Invoke applications

Inferencing applications can be achieved using any of the methods described below.
Hydrosphere UI
To send a sample request using Hydrosphere UI, open the desired application, and press the Test button at the upper right corner. We will generate dummy inputs based on your model's contract and send an HTTP request to the model's endpoint.
post
HTTP Inference
/gateway/application/<application_name>
To send an HTTP request, you should send a POST request to the /gateway/application/<applicationName> endpoint with the JSON body containing your request data, composed with respect to the model's contract.
Request
Response
Request
Path Parameters
application_name
required
string
Name of the application
Body Parameters
required
object
Request data, composed with respect to the model's contract.
Response
200: OK
​
gRPC
To send a gRPC request you need to create a specific client.
Python
Java
Python
import grpc 
import hydro_serving_grpc as hs  # pip install hydro-serving-grpc
​
# connect to your ML Lamba instance
channel = grpc.insecure_channel("<host>")
stub = hs.PredictionServiceStub(channel)
​
# 1. define a model, that you'll use
model_spec = hs.ModelSpec(name="model")
​
# 2. define tensor_shape for Tensor instance
tensor_shape = hs.TensorShapeProto(
    dim=[hs.TensorShapeProto.Dim(size=-1), hs.TensorShapeProto.Dim(size=2)])
​
# 3. define tensor with needed data
tensor = hs.TensorProto(dtype=hs.DT_DOUBLE, tensor_shape=tensor_shape, double_val=[1,1,1,1])
​
# 4. create PredictRequest instance
request = hs.PredictRequest(model_spec=model_spec, inputs={"x": tensor})
​
# call Predict method
result = stub.Predict(request)
Java
import com.google.protobuf.Int64Value;
import io.grpc.ManagedChannel;
import io.grpc.ManagedChannelBuilder;
import io.hydrosphere.serving.tensorflow.DataType;
import io.hydrosphere.serving.tensorflow.TensorProto;
import io.hydrosphere.serving.tensorflow.TensorShapeProto;
import io.hydrosphere.serving.tensorflow.api.Model;
import io.hydrosphere.serving.tensorflow.api.Predict;
import io.hydrosphere.serving.tensorflow.api.PredictionServiceGrpc;
​
import java.util.Random;
​
public class HydrosphereClient {
​
    private final String modelName;         // Actual model name, registered within Hydrosphere platform
    private final Int64Value modelVersion;  // Model version of the registered model within Hydrosphere platform
    private final ManagedChannel channel;
    private final PredictionServiceGrpc.PredictionServiceBlockingStub blockingStub;
​
    public HydrosphereClient2(String target, String modelName, long modelVersion) {
        this(ManagedChannelBuilder.forTarget(target).build(), modelName, modelVersion);
    }
​
    HydrosphereClient2(ManagedChannel channel, String modelName, long modelVersion) {
        this.channel = channel;
        this.modelName = modelName;
        this.modelVersion = Int64Value.newBuilder().setValue(modelVersion).build();
        this.blockingStub = PredictionServiceGrpc.newBlockingStub(this.channel);
    }
​
    private Model.ModelSpec getModelSpec() {
        /*
        Helper method to generate ModelSpec.
         */
        return Model.ModelSpec.newBuilder()
                .setName(this.modelName)
                .setVersion(this.modelVersion)
                .build();
    }
​
    private TensorProto generateDoubleTensorProto() {
        /*
        Helper method generating random TensorProto object for double values.
        */
        return TensorProto.newBuilder()
                .addDoubleVal(new Random().nextDouble())
                .setDtype(DataType.DT_DOUBLE)
                .setTensorShape(TensorShapeProto.newBuilder().build())  // Empty TensorShape indicates scalar shape
                .build();
    }
​
    public Predict.PredictRequest generatePredictRequest() {
        /*
        PredictRequest is used to define the data passed to the model for inference.
        */
        return Predict.PredictRequest.newBuilder()
                .putInputs("in", this.generateDoubleTensorProto())
                .setModelSpec(this.getModelSpec())
                .build();
    }
​
​
    public Predict.PredictResponse predict(Predict.PredictRequest request) {
        /*
        The actual use of RPC method Predict of the PredictionService to invoke prediction.
        */
        return this.blockingStub.predict(request);
    }
​
    public static void main(String[] args) throws Exception {
        HydrosphereClient client = new HydrosphereClient("<host>", "example", 2);
        Predict.PredictRequest request = client.generatePredictRequest();
        Predict.PredictResponse response = client.predict(request);
        System.out.println(response);
    }
}
Python SDK
You can learn more about our Python SDK here.
import hydrosdk as hs
​
hs_cluster = hs.Cluster(http_address='{HTTP_CLUSTER_ADDRESS}',
                         grpc_address='{GRPC_CLUSTER_ADDRESS}',)
​
app = hs.Application.find(hs_cluster, "{APP_NAME}")
​
predictor = adult_servable.predictor()
​
data  = ...  # your data
predictor.predict(data)
🏄 Quickstart - Previous
How-To
Write definitions
Last updated 8 months ago
Invoke applications

Hydrosphere UI

postHTTP Inference

gRPC

Python SDK

post
HTTP Inference
