Deeplearning4j (DL4J)

This document illustrates how to create Konduit Serving configurations with the Java SDK:

Deeplearning4j (DL4J)

• Using Java to create a configuration

import ai.konduit.serving.InferenceConfiguration;
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.model.TensorDataTypesConfig;
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.io.ClassPathResource;
import org.nd4j.serde.binary.BinarySerde;
import org.nd4j.tensorflow.conversion.TensorDataType;

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.

Set the dl4j model path to dl4jmodelfilePath.

String dl4jmodelfilePath = new ClassPathResource("data/multilayernetwork/SimpleCNN.zip").
    getFile().getAbsolutePath();

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 InferenceModelStepDL4J class.

Configure the step

Define the DL4J configuration as a ModelConfig object.

• tensorDataTypesConfig: The ModelConfig object requires a HashMap input_data_types. Its keys should represent column names, and the values should represent data types as strings, e.g. "INT32", "FLOAT",etc,. See here for a list of supported data types.

• modelConfigType: This argument requires a ModelConfigType object. In the Java program above, we recognised that SimpleCNN is configured as a MultiLayerNetwork, in contrast with the ComputationGraph class, which is used for more complex networks. Specify modelType as MULTI_LAYER_NETWORK, and modelLoadingPath to point to the location of DL4J weights saved in the ZIP file format.

Map<String, TensorDataType> input_data_types = new HashMap<>();
input_data_types.put("image_array", TensorDataType.FLOAT);

List<String> input_names = new ArrayList<String>(input_data_types.keySet());
List<String> output_names = new ArrayList<>();
output_names.add("output");

ModelConfig dl4jModelConfig = ModelConfig.builder()
    .tensorDataTypesConfig(TensorDataTypesConfig.builder().
            inputDataTypes(input_data_types).build())
    .modelConfigType(ModelConfigType.builder().
            modelLoadingPath(dl4jmodelfilePath.toString()).
            modelType(ModelConfig.ModelType.MULTI_LAYER_NETWORK).build())
    .build();

For the ModelStep object, the following parameters are specified:

• modelConfig: pass the ModelConfig object here

• input_names: names for the input data

• output_names: names for the output data

ModelStep dl4jModelStep = ModelStep.builder()
    .modelConfig(dl4jModelConfig)
    .inputNames(input_names)
    .outputNames(output_names)
    .build();

Configure the server

Specify the following:

• httpPort: 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 Java list. In this case, there is a single step: dl4jModelStep.

InferenceConfiguration inferenceConfiguration = InferenceConfiguration.builder()
    .servingConfig(servingConfig)
    .step(dl4jModelStep)
    .build();

Accepted input and output data formats are as follows:

• Input: JSON, ARROW, IMAGE, ND4J (not yet implemented) and NUMPY.

• Output: NUMPY, JSON, ND4J (not yet implemented) and ARROW.

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

We generate a (3, 224, 224) array of random numbers between 0 and 255 as input to the model for prediction.

Before requesting for a prediction, we normalize the image to be between 0 and 1:

INDArray rand_image = Util.randInt(new int[]{1, 3, 244, 244}, 255);

File file = new File("src/main/resources/data/test-dl4j.zip");

if(!file.exists()) file.createNewFile();

BinarySerde.writeArrayToDisk(rand_image, file);

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.

Accepted input and output data formats are as follows:

• Input: JSON, ARROW, IMAGE, ND4J (not yet implemented) and NUMPY.

• Output: NUMPY, JSON, ND4J (not yet implemented) and ARROW.

KonduitServingMain.builder()
    .onSuccess(() -> {
        try {
            String response = Unirest.post(String.format("http://localhost:%s/raw/nd4j", port))
                    .field("image_array", 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 08, 2020 3:03:50 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);

{
  "output" : {
    "batchId" : "d5090c30-526d-4e1f-93e2-a918435ac1da",
    "ndArray" : {
      "dataType" : "FLOAT",
      "shape" : [ 1, 5 ],
      "data" : [ 0.028113496, 0.3778126, 0.023068674, 3.759411E-5, 0.5709677 ]
    }
  }
}

The complete inference configuration in JSON format is as follows:

System.out.println(inferenceConfiguration.toJson());

{
  "memMapConfig" : null,
  "servingConfig" : {
    "httpPort" : 24229,
    "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" : [ "image_array" ],
    "inputSchemas" : { },
    "modelConfig" : {
      "@type" : "ModelConfig",
      "modelConfigType" : {
        "modelLoadingPath" : "C:\\konduit-serving-examples\\java\\target\\classes\\data\\multilayernetwork\\SimpleCNN.zip",
        "modelType" : "MULTI_LAYER_NETWORK"
      },
      "tensorDataTypesConfig" : {
        "inputDataTypes" : {
          "image_array" : "FLOAT"
        },
        "outputDataTypes" : { }
      }
    },
    "normalizationConfig" : null,
    "outputColumnNames" : { },
    "outputNames" : [ "output" ],
    "outputSchemas" : { },
    "parallelInferenceConfig" : {
      "batchLimit" : 32,
      "inferenceMode" : "BATCHED",
      "maxTrainEpochs" : 1,
      "queueLimit" : 64,
      "vertxConfigJson" : null,
      "workers" : 1
    }
  } ]
}