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annotate MLaaS/README.md @ 0:cbbe42422d56 draft
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author | kls286 |
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date | Tue, 28 Mar 2023 15:07:30 +0000 |
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1 ## MLaaS end-to-end example using MNIST dataset |
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2 MLaaS stands for Machine Learning as a Service, and here we'll provide |
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3 end-to-end example based on MNIST dataset using |
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4 Python [Keras](https://keras.io/) ML framework for training |
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5 part, and [TFaas](https://github.com/vkuznet/TFaaS) ML framework |
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6 for inference part. |
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7 |
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8 ### Requirements (environment) |
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9 To proceed with ML trainig we need to acquire MNIST dataset. |
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10 We will assume that you have a box where recent version of python is installed, |
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11 please note that instructions were tested with `Python 3.10.10` |
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12 |
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13 ``` |
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14 # create mnist_env, here python refers to python 3.10.10 |
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15 python -m venv mnist_env |
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16 |
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17 # download mnist dataset for training purposes in numpy gziped arrays |
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18 curl -ksLO https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz |
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19 |
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20 # download MNIST dataset for training purposes in pkl.gz data-format |
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21 curl -ksLO https://s3.amazonaws.com/img-datasets/mnist.pkl.gz |
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22 |
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23 # download MNIST images |
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24 # download MNIST actual images which we will use within inference |
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25 curl -O http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz |
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26 ``` |
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27 |
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28 ### Train ML model |
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29 Below you can see fully tested Keras mased ML codebase to train |
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30 simple convolutional neural network over MNIST dataset (save |
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31 this code as `ktrain.py`): |
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32 ``` |
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33 #!/usr/bin/env python |
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34 #-*- coding: utf-8 -*- |
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35 #pylint: disable= |
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36 """ |
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37 File : ktrain.py |
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38 Author : Valentin Kuznetsov <vkuznet AT gmail dot com> |
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39 Description: Keras based ML network to train over MNIST dataset |
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40 """ |
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41 |
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42 # system modules |
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43 import os |
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44 import sys |
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45 import json |
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46 import gzip |
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47 import pickle |
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48 import argparse |
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49 |
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50 # third-party modules |
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51 import numpy as np |
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52 import tensorflow as tf |
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53 from tensorflow import keras |
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54 from tensorflow.keras import layers |
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55 from tensorflow.keras import backend as K |
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56 from tensorflow.python.tools import saved_model_utils |
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57 |
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58 |
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59 def modelGraph(model_dir): |
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60 """ |
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61 Provide input/output names used by TF Graph along with graph itself |
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62 The code is based on TF saved_model_cli.py script. |
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63 """ |
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64 input_names = [] |
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65 output_names = [] |
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66 tag_sets = saved_model_utils.get_saved_model_tag_sets(model_dir) |
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67 for tag_set in sorted(tag_sets): |
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68 print('%r' % ', '.join(sorted(tag_set))) |
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69 meta_graph_def = saved_model_utils.get_meta_graph_def(model_dir, tag_set[0]) |
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70 for key in meta_graph_def.signature_def.keys(): |
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71 meta = meta_graph_def.signature_def[key] |
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72 if hasattr(meta, 'inputs') and hasattr(meta, 'outputs'): |
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73 inputs = meta.inputs |
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74 outputs = meta.outputs |
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75 input_signatures = list(meta.inputs.values()) |
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76 input_names = [signature.name for signature in input_signatures] |
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77 if len(input_names) > 0: |
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78 output_signatures = list(meta.outputs.values()) |
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79 output_names = [signature.name for signature in output_signatures] |
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80 return input_names, output_names, meta_graph_def |
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81 |
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82 def readData(fin, num_classes): |
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83 """ |
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84 Helper function to read MNIST data and provide it to |
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85 upstream code, e.g. to the training layer |
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86 """ |
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87 # Load the data and split it between train and test sets |
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88 # (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() |
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89 f = gzip.open(fin, 'rb') |
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90 if sys.version_info < (3,): |
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91 mnist_data = pickle.load(f) |
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92 else: |
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93 mnist_data = pickle.load(f, encoding='bytes') |
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94 f.close() |
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95 (x_train, y_train), (x_test, y_test) = mnist_data |
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96 |
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97 # Scale images to the [0, 1] range |
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98 x_train = x_train.astype("float32") / 255 |
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99 x_test = x_test.astype("float32") / 255 |
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100 # Make sure images have shape (28, 28, 1) |
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101 x_train = np.expand_dims(x_train, -1) |
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102 x_test = np.expand_dims(x_test, -1) |
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103 print("x_train shape:", x_train.shape) |
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104 print(x_train.shape[0], "train samples") |
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105 print(x_test.shape[0], "test samples") |
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106 |
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107 |
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108 # convert class vectors to binary class matrices |
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109 y_train = keras.utils.to_categorical(y_train, num_classes) |
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110 y_test = keras.utils.to_categorical(y_test, num_classes) |
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111 return x_train, y_train, x_test, y_test |
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112 |
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113 |
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114 def train(fin, fout=None, model_name=None, epochs=1, batch_size=128, h5=False): |
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115 """ |
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116 train function for MNIST |
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117 """ |
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118 # Model / data parameters |
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119 num_classes = 10 |
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120 input_shape = (28, 28, 1) |
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121 |
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122 # create ML model |
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123 model = keras.Sequential( |
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124 [ |
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125 keras.Input(shape=input_shape), |
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126 layers.Conv2D(32, kernel_size=(3, 3), activation="relu"), |
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127 layers.MaxPooling2D(pool_size=(2, 2)), |
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128 layers.Conv2D(64, kernel_size=(3, 3), activation="relu"), |
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129 layers.MaxPooling2D(pool_size=(2, 2)), |
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130 layers.Flatten(), |
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131 layers.Dropout(0.5), |
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132 layers.Dense(num_classes, activation="softmax"), |
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133 ] |
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134 ) |
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135 |
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136 model.summary() |
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137 print("model input", model.input, type(model.input), model.input.__dict__) |
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138 print("model output", model.output, type(model.output), model.output.__dict__) |
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139 model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) |
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140 |
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141 # train model |
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142 x_train, y_train, x_test, y_test = readData(fin, num_classes) |
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143 model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1) |
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144 |
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145 # evaluate trained model |
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146 score = model.evaluate(x_test, y_test, verbose=0) |
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147 print("Test loss:", score[0]) |
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148 print("Test accuracy:", score[1]) |
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149 print("save model to", fout) |
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150 writer(fout, model_name, model, input_shape, h5) |
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151 |
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152 def writer(fout, model_name, model, input_shape, h5=False): |
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153 """ |
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154 Writer provide write function for given model |
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155 """ |
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156 if not fout: |
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157 return |
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158 model.save(fout) |
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159 if h5: |
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160 model.save('{}/{}'.format(fout, h5), save_format='h5') |
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161 pbModel = '{}/saved_model.pb'.format(fout) |
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162 pbtxtModel = '{}/saved_model.pbtxt'.format(fout) |
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163 convert(pbModel, pbtxtModel) |
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164 |
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165 # get meta-data information about our ML model |
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166 input_names, output_names, model_graph = modelGraph(model_name) |
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167 print("### input", input_names) |
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168 print("### output", output_names) |
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169 # ML uses (28,28,1) shape, i.e. 28x28 black-white images |
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170 # if we'll use color images we'll use shape (28, 28, 3) |
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171 img_channels = input_shape[2] # last item represent number of colors |
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172 meta = {'name': model_name, |
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173 'model': 'saved_model.pb', |
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174 'labels': 'labels.txt', |
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175 'img_channels': img_channels, |
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176 'input_name': input_names[0].split(':')[0], |
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177 'output_name': output_names[0].split(':')[0], |
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178 'input_node': model.input.name, |
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179 'output_node': model.output.name |
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180 } |
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181 with open(fout+'/params.json', 'w') as ostream: |
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182 ostream.write(json.dumps(meta)) |
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183 with open(fout+'/labels.txt', 'w') as ostream: |
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184 for i in range(0, 10): |
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185 ostream.write(str(i)+'\n') |
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186 with open(fout + '/model.graph', 'wb') as ostream: |
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187 ostream.write(model_graph.SerializeToString()) |
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188 |
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189 def convert(fin, fout): |
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190 """ |
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191 convert input model.pb into output model.pbtxt |
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192 Based on internet search: |
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193 - https://www.tensorflow.org/guide/saved_model |
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194 - https://www.programcreek.com/python/example/123317/tensorflow.core.protobuf.saved_model_pb2.SavedModel |
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195 """ |
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196 import google.protobuf |
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197 from tensorflow.core.protobuf import saved_model_pb2 |
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198 import tensorflow as tf |
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199 |
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200 saved_model = saved_model_pb2.SavedModel() |
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201 |
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202 with open(fin, 'rb') as f: |
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203 saved_model.ParseFromString(f.read()) |
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204 |
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205 with open(fout, 'w') as f: |
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206 f.write(google.protobuf.text_format.MessageToString(saved_model)) |
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207 |
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208 |
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209 class OptionParser(): |
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210 def __init__(self): |
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211 "User based option parser" |
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212 self.parser = argparse.ArgumentParser(prog='PROG') |
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213 self.parser.add_argument("--fin", action="store", |
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214 dest="fin", default="", help="Input MNIST file") |
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215 self.parser.add_argument("--fout", action="store", |
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216 dest="fout", default="", help="Output models area") |
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217 self.parser.add_argument("--model", action="store", |
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218 dest="model", default="mnist", help="model name") |
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219 self.parser.add_argument("--epochs", action="store", |
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220 dest="epochs", default=1, help="number of epochs to use in ML training") |
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221 self.parser.add_argument("--batch_size", action="store", |
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222 dest="batch_size", default=128, help="batch size to use in training") |
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223 self.parser.add_argument("--h5", action="store", |
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224 dest="h5", default="mnist", help="h5 model file name") |
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225 |
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226 def main(): |
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227 "Main function" |
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228 optmgr = OptionParser() |
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229 opts = optmgr.parser.parse_args() |
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230 train(opts.fin, opts.fout, |
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231 model_name=opts.model, |
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232 epochs=opts.epochs, |
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233 batch_size=opts.batch_size, |
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234 h5=opts.h5) |
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235 |
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236 if __name__ == '__main__': |
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237 main() |
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238 ``` |
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239 |
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240 ### Training process |
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241 We will train our model using the following command (for simplicity we skip |
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242 warning messages from TF and irrelevant printouts): |
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243 ``` |
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244 # here fout=mnist represents mnist directory where we'll stored our trained model |
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245 # and model=mnist is the name of the model we'll use later in inference |
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246 ./ktrain.py --fin=./mnist.pkl.gz --fout=mnist --model=mnist |
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247 ... |
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248 x_train shape: (60000, 28, 28, 1) |
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249 60000 train samples |
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250 10000 test samples |
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251 Model: "sequential" |
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252 _________________________________________________________________ |
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253 Layer (type) Output Shape Param # |
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254 ================================================================= |
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255 conv2d (Conv2D) (None, 26, 26, 32) 320 |
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256 |
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257 max_pooling2d (MaxPooling2D (None, 13, 13, 32) 0 |
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258 ) |
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259 |
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260 conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 |
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261 |
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262 max_pooling2d_1 (MaxPooling (None, 5, 5, 64) 0 |
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263 2D) |
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264 |
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265 flatten (Flatten) (None, 1600) 0 |
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266 |
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267 dropout (Dropout) (None, 1600) 0 |
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268 |
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269 dense (Dense) (None, 10) 16010 |
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270 |
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271 ================================================================= |
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272 Total params: 34,826 |
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273 Trainable params: 34,826 |
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274 Non-trainable params: 0 |
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275 _________________________________________________________________ |
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276 |
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277 422/422 [==============================] - 37s 84ms/step - loss: 0.3645 - accuracy: 0.8898 - val_loss: 0.0825 - val_accuracy: 0.9772 |
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278 Test loss: 0.09409885853528976 |
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279 Test accuracy: 0.9703999757766724 |
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280 save model to mnist |
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281 |
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282 ### input ['serving_default_input_1:0'] |
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283 ### output ['StatefulPartitionedCall:0'] |
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284 ``` |
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285 When this process is over you'll find `mnist` directory with the following |
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286 content: |
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287 ``` |
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288 shell# ls mnist |
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289 |
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290 assets keras_metadata.pb model.graph saved_model.pb variables |
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291 fingerprint.pb labels.txt params.json saved_model.pbtxt |
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292 ``` |
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293 - `saved_model.pb` represents trained ML model in protobuffer data-format |
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294 - `saved_model.pbtxt` represents trained ML model in text protobuffer representation |
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295 - `labels.txt` contains our image labels |
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296 - `params.json` contains meta-data used by TFaaS and it has the following content: |
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297 ``` |
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298 cat mnist/params.json | jq |
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299 { |
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300 "name": "mnist", |
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301 "model": "saved_model.pb", |
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302 "labels": "labels.txt", |
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303 "img_channels": 1, |
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304 "input_name": "serving_default_input_1", |
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305 "output_name": "StatefulPartitionedCall", |
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306 "input_node": "input_1", |
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307 "output_node": "dense/Softmax:0" |
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308 } |
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309 ``` |
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310 Here you see, that our ML model is called `mnist`, the model is stored in |
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311 `saved_model.pb` file, and more importantly this file contains the input and |
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312 output tensor names and nodes which we need to provide for TFaaS to server |
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313 our predictions. |
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314 |
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315 ### Inference server |
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316 Now, it is time to start our inference server. You can find its code in `src/go` area. |
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317 To build the code you need |
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318 ``` |
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319 # download TF library and includes for your OS, e.g. macOS build |
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320 curl -ksLO https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.11.0.tar.gz |
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321 # or linux build |
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322 curl -ksLO https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.11.0.tar.gz |
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323 # or linux GPU build |
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324 curl -ksLO https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.11.0.tar.gz |
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325 |
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326 # provide TF include area location to go build command |
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327 # the /opt/tensorflow/include is are where TF includes are |
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328 export CGO_CPPFLAGS="-I/opt/tensorflow/include" |
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329 |
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330 # compile the code |
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331 make |
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332 |
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333 # it will produce tfaas executable |
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334 |
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335 # to run the code we need to setup `DYLD_LIBRARY_PATH` |
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336 export DYLD_LIBRARY_PATH=/opt/tensorflow/lib |
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337 ./tfaas -config config.json |
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338 ``` |
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339 where `config.json` has the following form (please refer for more details): |
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340 ``` |
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341 { |
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342 "port": 8083, |
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343 "modelDir": "models", |
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344 "staticDir": "static", |
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345 "configProto": "", |
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346 "base": "", |
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347 "serverKey": "", |
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348 "serverCrt": "", |
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349 "verbose": 1 |
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350 } |
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351 ``` |
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352 |
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353 ### Serving predictions with TFaaS inference server |
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354 Finally, we are ready for the inference part. |
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355 - upload your ML model to TFaaS server |
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356 ``` |
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357 # create tarball of your mnist ML trained model |
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358 tar cfz mnist.tar.gz mnist |
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359 |
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360 # upload tarball to TFaaS server |
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361 curl -v -X POST -H "Content-Encoding: gzip" \ |
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362 -H "Content-Type: application/octet-stream" \ |
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363 --data-binary @./mnist.tar.gz \ |
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364 http://localhost:8083/upload |
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365 |
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366 # check your model presence |
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367 curl http://localhost:8083/models |
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368 |
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369 # generate image from MNIST dataset you want to use for prediction |
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370 # img1.png will contain number 1, img4.png will contain number 4 |
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371 ./mnist_img.py --fout img1.png --imgid=3 |
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372 ./mnist_img.py --fout img4.png --imgid=2 |
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373 |
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374 # ask for prediction of your image |
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375 curl http://localhost:8083/predict/image -F 'image=@./img1.png' -F 'model=mnist' |
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376 [0,1,0,0,0,0,0,0,0,0] |
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377 |
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378 curl http://localhost:8083/predict/image -F 'image=@./img4.png' -F 'model=mnist' |
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379 [0,0,0,0,1,0,0,0,0,0] |
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380 ``` |