Keras

Example of Keras framework with CUSTOM endpoints

Adding package to the classpath

Firstly, we require to include the main package to the classpath so that the notebook can load every one of the important libraries from Konduit-Serving into the Jupyter notebook kernel.

%classpath add jar ../../konduit.jar

Classpaths can be considered similar to site-packages in the python ecosystem where each library that's to be imported to your code is loaded from.

Viewing Python script code

We're creating a Keras model from scratch here and then converting that into .h5 (HDF5) format.

%%bash
less train.py

You can view and follow through the code to get more information on how the model is trained.

import tensorflow as tf

from keras.datasets import mnist


tensorflow_version = tf.__version__
print(tensorflow_version)

# Load data
train_data, test_data = mnist.load_data()
x_train, y_train = train_data
x_test, y_test = test_data

# Normalize
x_train = x_train / 255.0
x_test = x_test / 255.0


def get_model():
    inputs = tf.keras.layers.Input(shape=(28, 28), name="input_layer")
    x = tf.keras.layers.Flatten()(inputs)
    x = tf.keras.layers.Dense(200, activation="relu")(x)
    x = tf.keras.layers.Dense(100, activation="relu")(x)
    x = tf.keras.layers.Dense(60, activation="relu")(x)
    x = tf.keras.layers.Dense(30, activation="relu")(x)
    outputs = tf.keras.layers.Dense(10, activation="softmax", name="output_layer")(x)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    model.compile(
        optimizer='sgd',
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy']
    )

    return model


def train(epochs=8):
    model = get_model()
    model.fit(x_train, y_train, epochs=epochs)

    model.summary()

    print("\n\n---\n"
          "Inputs: {}".format(model.inputs))
    print("Outputs: {}\n---".format(model.outputs))

    return model


train(8).save("keras.h5", save_format="h5")

Starting a server

Let's start to serve the model in Konduit-Serving.

%%bash
konduit serve -id keras-mnist -c keras.json -rwm -b

You'll be able to see a similar output like below.

Starting konduit server...
Expected classpath: /root/konduit/bin/../konduit.jar
INFO: Running command /root/miniconda/jre/bin/java -Dkonduit.logs.file.path=/root/.konduit-serving/command_logs/keras-mnist.log -Dlogback.configurationFile=/tmp/logback-run_command_4c3da934e0334efc.xml -cp /root/konduit/bin/../konduit.jar ai.konduit.serving.cli.launcher.KonduitServingLauncher run --instances 1 -s inference -c keras.json -Dserving.id=keras-mnist
For server status, execute: 'konduit list'
For logs, execute: 'konduit logs keras-mnist'

List the active servers available by using konduit list command.

%%bash
konduit list

You'll see the following list of the active Konduit servers.

Listing konduit servers...

 #   | ID                             | TYPE       | URL                  | PID     | STATUS     
 1   | keras-mnist                    | inference  | localhost:33997      | 24142   | started  

View the logs for the last 100 lines for a given id by using the konduit logs command.

%%bash
konduit logs keras-mnist --lines 100

Logging is printed on the notebook once you run the above command.

.
.
. 

####################################################################
#                                                                  #
#    |  /   _ \   \ |  _ \  |  | _ _| __ __|    |  /     |  /      #
#    . <   (   | .  |  |  | |  |   |     |      . <      . <       #
#   _|\_\ \___/ _|\_| ___/ \__/  ___|   _|     _|\_\ _) _|\_\ _)   #
#                                                                  #
####################################################################

.
.
.
15:15:13.074 [vert.x-eventloop-thread-0] INFO  a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server is listening on host: 'localhost'
15:15:13.074 [vert.x-eventloop-thread-0] INFO  a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server started on port 33997 with 4 pipeline steps

Feeding an input to test the model

View the test image before testing the model

%%html
<img src="test-image.jpg" alt="title">

We're going to use available test image in this directory.

Let's predict the output from the server with the above input image.

%%bash
konduit predict keras-mnist -it multipart '[email protected]'

The output of classification:

• output_layer : probabilities of possible outputs

• prob : highest probability

• index : the location of an item in an array

• label : label of image classification

{
  "output_layer" : [ [ 9.0376153E-7, 1.0595608E-8, 1.3115231E-5, 0.44657645, 6.748624E-12, 0.5524258, 1.848306E-7, 2.7652052E-9, 9.76023E-4, 7.5933513E-6 ] ],
  "prob" : 0.5524258017539978,
  "index" : 5,
  "label" : "5"
}

Stopping the server

Once we're finished with the server, we can stop using the konduit stop command following the id's server.

%%bash
konduit stop keras-mnist

You'll be able to see the following message.

Stopping konduit server 'keras-mnist'
Application 'keras-mnist' terminated with status 0