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Add Serve TensorFlow Models in Java
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‎ml/serve_tf_model/model-server/.attach_pid14326‎

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‎ml/serve_tf_model/model-server/pom.xml‎

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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<groupId>com.javahelps.tensorflow</groupId>
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<artifactId>model-server</artifactId>
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<version>1.0-SNAPSHOT</version>
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<build>
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<plugins>
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-compiler-plugin</artifactId>
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<configuration>
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<source>11</source>
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<target>11</target>
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</configuration>
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</plugin>
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</plugins>
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</build>
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<dependencies>
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow</artifactId>
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<version>1.13.1</version>
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</dependency>
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</dependencies>
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</project>

‎ml/serve_tf_model/model-server/src/main/java/com/javahelps/tensorflow/moderserver/ModelServer.java‎

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package com.javahelps.tensorflow.moderserver;
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import org.tensorflow.SavedModelBundle;
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import org.tensorflow.Session;
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import org.tensorflow.Tensor;
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import org.tensorflow.TensorFlowException;
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import java.util.List;
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public class ModelServer {
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public static void main(String[] args) {
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try (SavedModelBundle savedModelBundle = SavedModelBundle.load("/tmp/tf_add_model", "serve")) {
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try (Session session = savedModelBundle.session()) {
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Session.Runner runner = session.runner();
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runner.feed("x", Tensor.create(10));
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runner.feed("y", Tensor.create(20));
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List<Tensor<?>> tensors = runner.fetch("ans").run();
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System.out.println("Answer is: " + tensors.get(0).intValue());
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}
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} catch (TensorFlowException ex) {
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ex.printStackTrace();
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}
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}
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}

‎ml/serve_tf_model/tf_add_model/saved_model.pb‎

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�
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��
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:
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Add
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x"T
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y"T
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z"T"
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Ttype:
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2  
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C
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Placeholder
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output"dtype"
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dtypetype"
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shapeshape:"serve*1.10.02v1.10.0-0-g656e7a2b34�
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F
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x Placeholder*
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dtype0*
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_output_shapes
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:*
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shape:
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F
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y Placeholder*
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dtype0*
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_output_shapes
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:*
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shape:
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3
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ansAddxy*
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T0*
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_output_shapes
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:"*i
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serving_defaultV
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x
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x:0
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y
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y:0
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ans
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ans:0tensorflow/serving/predict

‎ml/serve_tf_model/train_tf_model.py‎

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#!/usr/bin/env python3
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import tensorflow as tf
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from tensorflow.python.saved_model import builder as saved_model_builder
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from tensorflow.python.saved_model import signature_constants
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from tensorflow.python.saved_model import signature_def_utils
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from tensorflow.python.saved_model import tag_constants
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from tensorflow.python.saved_model.utils import build_tensor_info
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x = tf.placeholder(tf.int32, name='x')
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y = tf.placeholder(tf.int32, name='y')
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# This is our model
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add = tf.add(x, y, name='ans')
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with tf.Session() as sess:
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# Pick out the model input and output
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x_tensor = sess.graph.get_tensor_by_name('x:0')
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y_tensor = sess.graph.get_tensor_by_name('y:0')
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ans_tensor = sess.graph.get_tensor_by_name('ans:0')
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x_info = build_tensor_info(x_tensor)
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y_info = build_tensor_info(y_tensor)
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ans_info = build_tensor_info(ans_tensor)
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# Create a signature definition for tfserving
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signature_definition = signature_def_utils.build_signature_def(
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inputs={'x': x_info, 'y': y_info},
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outputs={'ans': ans_info},
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method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
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builder = saved_model_builder.SavedModelBuilder('/tmp/tf_add_model')
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builder.add_meta_graph_and_variables(
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sess, [tag_constants.SERVING],
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signature_def_map={
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signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
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signature_definition
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})
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# Save the model so we can serve it with a model server :)
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builder.save()

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