVIDIA Triton Server
-Project](https://github.com/triton-inference-server/server) and the [KFServing
-Project](https://github.com/kubeflow/kfserving) to create a new ML inference
+Project](https://github.com/triton-inference-server/server) and the [KServe
+Project](https://github.com/kserve) to create a new ML inference
 protocol.
 The core idea behind this joint effort is that this new protocol will become
 the standard inference protocol and will be used across multiple inference
 services.
 
-In Seldon Core, this protocol can be used by specifying `protocol: kfserving` on
+In Seldon Core, this protocol can be used by specifying `protocol: v2` on
 your `SeldonDeployment`. 
 For example, 
 
@@ -61,7 +61,7 @@ metadata:
   name: sklearn
 spec:
   name: iris-predict
-  protocol: kfserving
+  protocol: v2
   predictors:
   - graph:
       children: []
@@ -75,7 +75,7 @@ spec:
     name: default
 ```
 
-At present, the `kfserving` protocol is only supported in a subset of
+At present, the `v2` protocol is only supported in a subset of
 pre-packaged inference servers.
 In particular,
 
@@ -86,4 +86,4 @@ In particular,
 | [XGBOOST_SERVER](../servers/xgboost.md) | ✅  | [Seldon MLServer](https://github.com/seldonio/mlserver) |
 | [MLFLOW_SERVER](../servers/mlflow.md) | ✅  | [Seldon MLServer](https://github.com/seldonio/mlserver) |
 
-You can try out the `kfserving` in [this example notebook](../examples/protocol_examples.html). 
+You can try out the `v2` in [this example notebook](../examples/protocol_examples.html). 

doc/source/graph/svcorch.md

@@ -17,7 +17,7 @@ At present, we support the following protocols:
 | --- | --- | --- | --- |
 | Seldon | `seldon` | [OpenAPI spec for Seldon](https://docs.seldon.io/projects/seldon-core/en/latest/reference/apis/openapi.html) |
 | Tensorflow | `tensorflow` | [REST API](https://www.tensorflow.org/tfx/serving/api_rest) and [gRPC API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto) reference |
-| KFServing | `kfserving` | [KFServing Dataplane reference](https://github.com/kubeflow/kfserving/tree/master/docs/predict-api/v2) |
+| V2 | `v2` | [KServe Dataplane reference](https://github.com/kserve/kserve/tree/master/docs/predict-api/v2) |
 
 These protocols are supported by some of our pre-packaged servers out of the
 box.

doc/source/python/python_component.md

@@ -464,7 +464,7 @@ class Model:
 ```
 
 #### Validation
-Output of developer-defined `metadata` method will be validated to follow the [kfserving dataplane proposal](https://github.com/kubeflow/kfserving/blob/master/docs/predict-api/v2/required_api.md#model-metadata) protocol, see [this](https://github.com/SeldonIO/seldon-core/issues/1638) GitHub issue for details:
+Output of developer-defined `metadata` method will be validated to follow the [V2 dataplane proposal](https://github.com/kserve/kfserve/blob/master/docs/predict-api/v2/required_api.md#model-metadata) protocol, see [this](https://github.com/SeldonIO/seldon-core/issues/1638) GitHub issue for details:
 ```javascript
 $metadata_model_response =
 {

doc/source/reference/apis/metadata.md

@@ -183,7 +183,7 @@ Example response:
 ```
 
 
-## Deep dive: SeldonMessage and kfserving metadata reference
+## Deep dive: SeldonMessage and kfserving V2 metadata reference
 
 You can define inputs/outputs of your model metadata using one of two formats:
 - `v1` format that closely correlates to the current structure of `SeldonMessage`
@@ -300,9 +300,9 @@ custom:
 ```
 
 
-### kfserving TensorMetadata
+### V2 TensorMetadata
 
-You can easily define metadata for your models that is compatible with [kfserving dataplane proposal](https://github.com/kubeflow/kfserving/blob/master/docs/predict-api/v2/required_api.md#model-metadata) specification.
+You can easily define metadata for your models that is compatible with [kfserving V2 dataplane proposal](https://github.com/kubeflow/kfserving/blob/master/docs/predict-api/v2/required_api.md#model-metadata) specification.
 ```javascript
 $metadata_model_response =
 {

doc/source/reference/apis/prediction.md

@@ -117,7 +117,7 @@ message SeldonMessageMetadata
   string messagetype = 1;
   google.protobuf.Value schema = 2;
 
-  // KFserving tesnor metadata fields
+  // v2 tensor metadata fields
   string name = 3;
   string datatype = 4;
   repeated int64 shape = 5;

doc/source/servers/mlflow.md

@@ -85,16 +85,10 @@ notebook](../examples/server_examples.html#Serve-MLflow-Elasticnet-Wines-Model)
 or check our [talk at the Spark + AI Summit
 2019](https://www.youtube.com/watch?v=D6eSfd9w9eA).
 
-## V2 KFServing protocol [Incubating]
-
-.. Warning:: 
-  Support for the V2 KFServing protocol is still considered an incubating
-  feature.
-  This means that some parts of Seldon Core may still not be supported (e.g.
-  tracing, graphs, etc.).
+## V2 protocol
 
 The MLFlow server can also be used to expose an API compatible with the [V2
-KFServing Protocol](../graph/protocols.md#v2-kfserving-protocol).
+Protocol](../graph/protocols.md#v2-protocol).
 Note that, under the hood, it will use the [Seldon
 MLServer](https://github.com/SeldonIO/MLServer) runtime.
 
@@ -142,8 +136,8 @@ $ gsutil cp -r ../model gs://seldon-models/test/elasticnet_wine_<uuid>
 ```
 
 - deploy the model to seldon-core
-In order to enable support for the V2 KFServing protocol, it's enough to
-specify the `protocol` of the `SeldonDeployment` to use `kfserving`.
+In order to enable support for the V2 protocol, it's enough to
+specify the `protocol` of the `SeldonDeployment` to use `v2`.
 For example,
 
 ```yaml
@@ -152,7 +146,7 @@ kind: SeldonDeployment
 metadata:
   name: mlflow
 spec:
-  protocol: kfserving  # Activate the v2 protocol
+  protocol: v2  # Activate the v2 protocol
   name: wines
   predictors:
     - graph:

doc/source/servers/sklearn.md

@@ -82,21 +82,15 @@ Acceptable values for the `method` parameter are `predict`, `predict_proba`,
 `decision_function`.
 
 
-## V2 KFServing protocol [Incubating]
-
-.. Warning:: 
-  Support for the V2 KFServing protocol is still considered an incubating
-  feature.
-  This means that some parts of Seldon Core may still not be supported (e.g.
-  tracing, graphs, etc.).
+## V2 protocol
 
 The SKLearn server can also be used to expose an API compatible with the [V2
-KFServing Protocol](../graph/protocols.md#v2-kfserving-protocol).
+V2 Protocol](../graph/protocols.md#v2-protocol).
 Note that, under the hood, it will use the [Seldon
 MLServer](https://github.com/SeldonIO/MLServer) runtime.
 
-In order to enable support for the V2 KFServing protocol, it's enough to
-specify the `protocol` of the `SeldonDeployment` to use `kfserving`.
+In order to enable support for the V2 protocol, it's enough to
+specify the `protocol` of the `SeldonDeployment` to use `v2`.
 For example,
 
 ```yaml
@@ -106,7 +100,7 @@ metadata:
   name: sklearn
 spec:
   name: iris-predict
-  protocol: kfserving # Activate the V2 protocol
+  protocol: v2 # Activate the V2 protocol
   predictors:
   - graph:
       children: []

doc/source/servers/tempo.md

@@ -4,39 +4,3 @@
 
 For more details see the [Tempo documentation](https://tempo.readthedocs.io/en/latest/).
 
-An example Tempo model yaml for Seldon Core is shown below:
-
-```python
-apiVersion: machinelearning.seldon.io/v1
-kind: SeldonDeployment
-metadata:
-  annotations:
-    seldon.io/tempo-description: ''
-    seldon.io/tempo-model: '{"model_details": {"name": "numpyro-divorce", "local_folder":
-      "/home/clive/work/mlops/fork-tempo/docs/examples/custom-model/artifacts", "uri":
-      "s3://tempo/divorce", "platform": "custom", "inputs": {"args": [{"ty": "numpy.ndarray",
-      "name": "marriage"}, {"ty": "numpy.ndarray", "name": "age"}]}, "outputs": {"args":
-      [{"ty": "numpy.ndarray", "name": null}]}, "description": ""}, "protocol": "tempo.kfserving.protocol.KFServingV2Protocol",
-      "runtime_options": {"runtime": "tempo.seldon.SeldonKubernetesRuntime", "docker_options":
-      {"defaultRuntime": "tempo.seldon.SeldonDockerRuntime"}, "k8s_options": {"replicas":
-      1, "minReplicas": null, "maxReplicas": null, "authSecretName": "minio-secret",
-      "serviceAccountName": null, "defaultRuntime": "tempo.seldon.SeldonKubernetesRuntime",
-      "namespace": "production"}, "ingress_options": {"ingress": "tempo.ingress.istio.IstioIngress",
-      "ssl": false, "verify_ssl": true}}}'
-  labels:
-    seldon.io/tempo: 'true'
-  name: numpyro-divorce
-  namespace: production
-spec:
-  predictors:
-  - graph:
-      envSecretRefName: minio-secret
-      implementation: TEMPO_SERVER
-      modelUri: s3://tempo/divorce
-      name: numpyro-divorce
-      serviceAccountName: tempo-pipeline
-      type: MODEL
-    name: default
-    replicas: 1
-  protocol: kfserving
-```

doc/source/servers/triton.md

@@ -12,7 +12,7 @@ kind: SeldonDeployment
 metadata:
   name: triton
 spec:
-  protocol: kfserving
+  protocol: v2
   predictors:
   - graph:
       implementation: TRITON_SERVER

doc/source/servers/xgboost.md

@@ -46,21 +46,15 @@ spec:
 You can try out a [worked notebook](../examples/server_examples.html) with a
 similar example.
 
-## V2 KFServing protocol [Incubating]
-
-.. Warning:: 
-  Support for the V2 KFServing protocol is still considered an incubating
-  feature.
-  This means that some parts of Seldon Core may still not be supported (e.g.
-  tracing, graphs, etc.).
+## V2 protocol
 
 The XGBoost server can also be used to expose an API compatible with the [V2
-KFServing protocol](../graph/protocols.md#v2-kfserving-protocol).
+protocol](../graph/protocols.md#v2-protocol).
 Note that, under the hood, it will use the [Seldon
 MLServer](https://github.com/SeldonIO/MLServer) runtime.
 
-In order to enable support for the V2 KFServing protocol, it's enough to
-specify the `protocol` of the `SeldonDeployment` to use `kfserving`.
+In order to enable support for the V2 protocol, it's enough to
+specify the `protocol` of the `SeldonDeployment` to use `v2`.
 For example,
 
 ```yaml
@@ -70,7 +64,7 @@ metadata:
   name: xgboost
 spec:
   name: iris
-  protocol: kfserving # Activate the V2 protocol
+  protocol: v2 # Activate the V2 protocol
   predictors:
   - graph:
       children: []

executor/api/constants.go

@@ -2,6 +2,7 @@ package api
 
 const ProtocolSeldon = "seldon"
 const ProtocolTensorflow = "tensorflow"
+const ProtocolV2 = "v2"
 const ProtocolKFServing = "kfserving"
 
 const TransportRest = "rest"

executor/api/rest/client.go

@@ -263,7 +263,7 @@ func (smc *JSONRestClient) modifyMethod(method string, modelName string) string
 		case client.SeldonMetadataPath:
 			return "/v1/models/" + modelName + "/metadata"
 		}
-	case api.ProtocolKFServing:
+	case api.ProtocolV2, api.ProtocolKFServing:
 		switch method {
 		case client.SeldonPredictPath, client.SeldonTransformInputPath, client.SeldonTransformOutputPath:
 			return "/v2/models/" + modelName + "/infer"
@@ -346,7 +346,7 @@ func (smc *JSONRestClient) Chain(ctx context.Context, modelName string, msg payl
 		return msg, nil
 	case api.ProtocolTensorflow: // Attempt to chain tensorflow payload
 		return ChainTensorflow(msg)
-	case api.ProtocolKFServing:
+	case api.ProtocolV2, api.ProtocolKFServing:
 		return ChainKFserving(msg)
 	}
 	return nil, errors.Errorf("Unknown protocol %s", smc.Protocol)

executor/api/rest/server.go

@@ -178,7 +178,7 @@ func (r *SeldonRestApi) Initialise() {
 			r.Router.NewRoute().Path("/v1/models/{"+ModelHttpPathVariable+"}/metadata").Methods("GET", "OPTIONS").HandlerFunc(r.wrapMetrics(metric.MetadataHttpServiceName, r.metadata))
 			// Enabling for standard seldon core feedback API endpoint with standard schema
 			r.Router.NewRoute().Path("/api/v1.0/feedback").Methods("OPTIONS", "POST").HandlerFunc(r.wrapMetrics(metric.FeedbackHttpServiceName, r.feedback))
-		case api.ProtocolKFServing:
+		case api.ProtocolV2, api.ProtocolKFServing:
 			r.Router.NewRoute().Path("/v2/models/{"+ModelHttpPathVariable+"}/infer").Methods("OPTIONS", "POST").HandlerFunc(r.wrapMetrics(metric.PredictionHttpServiceName, r.predictions))
 			r.Router.NewRoute().Path("/v2/models/infer").Methods("OPTIONS", "POST").HandlerFunc(r.wrapMetrics(metric.PredictionHttpServiceName, r.predictions)) // Nonstandard path - Seldon extension
 			r.Router.NewRoute().Path("/v2/models/{"+ModelHttpPathVariable+"}/ready").Methods("GET", "OPTIONS").HandlerFunc(r.wrapMetrics(metric.StatusHttpServiceName, r.status))

executor/cmd/executor/main.go

@@ -152,7 +152,7 @@ func runGrpcServer(wg *sync.WaitGroup, shutdown chan bool, lis net.Listener, log
 		tensorflowGrpcServer := tensorflow.NewGrpcTensorflowServer(predictor, client, serverUrl, namespace)
 		serving.RegisterPredictionServiceServer(grpcServer, tensorflowGrpcServer)
 		serving.RegisterModelServiceServer(grpcServer, tensorflowGrpcServer)
-	case api.ProtocolKFServing:
+	case api.ProtocolV2, api.ProtocolKFServing:
 		kfservingGrpcServer := kfserving.NewGrpcKFServingServer(predictor, client, serverUrl, namespace)
 		kfproto.RegisterGRPCInferenceServiceServer(grpcServer, kfservingGrpcServer)
 	}
@@ -247,8 +247,8 @@ func main() {
 		log.Fatal("Required argument predictor missing")
 	}
 
-	if !(*protocol == api.ProtocolSeldon || *protocol == api.ProtocolTensorflow || *protocol == api.ProtocolKFServing) {
-		log.Fatal("Protocol must be seldon, tensorflow or kfserving")
+	if !(*protocol == api.ProtocolSeldon || *protocol == api.ProtocolTensorflow || *protocol == api.ProtocolV2 || *protocol == api.ProtocolKFServing) {
+		log.Fatal("Protocol must be seldon, tensorflow or v2")
 	}
 
 	if *serverType == "kafka" {
@@ -384,7 +384,7 @@ func main() {
 		clientGrpc = seldon.NewSeldonGrpcClient(predictor, *sdepName, annotations)
 	case api.ProtocolTensorflow:
 		clientGrpc = tensorflow.NewTensorflowGrpcClient(predictor, *sdepName, annotations)
-	case api.ProtocolKFServing:
+	case api.ProtocolV2, api.ProtocolKFServing:
 		clientGrpc = kfserving.NewKFServingGrpcClient(predictor, *sdepName, annotations)
 	default:
 		log.Fatalf("Failed to create grpc client. Unknown protocol %s: %v", *protocol, err)

executor/samples/local/kfserving/model.yaml → executor/samples/local/v2/model.yaml

@@ -3,7 +3,7 @@ kind: SeldonDeployment
 metadata:
   name: triton
 spec:
-  protocol: kfserving
+  protocol: v2
   predictors:
   - graph:
       children: []

helm-charts/seldon-core-operator/values.yaml

@@ -119,15 +119,15 @@ predictor_servers:
       seldon:
         defaultImageVersion: "1.13.0-dev"
         image: seldonio/mlflowserver
-      kfserving:
+      v2:
         defaultImageVersion: "1.0.0.rc2-mlflow"
         image: seldonio/mlserver
   SKLEARN_SERVER:
     protocols:
       seldon:
         defaultImageVersion: "1.13.0-dev"
         image: seldonio/sklearnserver
-      kfserving:
+      v2:
         defaultImageVersion: "1.0.0.rc2-sklearn"
         image: seldonio/mlserver
   TENSORFLOW_SERVER:
@@ -143,17 +143,17 @@ predictor_servers:
       seldon:
         defaultImageVersion: "1.13.0-dev"
         image: seldonio/xgboostserver
-      kfserving:
+      v2:
         defaultImageVersion: "1.0.0.rc2-xgboost"
         image: seldonio/mlserver
   TRITON_SERVER:
     protocols:
-      kfserving:
+      v2:
         defaultImageVersion: "21.08-py3"
         image: nvcr.io/nvidia/tritonserver
   TEMPO_SERVER:
     protocols:
-      kfserving:
+      v2:
         defaultImageVersion: "1.0.0.rc2-slim"
         image: seldonio/mlserver