# Deeplearning4j (DL4J) ## Deeplearning4j (DL4J) * Using Java to create a configuration ```java 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. ```java String dl4jmodelfilePath = new ClassPathResource("data/multilayernetwork/SimpleCNN.zip"). getFile().getAbsolutePath(); ``` {% hint style="info" %} A reference Java project is provided in the Example repository ( ) 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. {% endhint %} ### 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](https://github.com/KonduitAI/konduit-serving/blob/master/konduit-serving-api/src/main/java/ai/konduit/serving/model/TensorDataType.java) 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. ```java Map input_data_types = new HashMap<>(); input_data_types.put("image_array", TensorDataType.FLOAT); List input_names = new ArrayList(input_data_types.keySet()); List 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 ```java 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. ```java 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`. ```java InferenceConfiguration inferenceConfiguration = InferenceConfiguration.builder() .servingConfig(servingConfig) .step(dl4jModelStep) .build(); ``` {% hint style="info" %} 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. {% endhint %} 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. ```java 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: ```java INDArray rand_image = Util.randInt(new int[]{1, 3, 244, 244}, 255); ``` ```java 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. {% hint style="info" %} 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. {% endhint %} ```java 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: ```java 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: ```java 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 } } ] } ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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