TensorFlow Serving Client for Java

TensorFlow Serving Client for Java (TFSC4J) is a Java client library for TensorFlow Serving. It supports the following TensorFlow Serving Client API (gRPC):

• Model status API
• Model Metadata API
• Classify and Regress API
• Predict API

Requirements

• Java 17+

Install

Add the dependency to your pom.xml:

<dependency>
    <groupId>io.github.tadayosi.tensorflow</groupId>
    <artifactId>tensorflow-serving-client</artifactId>
    <version>0.2.2</version>
</dependency>

Usage

Important

TFSC4J uses the gRPC port (default: 8500) to communicate with the TensorFlow model server.

To creat a client:

TensorFlowServingClient client = TensorFlowServingClient.newInstance();

By default, the client connects to localhost:8500, but if you want to connect to a different target URI (e.g. example.com:8080), instantiate a client as follows:

TensorFlowServingClient client = TensorFlowServingClient.builder()
    .target("example.com:8080")
    .build();

Model status API

To get the status of a model:

try (TensorFlowServingClient client = TensorFlowServingClient.newInstance()) {
    GetModelStatusRequest request = GetModelStatusRequest.newBuilder()
        .setModelSpec(ModelSpec.newBuilder()
            .setName("half_plus_two")
            .setVersion(Int64Value.of(123)))
        .build();
    GetModelStatusResponse response = client.getModelStatus(request);
    System.out.println(response);
}

Output:

model_version_status {
  version: 123
  state: AVAILABLE
  status {
  }
}

Model Metadata API

To get the metadata of a model:

try (TensorFlowServingClient client = TensorFlowServingClient.newInstance()) {
    GetModelMetadataRequest request = GetModelMetadataRequest.newBuilder()
        .setModelSpec(ModelSpec.newBuilder()
            .setName("half_plus_two")
            .setVersion(Int64Value.of(123)))
        .addMetadataField("signature_def")) // metadata_field is mandatory
        .build();
    GetModelMetadataResponse response = client.getModelMetadata(request);
    System.out.println(response);
}

Output:

model_spec {
  name: "half_plus_two"
  version {
    value: 123
  }
}
metadata {
  key: "signature_def"
  value {
    type_url: "type.googleapis.com/tensorflow.serving.SignatureDefMap"
    value: "..."
  }
}

Classify API

To classify:

try (TensorFlowServingClient client = TensorFlowServingClient.newInstance()) {
    ClassificationRequest request = ClassificationRequest.newBuilder()
        .setModelSpec(ModelSpec.newBuilder()
            .setName("half_plus_two")
            .setVersion(Int64Value.of(123))
            .setSignatureName("classify_x_to_y"))
        .setInput(Input.newBuilder()
            .setExampleList(ExampleList.newBuilder()
                .addExamples(Example.newBuilder()
                    .setFeatures(Features.newBuilder()
                        .putFeature("x", Feature.newBuilder()
                            .setFloatList(FloatList.newBuilder().addValue(1.0f))
                            .build())))))
        .build();
    ClassificationResponse response = client.classify(request);
    System.out.println(response);
}

Output:

result {
  classifications {
    classes {
      score: 2.5
    }
  }
}
model_spec {
  name: "half_plus_two"
  version {
    value: 123
  }
  signature_name: "classify_x_to_y"
}

Regress API

To regress:

try (TensorFlowServingClient client = TensorFlowServingClient.newInstance()) {
    RegressionRequest request = RegressionRequest.newBuilder()
        .setModelSpec(ModelSpec.newBuilder()
            .setName("half_plus_two")
            .setVersion(Int64Value.of(123))
            .setSignatureName("regress_x_to_y"))
        .setInput(Input.newBuilder()
            .setExampleList(ExampleList.newBuilder()
                .addExamples(Example.newBuilder()
                    .setFeatures(Features.newBuilder()
                        .putFeature("x", Feature.newBuilder()
                            .setFloatList(FloatList.newBuilder().addValue(1.0f))
                            .build())))))
        .build();
    RegressionResponse response = client.regress(request);
    System.out.println(response);
}

Output:

result {
  regressions {
    value: 2.5
  }
}
model_spec {
  name: "half_plus_two"
  version {
    value: 123
  }
  signature_name: "regress_x_to_y"
}

Predict API

To predict:

try (TensorFlowServingClient client = TensorFlowServingClient.newInstance()) {
    PredictRequest request = PredictRequest.newBuilder()
        .setModelSpec(ModelSpec.newBuilder()
            .setName("half_plus_two")
            .setVersion(Int64Value.of(123)))
        .putInputs("x", TensorProto.newBuilder()
            .setDtype(DataType.DT_FLOAT)
            .setTensorShape(TensorShapeProto.newBuilder()
                .addDim(Dim.newBuilder().setSize(3)))
            .addFloatVal(1.0f)
            .addFloatVal(2.0f)
            .addFloatVal(5.0f)
            .build())
        .build();
    PredictResponse response = client.predict(request);
    System.out.println(response);
}

Output:

outputs {
  key: "y"
  value {
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: 3
      }
    }
    float_val: 2.5
    float_val: 3.0
    float_val: 4.5
  }
}
model_spec {
  name: "half_plus_two"
  version {
    value: 123
  }
  signature_name: "serving_default"
}

Configuration

tfsc4j.properties

target = <target>
credentials = <credentials>

System properties

You can configure the TFSC4J properties via system properties with prefix tfsc4j..

For instance, you can configure target with the tfsc4j.target system property.

Environment variables

You can also configure the TFSC4J properties via environment variables with prefix TFSC4J_.

For instance, you can configure target with the TFSC4J_TARGET environment variable.

Examples

See examples.

Build

mvn clean install