Keras (TensorFlow 2.0)

This page illustrates a simple client-server interaction to perform inference on a Keras LSTM model using the Java SDK for Konduit Serving.
import ai.konduit.serving.InferenceConfiguration;
import ai.konduit.serving.config.ParallelInferenceConfig;
import ai.konduit.serving.config.ServingConfig;
import ai.konduit.serving.configprovider.KonduitServingMain;
import ai.konduit.serving.configprovider.KonduitServingMainArgs;
import ai.konduit.serving.model.ModelConfig;
import ai.konduit.serving.model.ModelConfigType;
import ai.konduit.serving.pipeline.step.ModelStep;
import ai.konduit.serving.verticles.inference.InferenceVerticle;
import com.mashape.unirest.http.Unirest;
import com.mashape.unirest.http.exceptions.UnirestException;
import org.apache.commons.io.FileUtils;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.serde.binary.BinarySerde;

Saving models in Keras HDF5 (.h5) format

Models can be saved using Python with the .save() method. Refer to the TensorFlow documentation for Keras for details. These saved models shall be loaded in Java.

Keras model loading functionality in Konduit Serving converts Keras models to Deeplearning4J models. As a result, Keras models containing operations not supported in Deeplearning4J cannot be served in Konduit Serving. See issue 8348.

Overview

Konduit Serving works by defining a series of steps. These include operations such as

  1. Pre- or post-processing steps

  2. One or more machine learning models

  3. Transforming the output in a way that can be understood by humans

If deploying your model does not require pre- nor post-processing, only one step - a machine learning model - is required. This configuration is defined using a single ModelStep.

A reference Java project is provided in the Example repository ( https://github.com/KonduitAI/konduit-serving-examples ) with a Maven pom.xml dependencies file. If using the IntelliJ IDEA IDE, open the java folder as a Maven project and run the main function of the InferenceModelStepKeras class.

Configure the step

Define the Keras configuration as a ModelConfig object.

  • modelConfigType: This argument requires a ModelConfigType object. Specify modelType as ModelConfig.ModelType.KERAS, and modelLoadingPath to point to the location of Keras weights saved in the HDF5 file format.

For the ModelStep object, the following parameters are specified:

  • modelConfig: pass the ModelConfig object here

  • parallelInferenceConfig: specify the number of workers to run in parallel. Here, we specify workers = 1.

  • inputName, outputName: names for the input and output nodes, as lists

String kerasmodelfilePath = new ClassPathResource("data/keras/embedding_lstm_tensorflow_2.h5").getFile().getAbsolutePath();
ModelConfig kerasModelConfig = ModelConfig.builder()
.modelConfigType(ModelConfigType.builder()
.modelLoadingPath(kerasmodelfilePath.toString())
.modelType(ModelConfig.ModelType.KERAS).build())
.build();
ModelStep kerasmodelStep = ModelStep.builder()
.modelConfig(kerasModelConfig)
.inputName("input")
.outputName("lstm_1")
.parallelInferenceConfig(ParallelInferenceConfig.builder().workers(1).build())
.build();

Input and output names can be obtained by visualizing the graph in Netron.

Configure the server

In the ServingConfig, specify any port number that is not reserved.

int port = Util.randInt(1000, 65535);
ServingConfig servingConfig = ServingConfig.builder().httpPort(port).
build();

The ServingConfig has to be passed to Server in addition to the steps as a list. In this case, there is a single step: kerasmodelStep.

InferenceConfiguration inferenceConfiguration = InferenceConfiguration.builder()
.servingConfig(servingConfig)
.step(kerasmodelStep)
.build();

The inferenceConfiguration is stored as a JSON File. Set the KonduitServingMainArgs with the saved config.json file path as configPath and other necessary server configuration arguments.

File configFile = new File("config.json");
FileUtils.write(configFile, inferenceConfiguration.toJson(), Charset.defaultCharset());
KonduitServingMainArgs args1 = KonduitServingMainArgs.builder()
.configStoreType("file").ha(false)
.multiThreaded(false).configPort(port)
.verticleClassName(InferenceVerticle.class.getName())
.configPath(configFile.getAbsolutePath())
.build();

Start server by calling KonduitServingMain with the configurations mentioned in the KonduitServingMainArgs using Callback Function(as per the code mentioned in the Inference Section below)

Inference

NDARRAY inputs to set ModelStep must be specified with a shape size.

To configure the client, set the required URL to connect server and specify any port number that is not reserved (as used in server configuration).

A Callback Function onSuccess is implemented in order to post the Client request and get the HttpResponse, only after the successful run of the KonduitServingMain Server.

INDArray arr = Nd4j.create(new float[]{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, 1, 10);
File file = new File("src/main/resources/data/test-input.zip");
System.out.println(file.getAbsolutePath());
BinarySerde.writeArrayToDisk(arr, file);
KonduitServingMain.builder()
.onSuccess(() -> {
try {
String response = Unirest.post(String.format("http://localhost:%s/raw/nd4j", port))
.field("input", file)
.asString().getBody();
System.out.print(response);
System.exit(0);
} catch (UnirestException e) {
e.printStackTrace();
System.exit(0);
}
})
.build()
.runMain(args1.toArgs());

Confirm the output

After executing the above, in order to confirm the successful start of the Server, check for the below output text:

Jan 07, 2020 2:31:37 PM ai.konduit.serving.configprovider.KonduitServingMain
INFO: Deployed verticle ai.konduit.serving.verticles.inference.InferenceVerticle

The Output of the program is as follows:

System.out.print(response)
{
"lstm_1" : {
"batchId" : "61c32b20-20c1-42d9-909e-40519304f2ac",
"ndArray" : {
"dataType" : "FLOAT",
"shape" : [ 1, 6, 10 ],
"data" : [ -0.0022880966, -0.0042849067, -0.005983479, -0.0073982426, -0.008555864, -0.009488584, -0.010229964, -0.010812048, -0.011263946,
-0.011611057, -0.0027362786, -0.005198746, -0.0072902446, -0.009000374, -0.010361125, -0.011421581, -0.012234278, -0.012848378, -0.013306688,
-0.013644846, 8.9187745E-4, 0.0012898755, 0.0014061421, 0.0013690761, 0.0012552886, 0.00110957, 9.573803E-4, 8.1235886E-4, 6.812691E-4, 5.6666887E-4,
-0.0029521661, -0.0049125706, -0.0062154937, -0.0070830043, -0.007662085, -0.008049844, -0.008310454, -0.008486291, -0.00860548, -0.008686648, -2.41272E-4,
-2.1998871E-4, -3.9213814E-5, 2.2318505E-4, 5.1378313E-4, 7.9911196E-4, 0.0010602918, 0.0012885022, 0.0014812968, 0.00164004, 0.0029523545, 0.0050065047,
0.006426977, 0.007400234, 0.008058914, 0.008497588, 0.008783782, 0.00896551, 0.009076695, 0.009141108 ]
}
}

The complete inference configuration in JSON format is as follows:

System.out.println(inferenceConfiguration.toJson());
{
"memMapConfig" : null,
"servingConfig" : {
"httpPort" : 62969,
"listenHost" : "localhost",
"logTimings" : false,
"metricTypes" : [ "CLASS_LOADER", "JVM_MEMORY", "JVM_GC", "PROCESSOR", "JVM_THREAD", "LOGGING_METRICS", "NATIVE" ],
"outputDataFormat" : "JSON",
"uploadsDirectory" : "file-uploads/"
},
"steps" : [ {
"@type" : "ModelStep",
"inputColumnNames" : { },
"inputNames" : [ "input" ],
"inputSchemas" : { },
"modelConfig" : {
"@type" : "ModelConfig",
"modelConfigType" : {
"modelLoadingPath" : "C:\\konduit-serving-examples\\java\\target\\classes\\data\\keras\\embedding_lstm_tensorflow_2.h5",
"modelType" : "KERAS"
},
"tensorDataTypesConfig" : null
},
"normalizationConfig" : null,
"outputColumnNames" : { },
"outputNames" : [ "lstm_1" ],
"outputSchemas" : { },
"parallelInferenceConfig" : {
"batchLimit" : 32,
"inferenceMode" : "BATCHED",
"maxTrainEpochs" : 1,
"queueLimit" : 64,
"vertxConfigJson" : null,
"workers" : 1
}
} ]
}