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.

HTTP Inference

POST /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.

Path Parameters




Name of the application

Request Body



Request data, composed with respect to the model's contract.


To send a gRPC request you need to create a specific client.

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)

Python SDK

You can learn more about our Python SDK here.

import hydrosdk as hs

hs_cluster = hs.Cluster(http_address='{HTTP_CLUSTER_ADDRESS}',

app = hs.Application.find(hs_cluster, "{APP_NAME}")

predictor = adult_servable.predictor()

data  = ...  # your data

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