Keras
Example of Keras framework with CUSTOM endpoints
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.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")
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
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 outputsprob: highest probabilityindex: the location of an item in an arraylabel: 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"
}
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
Last modified 1yr ago