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.
