Tensorflow Serving

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Train Your Own Model and Serve It With TensorFlow Serving

In this notebook, you will train a neural network to classify images of handwritten digits from the MNIST dataset. You will then save the trained model, and serve it using TensorFlow Serving.

Setup

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try:
    %tensorflow_version 2.x
except:
    pass
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import os
import json
import tempfile
import requests
import numpy as np
import matplotlib.pyplot as plt

import tensorflow as tf

print("\u2022 Using TensorFlow Version:", tf.__version__)

Using TensorFlow Version: 2.2.0-dev20200217

Import the MNIST Dataset

The MNIST dataset contains 70,000 grayscale images of the digits 0 through 9. The images show individual digits at a low resolution (28 by 28 pixels).

Even though these are really images, we will load them as NumPy arrays and not as binary image objects.

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mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 1s 0us/step

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# EXERCISE: Scale the values of the arrays below to be between 0.0 and 1.0.
train_images = train_images / 255.0
test_images =  test_images / 255.0
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train_images.shape, test_images.shape
((60000, 28, 28), (10000, 28, 28))

In the cell below use the .reshape method to resize the arrays to the following sizes:

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train_images.shape: (60000, 28, 28, 1)
test_images.shape: (10000, 28, 28, 1)
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# EXERCISE: Reshape the arrays below.
train_images = train_images.reshape((*train_images.shape,1))
test_images =  test_images.reshape((*test_images.shape,1))
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print('\ntrain_images.shape: {}, of {}'.format(train_images.shape, train_images.dtype))
print('test_images.shape: {}, of {}'.format(test_images.shape, test_images.dtype))
train_images.shape: (60000, 28, 28, 1), of float64
test_images.shape: (10000, 28, 28, 1), of float64

Look at a Sample Image

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idx = 42

plt.imshow(test_images[idx].reshape(28,28), cmap=plt.cm.binary)
plt.title('True Label: {}'.format(test_labels[idx]), fontdict={'size': 16})
plt.show()

png

Build a Model

In the cell below build a tf.keras.Sequential model that can be used to classify the images of the MNIST dataset. Feel free to use the simplest possible CNN. Make sure your model has the correct input_shape and the correct number of output units.

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# EXERCISE: Create a model.
model =  tf.keras.Sequential([
    
        tf.keras.layers.Conv2D(input_shape=(28,28,1), filters=8, kernel_size=3,
                               strides=2, activation='relu', name='Conv1'),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(10, activation=tf.nn.softmax, name='Softmax')
])

model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
Conv1 (Conv2D)               (None, 13, 13, 8)         80        
_________________________________________________________________
flatten (Flatten)            (None, 1352)              0         
_________________________________________________________________
Softmax (Dense)              (None, 10)                13530     
=================================================================
Total params: 13,610
Trainable params: 13,610
Non-trainable params: 0
_________________________________________________________________

Train the Model

In the cell below configure your model for training using the adam optimizer, sparse_categorical_crossentropy as the loss, and accuracy for your metrics. Then train the model for the given number of epochs, using the train_images array.

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# EXERCISE: Configure the model for training.
model.compile(optimizer = "adam", loss = "sparse_categorical_crossentropy", metrics=["accuracy"])

epochs = 5

# EXERCISE: Train the model.
history = model.fit(train_images, train_labels,
                    batch_size = 16,
                    epochs = epochs,
                    validation_data= [test_images, test_labels],
                    verbose = 1
                   )
Train on 60000 samples, validate on 10000 samples
Epoch 1/5
60000/60000 [==============================] - 8s 127us/sample - loss: 0.3098 - accuracy: 0.9120 - val_loss: 0.1723 - val_accuracy: 0.9489
Epoch 2/5
60000/60000 [==============================] - 7s 121us/sample - loss: 0.1511 - accuracy: 0.9569 - val_loss: 0.1145 - val_accuracy: 0.9667
Epoch 3/5
60000/60000 [==============================] - 7s 122us/sample - loss: 0.1103 - accuracy: 0.9680 - val_loss: 0.0939 - val_accuracy: 0.9720
Epoch 4/5
60000/60000 [==============================] - 7s 121us/sample - loss: 0.0901 - accuracy: 0.9737 - val_loss: 0.0895 - val_accuracy: 0.9739
Epoch 5/5
60000/60000 [==============================] - 7s 121us/sample - loss: 0.0780 - accuracy: 0.9763 - val_loss: 0.0787 - val_accuracy: 0.9758

Evaluate the Model

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# EXERCISE: Evaluate the model on the test images.
results_eval = model.evaluate(test_images, test_labels)

for metric, value in zip(model.metrics_names, results_eval):
    print(metric + ': {:.3}'.format(value))
10000/10000 [==============================] - 0s 39us/sample - loss: 0.0787 - accuracy: 0.9758
loss: 0.0787
accuracy: 0.976

Save the Model

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MODEL_DIR = "digits_model"

version = 1

export_path = os.path.join(MODEL_DIR, str(version))

if os.path.isdir(export_path):
    print('\nAlready saved a model, cleaning up\n')
    !rm -r {export_path}

model.save(export_path, save_format="tf")

print('\nexport_path = {}'.format(export_path))
!ls -l {export_path}

Examine Your Saved Model

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!saved_model_cli show --dir {export_path} --all
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is: 

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['Conv1_input'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 28, 28, 1)
        name: serving_default_Conv1_input:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['Softmax'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 10)
        name: StatefulPartitionedCall:0
  Method name is: tensorflow/serving/predict
WARNING:tensorflow:From /Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1809: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

Defined Functions:
  Function Name: '__call__'
    Option #1
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #3
      Callable with:
        Argument #1
          Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #4
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None

  Function Name: '_default_save_signature'
    Option #1
      Callable with:
        Argument #1
          Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')

  Function Name: 'call_and_return_all_conditional_losses'
    Option #1
      Callable with:
        Argument #1
          Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #3
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #4
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None

Add TensorFlow Serving Distribution URI as a Package Source

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# This is the same as you would do from your command line, but without the [arch=amd64], and no sudo
# You would instead do:
# echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list && \
# curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add -

!echo "deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | tee /etc/apt/sources.list.d/tensorflow-serving.list && \
curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | apt-key add -
!apt update
tee: /etc/apt/sources.list.d/tensorflow-serving.list: No such file or directory
deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal
Unable to locate an executable at "/Library/Java/JavaVirtualMachines/jdk1.8.0_73.jdk/Contents/Home/bin/apt" (-1)

Install TensorFlow Serving

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!apt-get install tensorflow-model-server
/bin/sh: apt-get: command not found

Run the TensorFlow Model Server

You will now launch the TensorFlow model server with a bash script. In the cell below use the following parameters when running the TensorFlow model server:

  • rest_api_port: Use port 8501 for your requests.
  • model_name: Use digits_model as your model name.
  • model_base_path: Use the environment variable MODEL_DIR defined below as the base path to the saved model.
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os.environ["MODEL_DIR"] = MODEL_DIR
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MODEL_DIR
'digits_model'
  • -p 8501:8501 : Publishing the container’s port 8501 (where TF Serving responds to REST API requests) to the host’s port 8501
  • —mount type=bind,source=/tmp/resnet,target=/models/resnet : Mounting the host’s local directory (/tmp/resnet) on the container (/models/resnet) so TF Serving can read the model from inside the container.
  • -e MODEL_NAME=digits_model : Telling TensorFlow Serving to load the model named “digits_model”
  • -t tensorflow/serving : Running a Docker container based on the serving image “tensorflow/serving”
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%%bash

nohup docker run -p 8501:8501 \
  --mount type=bind,source="/Users/ZRC/Desktop/TensorFlow Data and Deployment/week13/Exercises/digits_model,target=/models/digits_model" \
  -e MODEL_NAME=digits_model -t tensorflow/serving  &
docker: Error response from daemon: driver failed programming external connectivity on endpoint wonderful_ganguly (32049ac8fc320931031b817d6269004dcac5878b1e8c8addceb79fb1cd5dca24): Bind for 0.0.0.0:8501 failed: port is already allocated.

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# # EXERCISE: Fill in the missing code below.
# %%bash --bg 
# nohup tensorflow_model_server \
#   --rest_api_port=8501 \
#   --model_name=digits_model \
#   --model_base_path="${MODEL_DIR}" >server.log 2>&1
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!tail server.log
docker: Error response from daemon: driver failed programming external connectivity on endpoint determined_antonelli (6a4d67f3751bc7f9fed7821f6df430cbf368992e00c710738cb6f1d9f1e647d2): Bind for 0.0.0.0:8501 failed: port is already allocated.

Create JSON Object with Test Images

In the cell below construct a JSON object and use the first three images of the testing set (test_images) as your data.

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# EXERCISE: Create JSON Object
data = data = json.dumps({"signature_name": "serving_default", "instances": test_images[0:3].tolist()})

Make Inference Request

In the cell below, send a predict request as a POST to the server’s REST endpoint, and pass it your test data. You should ask the server to give you the latest version of your model.

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# EXERCISE: Fill in the code below
headers = {"content-type": "application/json"}
json_response = requests.post('http://localhost:8501/v1/models/digits_model:predict', data=data, headers=headers)
    
predictions = json.loads(json_response.text)['predictions']
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predictions
[[1.68959902e-09,
  3.5654768e-10,
  4.89267848e-07,
  6.09665512e-05,
  5.58686e-10,
  2.27727126e-09,
  8.49646383e-15,
  0.999936819,
  1.21679037e-07,
  1.63355e-06],
 [1.37625943e-06,
  6.47248962e-05,
  0.99961108,
  4.23558049e-06,
  5.8764843e-10,
  7.63888067e-07,
  0.000306190021,
  2.43645703e-15,
  1.15736648e-05,
  8.24755108e-12],
 [2.70507389e-05,
  0.996317267,
  0.00121238839,
  1.1719947e-05,
  0.00065574795,
  2.86702857e-06,
  0.000181563752,
  0.000955980679,
  0.000630841067,
  4.60029833e-06]]

Plot Predictions

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plt.figure(figsize=(10,15))

for i in range(3):
    plt.subplot(1,3,i+1)
    plt.imshow(test_images[i].reshape(28,28), cmap = plt.cm.binary)
    plt.axis('off')
    color = 'green' if np.argmax(predictions[i]) == test_labels[i] else 'red'
    plt.title('Prediction: {}\nTrue Label: {}'.format(np.argmax(predictions[i]), test_labels[i]), color=color)
    
plt.show()

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