├── .gitattributes
├── save
├── checkpoint
├── test.ckpt.meta
├── test.ckpt.index
└── test.ckpt.data-00000-of-00001
├── serve
└── test
│ └── 1
│ ├── saved_model.pb
│ └── variables
│ ├── variables.index
│ └── variables.data-00000-of-00001
├── README.md
├── .idea
├── modules.xml
├── misc.xml
├── serving.iml
└── workspace.xml
├── client.py
├── LICENSE
├── serve.py
├── .gitignore
└── train.py
/.gitattributes:
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1 | # Auto detect text files and perform LF normalization
2 | * text=auto
3 |
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/save/checkpoint:
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1 | model_checkpoint_path: "test.ckpt"
2 | all_model_checkpoint_paths: "test.ckpt"
3 |
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/save/test.ckpt.meta:
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https://raw.githubusercontent.com/FrancescoSaverioZuppichini/TensorFlow-Serving-Example/HEAD/save/test.ckpt.meta
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/save/test.ckpt.index:
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https://raw.githubusercontent.com/FrancescoSaverioZuppichini/TensorFlow-Serving-Example/HEAD/save/test.ckpt.index
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/serve/test/1/saved_model.pb:
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https://raw.githubusercontent.com/FrancescoSaverioZuppichini/TensorFlow-Serving-Example/HEAD/serve/test/1/saved_model.pb
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/README.md:
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1 | # TensorFlow Serving Example
2 |
3 | Code example for my medium article https://towardsdatascience.com/deploy-tensorflow-models-9813b5a705d5
4 |
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/save/test.ckpt.data-00000-of-00001:
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https://raw.githubusercontent.com/FrancescoSaverioZuppichini/TensorFlow-Serving-Example/HEAD/save/test.ckpt.data-00000-of-00001
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/serve/test/1/variables/variables.index:
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https://raw.githubusercontent.com/FrancescoSaverioZuppichini/TensorFlow-Serving-Example/HEAD/serve/test/1/variables/variables.index
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/serve/test/1/variables/variables.data-00000-of-00001:
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https://raw.githubusercontent.com/FrancescoSaverioZuppichini/TensorFlow-Serving-Example/HEAD/serve/test/1/variables/variables.data-00000-of-00001
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/.idea/modules.xml:
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/.idea/misc.xml:
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1 |
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3 |
4 | ApexVCS
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7 |
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/.idea/serving.iml:
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/client.py:
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1 | import numpy as np
2 | from predict_client.prod_client import ProdClient
3 | from flask import Flask
4 | from flask import request
5 | from flask import jsonify
6 |
7 | HOST = 'localhost:9000'
8 | MODEL_NAME = 'test'
9 | MODEL_VERSION = 1
10 |
11 | app = Flask(__name__)
12 | client = ProdClient(HOST, MODEL_NAME, MODEL_VERSION)
13 |
14 | def convert_data(raw_data):
15 | return np.array(raw_data, dtype=np.float32)
16 |
17 | def get_prediction_from_model(data):
18 | req_data = [{'in_tensor_name': 'inputs', 'in_tensor_dtype': 'DT_FLOAT', 'data': data}]
19 |
20 | prediction = client.predict(req_data, request_timeout=10)
21 |
22 | return prediction
23 |
24 |
25 | @app.route("/prediction", methods=['POST'])
26 | def get_prediction():
27 | req_data = request.get_json()
28 | raw_data = req_data['data']
29 |
30 | data = convert_data(raw_data)
31 | prediction = get_prediction_from_model(data)
32 |
33 | # ndarray cannot be converted to JSON
34 | return jsonify({ 'predictions': prediction['outputs'].tolist() })
35 |
36 | if __name__ == '__main__':
37 | app.run(host='localhost',port=3000)
38 |
39 |
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/LICENSE:
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1 | MIT License
2 |
3 | Copyright (c) 2018 FrancescoSaverioZuppichini
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
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/serve.py:
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1 | import tensorflow as tf
2 | import os
3 |
4 | SAVE_PATH = './save'
5 | MODEL_NAME = 'test'
6 | VERSION = 1
7 | SERVE_PATH = './serve/{}/{}'.format(MODEL_NAME, VERSION)
8 |
9 | checkpoint = tf.train.latest_checkpoint(SAVE_PATH)
10 |
11 | tf.reset_default_graph()
12 |
13 | with tf.Session() as sess:
14 | # import the saved graph
15 | saver = tf.train.import_meta_graph(checkpoint + '.meta')
16 | # get the graph for this session
17 | graph = tf.get_default_graph()
18 | sess.run(tf.global_variables_initializer())
19 | # get the tensors that we need
20 | inputs = graph.get_tensor_by_name('inputs:0')
21 | predictions = graph.get_tensor_by_name('prediction/Sigmoid:0')
22 | # create tensors info
23 | model_input = tf.saved_model.utils.build_tensor_info(inputs)
24 | model_output = tf.saved_model.utils.build_tensor_info(predictions)
25 | # build signature definition
26 | signature_definition = tf.saved_model.signature_def_utils.build_signature_def(
27 | inputs={'inputs': model_input},
28 | outputs={'outputs': model_output},
29 | method_name= tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
30 |
31 | builder = tf.saved_model.builder.SavedModelBuilder(SERVE_PATH)
32 |
33 | builder.add_meta_graph_and_variables(
34 | sess, [tf.saved_model.tag_constants.SERVING],
35 | signature_def_map={
36 | tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
37 | signature_definition
38 | })
39 | # Save the model so we can serve it with a model server :)
40 | builder.save()
41 |
42 |
43 |
44 |
45 |
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/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | .idea/
7 | # C extensions
8 | *.so
9 |
10 | # Distribution / packaging
11 | .Python
12 | build/
13 | develop-eggs/
14 | dist/
15 | downloads/
16 | eggs/
17 | .eggs/
18 | lib/
19 | lib64/
20 | parts/
21 | sdist/
22 | var/
23 | wheels/
24 | *.egg-info/
25 | .installed.cfg
26 | *.egg
27 |
28 | # PyInstaller
29 | # Usually these files are written by a python script from a template
30 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
31 | *.manifest
32 | *.spec
33 |
34 | # Installer logs
35 | pip-log.txt
36 | pip-delete-this-directory.txt
37 |
38 | # Unit test / coverage reports
39 | htmlcov/
40 | .tox/
41 | .coverage
42 | .coverage.*
43 | .cache
44 | nosetests.xml
45 | coverage.xml
46 | *.cover
47 | .hypothesis/
48 |
49 | # Translations
50 | *.mo
51 | *.pot
52 |
53 | # Django stuff:
54 | *.log
55 | local_settings.py
56 |
57 | # Flask stuff:
58 | instance/
59 | .webassets-cache
60 |
61 | # Scrapy stuff:
62 | .scrapy
63 |
64 | # Sphinx documentation
65 | docs/_build/
66 |
67 | # PyBuilder
68 | target/
69 |
70 | # Jupyter Notebook
71 | .ipynb_checkpoints
72 |
73 | # pyenv
74 | .python-version
75 |
76 | # celery beat schedule file
77 | celerybeat-schedule
78 |
79 | # SageMath parsed files
80 | *.sage.py
81 |
82 | # Environments
83 | .env
84 | .venv
85 | env/
86 | venv/
87 | ENV/
88 |
89 | # Spyder project settings
90 | .spyderproject
91 | .spyproject
92 |
93 | # Rope project settings
94 | .ropeproject
95 |
96 | # mkdocs documentation
97 | /site
98 |
99 | # mypy
100 | .mypy_cache/
101 |
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/train.py:
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1 | import tensorflow as tf
2 | import numpy as np
3 | import os, sys
4 |
5 | DATA_SIZE = 100
6 | SAVE_PATH = './save'
7 | EPOCHS = 1000
8 | LEARNING_RATE = 0.01
9 | MODEL_NAME = 'test'
10 |
11 | if not os.path.exists(SAVE_PATH):
12 | os.mkdir(SAVE_PATH)
13 |
14 | data = (np.random.rand(DATA_SIZE, 2), np.random.rand(DATA_SIZE, 1))
15 | test = (np.random.rand(DATA_SIZE // 8, 2), np.random.rand(DATA_SIZE // 8, 1))
16 |
17 | tf.reset_default_graph()
18 |
19 | x = tf.placeholder(tf.float32, shape=[None, 2], name='inputs')
20 | y = tf.placeholder(tf.float32, shape=[None, 1], name='targets')
21 |
22 | net = tf.layers.dense(x, 16, activation=tf.nn.relu)
23 | net = tf.layers.dense(net, 16, activation=tf.nn.relu)
24 | pred = tf.layers.dense(net, 1, activation=tf.nn.sigmoid, name='prediction')
25 |
26 | loss = tf.reduce_mean(tf.squared_difference(y, pred), name='loss')
27 | train_step = tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss)
28 |
29 | checkpoint = tf.train.latest_checkpoint(SAVE_PATH)
30 | should_train = checkpoint == None
31 |
32 | with tf.Session() as sess:
33 | sess.run(tf.global_variables_initializer())
34 | if should_train:
35 | print("Training")
36 | saver = tf.train.Saver()
37 | for epoch in range(EPOCHS):
38 | _, curr_loss = sess.run([train_step, loss], feed_dict={x: data[0], y: data[1]})
39 | print('EPOCH = {}, LOSS = {:0.4f}'.format(epoch, curr_loss))
40 | path = saver.save(sess, SAVE_PATH + '/' + MODEL_NAME + '.ckpt')
41 | print("saved at {}".format(path))
42 | else:
43 | print("Restoring")
44 | graph = tf.get_default_graph()
45 | saver = tf.train.import_meta_graph(checkpoint + '.meta')
46 | saver.restore(sess, checkpoint)
47 |
48 | loss = graph.get_tensor_by_name('loss:0')
49 |
50 | test_loss = sess.run(loss, feed_dict={'inputs:0': test[0], 'targets:0': test[1]})
51 | print(sess.run(pred, feed_dict={'inputs:0': np.random.rand(10,2)}))
52 | print("TEST LOSS = {:0.4f}".format(test_loss))
53 |
54 |
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