KServe API Clients

Python Client

When creating a Python-based client application, you can use Triton client library - tritonclient.

Install the Package

pip3 install tritonclient[all] 

Request Health Endpoints

python [GRPC]

import tritonclient.grpc as grpcclient

client = grpcclient.InferenceServerClient("localhost:9000")

server_live = client.is_server_live()

server_ready = client.is_server_ready()

model_ready = client.is_model_ready("model_name")

python [REST]

import tritonclient.http as httpclient

client = httpclient.InferenceServerClient("localhost:9000")

server_live = client.is_server_live()

server_ready = client.is_server_ready()

model_ready = client.is_model_ready("model_name")

cpp [GRPC]

#include "grpc_client.h"

namespace tc = triton::client;
int main() {
    std::unique_ptr<tc::InferenceServerGrpcClient> client;
    tc::InferenceServerGrpcClient::Create(&client, "localhost:9000");

    bool serverLive = client->IsServerLive(&serverLive);

    bool serverReady = client->IsServerReady(&serverReady);

    bool modelReady = client->IsModelReady(&modelReady, "model_name", "model_version");
}

cpp [REST]

#include "http_client.h"

namespace tc = triton::client;
int main() {
    std::unique_ptr<tc::InferenceServerHttpClient> client;
    tc::InferenceServerHttpClient::Create(&client, "localhost:9000");

    bool serverLive = client->IsServerLive(&serverLive);

    bool serverReady = client->IsServerReady(&serverReady);

    bool modelReady = client->IsModelReady(&modelReady, "model_name", "model_version");
}

java

public static void main(String[] args) {
    ManagedChannel channel = ManagedChannelBuilder
                    .forAddress("localhost", 9000)
                    .usePlaintext().build();
    GRPCInferenceServiceBlockingStub grpc_stub = GRPCInferenceServiceGrpc.newBlockingStub(channel);

    ServerLiveRequest.Builder serverLiveRequest = ServerLiveRequest.newBuilder();
    ServerLiveResponse serverLiveResponse = grpc_stub.serverLive(serverLiveRequest.build());

    bool serverLive = serverLiveResponse.getLive();

    ServerReadyRequest.Builder serverReadyRequest = ServerReadyRequest.newBuilder();
    ServerReadyResponse serverReadyResponse = grpc_stub.serverReady(serverReadyRequest.build());

    bool serverReady = serverReadyResponse.getReady();

    ModelReadyRequest.Builder modelReadyRequest = ModelReadyRequest.newBuilder();
    modelReadyRequest.setName("model_name");
    modelReadyRequest.setVersion("version");
    ModelReadyResponse modelReadyResponse = grpc_stub.modelReady(modelReadyRequest.build());

    bool modelReady = modelReadyResponse.getReady();
    
    channel.shutdownNow();
}

golang

func main() {
    grpc.Dial("localhost:9000", grpc.WithInsecure())
    client := grpc_client.NewGRPCInferenceServiceClient(conn)

    serverLiveRequest := grpc_client.ServerLiveRequest{}
    serverLiveResponse, err := client.ServerLive(ctx, &serverLiveRequest)

    serverReadyRequest := grpc_client.ServerReadyRequest{}
    serverReadyResponse, err := client.ServerReady(ctx, &serverReadyRequest)
    
    modelReadyRequest := grpc_client.ModelReadyRequest{
            Name:    "modelName",
            Version: "modelVersion",
    }
    modelReadyResponse, err := client.ModelReady(ctx, &modelReadyRequest)
}

curl

curl http://localhost:8000/v2/health/live
curl http://localhost:8000/v2/health/ready
curl http://localhost:8000/v2/models/model_name/ready

Request Server Metadata

python [GRPC]

import tritonclient.grpc as grpcclient

client = grpcclient.InferenceServerClient("localhost:9000")
server_metadata = client.get_server_metadata()

python [REST]

import tritonclient.http as httpclient

client = httpclient.InferenceServerClient("localhost:9000")
server_metadata = client.get_server_metadata()

cpp [GRPC]

#include "grpc_client.h"

namespace tc = triton::client;
int main() {
    std::unique_ptr<tc::InferenceServerGrpcClient> client;
    tc::InferenceServerGrpcClient::Create(&client, "localhost:9000");

    inference::ServerMetadataResponse server_metadata;
    client->ServerMetadata(&server_metadata);

    std::string name = server_metadata.name();
    std::string version = server_metadata.version();
}

cpp [REST]

#include "http_client.h"

namespace tc = triton::client;
int main() {
    std::unique_ptr<tc::InferenceServerHttpClient> client;
    tc::InferenceServerHttpClient::Create(&client, "localhost:9000");

    std::string server_metadata;
    client->ServerMetadata(&server_metadata);
}

java

public static void main(String[] args) {
    ManagedChannel channel = ManagedChannelBuilder
                    .forAddress("localhost", 9000)
                    .usePlaintext().build();
    GRPCInferenceServiceBlockingStub grpc_stub = GRPCInferenceServiceGrpc.newBlockingStub(channel);

    ServerMetadataRequest.Builder request = ServerMetadataRequest.newBuilder();
    ServerMetadataResponse response = grpc_stub.serverMetadata(request.build());
    
    channel.shutdownNow();
}

golang

grpc.Dial("localhost:9000", grpc.WithInsecure())
client := grpc_client.NewGRPCInferenceServiceClient(conn)

serverMetadataRequest := grpc_client.ServerMetadataRequest{}
serverMetadataResponse, err := client.ServerMetadata(ctx, &serverMetadataRequest)

curl

curl http://localhost:8000/v2

Request Model Metadata

python [GRPC]

import tritonclient.grpc as grpcclient

client = grpcclient.InferenceServerClient("localhost:9000")
model_metadata = client.get_model_metadata("model_name")

python [REST]

import tritonclient.http as httpclient

client = httpclient.InferenceServerClient("localhost:9000")
model_metadata = client.get_model_metadata("model_name")

cpp [GRPC]

#include "grpc_client.h"

namespace tc = triton::client;
int main() {
    std::unique_ptr<tc::InferenceServerGrpcClient> client;
    tc::InferenceServerGrpcClient::Create(&client, "localhost:9000");

    inference::ModelMetadataResponse model_metadata;
    client->ModelMetadata(&model_metadata, "model_name", "model_version");
}

cpp [REST]

#include "http_client.h"

namespace tc = triton::client;
int main() {
    std::unique_ptr<tc::InferenceServerHttpClient> client;
    tc::InferenceServerHttpClient::Create(&client, "localhost:9000");

    std::string model_metadata;
    client->ModelMetadata(&model_metadata, "model_name", "model_version")
}

java

public static void main(String[] args) {
    ManagedChannel channel = ManagedChannelBuilder
                    .forAddress("localhost", 9000)
                    .usePlaintext().build();
    GRPCInferenceServiceBlockingStub grpc_stub = GRPCInferenceServiceGrpc.newBlockingStub(channel);

    ModelMetadataRequest.Builder request = ModelMetadataRequest.newBuilder();
    request.setName("model_name");
    request.setVersion("model_version");
    ModelMetadataResponse response = grpc_stub.modelMetadata(request.build());
    
    channel.shutdownNow();
}

golang

grpc.Dial("localhost:9000", grpc.WithInsecure())
client := grpc_client.NewGRPCInferenceServiceClient(conn)

modelMetadataRequest := grpc_client.ModelMetadataRequest{
    Name:    "modelName",
    Version: "modelVersion",
}
modelMetadataResponse, err := client.ModelMetadata(ctx, &modelMetadataRequest)

curl

curl http://localhost:8000/v2/models/model_name

Request Prediction on an Encoded Image

python [GRPC]

import tritonclient.grpc as grpcclient

triton_client = grpcclient.InferenceServerClient(
    url="address",
    ssl=False,
    verbose=False)

image_data = []
with open("image_path", 'rb') as f:
    image_data.append(f.read())
inputs = []
inputs.append(grpcclient.InferInput('input_name', 1, "BYTES"))
nmpy = np.array(image_data , dtype=np.object_)
inputs[0].set_data_from_numpy(nmpy)

outputs = []
outputs.append(grpcclient.InferRequestedOutput("output_name"))

results = triton_client.infer(model_name="model_name",
                            inputs=inputs,
                            outputs=outputs)

python [REST]

import tritonclient.http as httpclient

triton_client = httpclient.InferenceServerClient(
            url="address",
            ssl=False,
            ssl_options=None,
            verbose=False)

image_data = []
with open("image_path", 'rb') as f:
    image_data.append(f.read())
inputs = []
inputs.append(httpclient.InferInput('input_name', 1, "BYTES"))
nmpy = np.array(image_data , dtype=np.object_)
inputs[0].set_data_from_numpy(nmpy)

outputs = []
outputs.append(httpclient.InferRequestedOutput("output_name"))

results = triton_client.infer(model_name="model_name",
                            inputs=inputs,
                            outputs=outputs)

cpp [GRPC]

#include "grpc_client.h"

namespace tc = triton::client;
int main() {
    std::unique_ptr<tc::InferenceServerGrpcClient> client;
    tc::InferenceServerGrpcClient::Create(&client, "localhost:9000");

    std::vector<int64_t> shape{1};
    tc::InferInput* input;
    tc::InferInput::Create(&input, "input_name", shape, "BYTES");
    std::shared_ptr<tc::InferInput> input_ptr;
    input_ptr.reset(input)

    std::ifstream file(fileName, std::ios::binary);
    file.unsetf(std::ios::skipws);
    std::streampos fileSize;

    file.seekg(0, std::ios::end);
    fileSize = file.tellg();
    file.seekg(0, std::ios::beg);
    std::ostringstream oss;
    oss << file.rdbuf();
    input_ptr->AppendFromString({oss.str()});

    tc::InferOptions options("model_name");
    tc::InferResult* result;
    client->Infer(&result, options, inputs);
}

cpp [REST]

#include "http_client.h"

namespace tc = triton::client;
int main() {
    std::unique_ptr<tc::InferenceServerHttpClient> client;
    tc::InferenceServerHttpClient::Create(&client, "localhost:9000");

    std::vector<int64_t> shape{1};
    tc::InferInput* input;
    tc::InferInput::Create(&input, "input_name", shape, "BYTES");
    std::shared_ptr<tc::InferInput> input_ptr;
    input_ptr.reset(input)

    std::ifstream file(fileName, std::ios::binary);
    file.unsetf(std::ios::skipws);
    std::streampos fileSize;

    file.seekg(0, std::ios::end);
    fileSize = file.tellg();
    file.seekg(0, std::ios::beg);
    std::ostringstream oss;
    oss << file.rdbuf();
    input_ptr->AppendFromString({oss.str()});

    tc::InferOptions options("model_name");
    tc::InferResult* result;
    client->Infer(&result, options, inputs);
}

java

public static void main(String[] args) {
    ManagedChannel channel = ManagedChannelBuilder
                    .forAddress("localhost", 9000)
                    .usePlaintext().build();
    GRPCInferenceServiceBlockingStub grpc_stub = GRPCInferenceServiceGrpc.newBlockingStub(channel);

    ModelInferRequest.Builder request = ModelInferRequest.newBuilder();
    request.setModelName("model_name");
    request.setModelVersion("model_version");

    ModelInferRequest.InferInputTensor.Builder input = ModelInferRequest.InferInputTensor
            .newBuilder();
    String defaultInputName = "b";
    input.setName("input_name");
    input.setDatatype("BYTES");
    input.addShape(1);

    FileInputStream imageStream = new FileInputStream("image_path");
    InferTensorContents.Builder input_data = InferTensorContents.newBuilder();
    input_data.addBytesContents(ByteString.readFrom(imageStream));
    input.setContents(input_data);
    request.addInputs(0, input);

    ModelInferResponse response = grpc_stub.modelInfer(request.build());
    
    channel.shutdownNow();
}

golang

grpc.Dial("localhost:9000", grpc.WithInsecure())
client := grpc_client.NewGRPCInferenceServiceClient(conn)

ctx, cancel := context.WithTimeout(context.Background(), 10*time.Second)

bytes, err := ioutil.ReadFile(fileName)
contents := grpc_client.InferTensorContents{}
contents.BytesContents = append(contents.BytesContents, bytes) 

inferInput := grpc_client.ModelInferRequest_InferInputTensor{
    Name:     "0",
    Datatype: "BYTES",
    Shape:    []int64{1},
    Contents: &contents,
}

inferInputs := []*grpc_client.ModelInferRequest_InferInputTensor{
    &inferInput,
}

modelInferRequest := grpc_client.ModelInferRequest{
    ModelName:    modelName,
    ModelVersion: modelVersion,
    Inputs:       inferInputs,
}

modelInferResponse, err := client.ModelInfer(ctx, &modelInferRequest)

curl

echo -n '{"inputs" : [{"name" : "0", "shape" : [1], "datatype" : "BYTES"}]}' > request.json
stat --format=%s request.json
66
printf "%x\n" `stat -c "%s" ./image.jpeg`
1c21
echo -n -e '\x21\x1c\x00\x00' >> request.json
cat ./image.jpeg >> request.json
curl --data-binary "@./request.json" -X POST http://localhost:8000/v2/models/resnet/versions/0/infer -H "Inference-Header-Content-Length: 66"

Request Prediction on a Numpy Array

python [GRPC]

import numpy as np
import tritonclient.grpc as grpcclient

client = grpcclient.InferenceServerClient("localhost:9000")
data = np.array([1.0, 2.0, ..., 1000.0])
infer_input = grpcclient.InferInput("input_name", data.shape, "FP32")
infer_input.set_data_from_numpy(data)
results = client.infer("model_name", [infer_input])

python [REST]

import numpy as np
import tritonclient.http as httpclient

client = httpclient.InferenceServerClient("localhost:9000")
data = np.array([1.0, 2.0, ..., 1000.0])
infer_input = httpclient.InferInput("input_name", data.shape, "FP32")
infer_input.set_data_from_numpy(data)
results = client.infer("model_name", [infer_input])

cpp [GRPC]

#include "grpc_client.h"

namespace tc = triton::client;
int main() {
    std::unique_ptr<tc::InferenceServerGrpcClient> client;
    tc::InferenceServerGrpcClient::Create(&client, "localhost:9000");

    std::vector<int64_t> shape{1, 10};
    tc::InferInput* input;
    tc::InferInput::Create(&input, "input_name", shape, "FP32");
    std::shared_ptr<tc::InferInput> input_ptr;
    input_ptr.reset(input);

    std::vector<float> input_data(10);
    for (size_t i = 0; i < 10; ++i) {
        input_data[i] = i;
    }
    std::vector<tc::InferInput*> inputs = {input_ptr.get()};
    tc::InferOptions options("model_name");
    tc::InferResult* result;
    input_ptr->AppendRaw(input_data);
    client->Infer(&result, options, inputs);
    input->Reset();
}

cpp [REST]

#include "http_client.h"

namespace tc = triton::client;
int main() {
    std::unique_ptr<tc::InferenceServerHttpClient> client;
    tc::InferenceServerHttpClient::Create(&client, "localhost:9000");

    std::vector<int64_t> shape{1, 10};
    tc::InferInput* input;
    tc::InferInput::Create(&input, "input_name", shape, "FP32");
    std::shared_ptr<tc::InferInput> input_ptr;
    input_ptr.reset(input);

    std::vector<float> input_data(10);
    for (size_t i = 0; i < 10; ++i) {
        input_data[i] = i;
    }
    std::vector<tc::InferInput*> inputs = {input_ptr.get()};
    tc::InferOptions options("model_name");
    tc::InferResult* result;
    input_ptr->AppendRaw(input_data);
    client->Infer(&result, options, inputs);
    input->Reset();
}

java

public static void main(String[] args) {
    ManagedChannel channel = ManagedChannelBuilder
                    .forAddress("localhost", 9000)
                    .usePlaintext().build();
    GRPCInferenceServiceBlockingStub grpc_stub = GRPCInferenceServiceGrpc.newBlockingStub(channel);

    ModelInferRequest.Builder request = ModelInferRequest.newBuilder();
    request.setModelName("model_name");
    request.setModelVersion("model_version");

    List<Float> lst = Arrays.asList(0f, 1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f, 9f);
    InferTensorContents.Builder input_data = InferTensorContents.newBuilder();
    input_data.addAllFp32Contents(lst);

    ModelInferRequest.InferInputTensor.Builder input = ModelInferRequest.InferInputTensor
            .newBuilder();
    String defaultInputName = "b";
    input.setName("input_name");
    input.setDatatype("FP32");
    input.addShape(1);
    input.addShape(10);
    input.setContents(input_data);

    request.addInputs(0, input);

    ModelInferResponse response = grpc_stub.modelInfer(request.build());
    
    channel.shutdownNow();
}

golang

grpc.Dial("localhost:9000", grpc.WithInsecure())
client := grpc_client.NewGRPCInferenceServiceClient(conn)

ctx, cancel := context.WithTimeout(context.Background(), 10*time.Second)

inputData := make([]float32, inputSize)
for i := 0; i < inputSize; i++ {
    inputData[i] = float32(i)
}

inferInputs := []*grpc_client.ModelInferRequest_InferInputTensor{
    &grpc_client.ModelInferRequest_InferInputTensor{
        Name:     "b",
        Datatype: "FP32",
        Shape:    []int64{1, 10},
        Contents: &grpc_client.InferTensorContents{
            Fp32Contents: inputData,
        },
    },
}

modelInferRequest := grpc_client.ModelInferRequest{
    ModelName:    "model_name",
    ModelVersion: "model_version",
    Inputs:       inferInputs,
}

modelInferResponse, err := client.ModelInfer(ctx, &modelInferRequest)

curl

curl -X POST http://localhost:8000/v2/models/model_name/infer
-H 'Content-Type: application/json'
-d '{"inputs" : [ {"name" : "input_name", "shape" : [ 1, 10 ], "datatype"  : "FP32", "data" : [1,2,3,4,5,6,7,8,9,10]} ]}'

Request Prediction on a string

python [GRPC]

import numpy as np
import tritonclient.grpc as grpcclient

client = grpcclient.InferenceServerClient("localhost:9000")
data = "<string>"
input = np.array([data.encode('utf-8')], dtype=np.object_)
infer_input = grpcclient.InferInput("input_name", [1], "BYTES")
infer_input.set_data_from_numpy(input)
results = client.infer("model_name", [infer_input])

python [REST]

import numpy as np
import tritonclient.http as httpclient

client = httpclient.InferenceServerClient("localhost:9000")
data = "<string>"
input = np.array([data.encode('utf-8')], dtype=np.object_)
infer_input = httpclient.InferInput("input_name", [1], "BYTES")
infer_input.set_data_from_numpy(input)
results = client.infer("model_name", [infer_input])

cpp [GRPC]

#include "grpc_client.h"

namespace tc = triton::client;

int main(int argc, char** argv) {
    std::unique_ptr<tc::InferenceServerGrpcClient> client;
    tc::InferenceServerGrpcClient::Create(&client, "localhost:9000");
    
    tc::InferInput* input;
    tc::InferInput::Create(&input, "input_name", {1}, "BYTES");
    std::shared_ptr<tc::InferInput> input_ptr;
    input_ptr.reset(input);
    input_ptr->AppendFromString({std::string("<string>")});
    std::vector<tc::InferInput*> inputs = {input_ptr.get()};
    
    tc::InferOptions options("model_name");
    tc::InferResult* results;
    client->Infer(&results, options, inputs);
    std::shared_ptr<tc::InferResult> results_ptr;
    results_ptr.reset(results);
    return 0;
}

cpp [REST]

#include "http_client.h"

namespace tc = triton::client;

int main(int argc, char** argv) {
    std::unique_ptr<tc::InferenceServerHttpClient> client;
    tc::InferenceServerHttpClient::Create(&client, "localhost:9000");
    
    tc::InferInput* input;
    tc::InferInput::Create(&input, "input_name", {1}, "BYTES");
    std::shared_ptr<tc::InferInput> input_ptr;
    input_ptr.reset(input);
    input_ptr->AppendFromString({std::string("<string>")});    
    std::vector<tc::InferInput*> inputs = {input_ptr.get()};
    
    tc::InferOptions options("model_name");
    tc::InferResult* results;
    client->Infer(&results, options, inputs);
    std::shared_ptr<tc::InferResult> results_ptr;
    results_ptr.reset(results);
    return 0;
}

curl

curl -X POST http://localhost:9000/v2/models/model_name/infer
-H 'Content-Type: application/json'
-d '{"inputs" : [ {"name" : "input_name", "shape" : [ 1 ], "datatype"  : "BYTES", "data" : ["<string>"]} ]}

Request Streaming Prediction

python [GRPC]

import numpy as np
import tritonclient.grpc as grpcclient

def callback(result, error):
    if error:
        raise error
    # ... process result
    timestamp = result.get_response().parameters['OVMS_MP_TIMESTAMP'].int64_param  # optional

client = grpcclient.InferenceServerClient("localhost:9000")

data = np.array([1.0, 2.0, ..., 1000.0])
infer_input = grpcclient.InferInput("input_name", data.shape, "FP32")
infer_input.set_data_from_numpy(data)

client.start_stream(callback=callback)

for _ in range(5):  # reuse opened stream
    client.async_stream_infer(
        model_name="model_name",
        inputs=[infer_input],
        parameters={'OVMS_MP_TIMESTAMP': 43166520112})  # optional

client.stop_stream()

For complete usage examples see Kserve samples.