⚠️ TorchServe is now in limited maintenance. This project is not maintained anymore as well. ⚠️

TorchServe Client for Java

TorchServe Client for Java (TSC4J) is a Java client library for TorchServe. It supports the following TorchServe REST API:

Requirements

Java 17+

Install

Add the dependency to your pom.xml:

<dependency>
    <groupId>io.github.tadayosi.torchserve</groupId>
    <artifactId>torchserve-client</artifactId>
    <version>0.4.1</version>
</dependency>

Usage

Inference

Prediction:

TorchServeClient client = TorchServeClient.newInstance();

byte[] image = Files.readAllBytes(Path.of("0.png"));
Object result = client.inference().predictions("mnist_v2", image);
System.out.println(result);
// => 0

With the inference API endpoint other than http://localhost:8080:

TorchServeClient client = TorchServeClient.builder()
    .inferenceAddress("http://localhost:12345")
    .build();

With token authorization:

TorchServeClient client = TorchServeClient.builder()
    .inferenceKey("<inference-key>")
    .build();

Management

TorchServeClient client = TorchServeClient.newInstance();

Response response = client.management().registerModel(
  "https://torchserve.pytorch.org/mar_files/mnist_v2.mar",
  RegisterModelOptions.empty());
System.out.println(response.getStatus());
// => "Model "mnist_v2" Version: 2.0 registered with 0 initial workers. Use scale workers API to add workers for the model."

Scale workers for a model:

TorchServeClient client = TorchServeClient.newInstance();

Response response = client.management().setAutoScale(
  "mnist_v2",
  SetAutoScaleOptions.builder()
    .minWorker(1)
    .maxWorker(2)
    .build());
System.out.println(response.getStatus());
// => "Processing worker updates..."

Describe a model:

TorchServeClient client = TorchServeClient.newInstance();

List<ModelDetail> model = client.management().describeModel("mnist_v2");
System.out.println(model.get(0));
// =>
// ModelDetail {
//     modelName: mnist_v2
//     modelVersion: 2.0
// ...

Unregister a model:

TorchServeClient client = TorchServeClient.newInstance();

Response response = client.management().unregisterModel(
  "mnist_v2",
  UnregisterModelOptions.empty());
System.out.println(response.getStatus());
// => "Model "mnist_v2" unregistered"

List models:

TorchServeClient client = TorchServeClient.newInstance();

ModelList models = client.management().listModels(10, null);
System.out.println(models);
// =>
// ModelList {
//     nextPageToken: null
//     models: [Model {
//     modelName: mnist_v2
//     modelUrl: https://torchserve.pytorch.org/mar_files/mnist_v2.mar
// },
// ...

Set default version for a model:

TorchServeClient client = TorchServeClient.newInstance();

Response response = client.management().setDefault("mnist_v2", "2.0");
System.out.println(response.getStatus());
// => "Default version successfully updated for model "mnist_v2" to "2.0""

With the management API endpoint other than http://localhost:8081:

TorchServeClient client = TorchServeClient.builder()
    .managementAddress("http://localhost:12345")
    .build();

With token authorization:

TorchServeClient client = TorchServeClient.builder()
    .managementKey("<management-key>")
    .build();

Metrics

Get metrics in Prometheus format:

TorchServeClient client = TorchServeClient.newInstance();

String metrics = client.metrics().metrics();
System.out.println(metrics);
// =>
// # HELP MemoryUsed Torchserve prometheus gauge metric with unit: Megabytes
// # TYPE MemoryUsed gauge
// MemoryUsed{Level="Host",Hostname="3a9b51d41fbf",} 2075.09765625
// ...

With the metrics API endpoint other than http://localhost:8082:

TorchServeClient client = TorchServeClient.builder()
    .metricsAddress("http://localhost:12345")
    .build();

Configuration

tsc4j.properties

inference.key = <inference-key>
inference.address = http://localhost:8080
# inference.address takes precedence over inference.port if it's defined
inference.port = 8080

management.key = <management-key>
management.address = http://localhost:8081
# management.address takes precedence over management.port if it's defined
management.port = 8081

metrics.address = http://localhost:8082
# metrics.address takes precedence over metrics.port if it's defined
metrics.port = 8082

System properties

You can configure the TSC4J properties via system properties with prefix tsc4j..

For instance, you can configure inference.address with the tsc4j.inference.address system property.

Environment variables

You can also configure the TSC4J properties via environment variables with prefix TSC4J_.

For instance, you can configure inference.address with the TSC4J_INFERENCE_ADDRESS environment variable.

Examples

See examples.

Build

mvn clean install

tadayosi/torchserve-client-java