TensorFlow Serving - Amazon Deep Learning AMIs
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TensorFlow Serving

TensorFlow Serving is a flexible, high-performance serving system for machine learning models.

The tensorflow-serving-api is pre-installed with single framwork DLAMI. To use tensorflow serving, first activate the TensorFlow environment.

$ source /opt/tensorflow/bin/activate

Then use your preferred text editor to create a script that has the following content. Name it test_train_mnist.py. This script is referenced from TensorFlow Tutorial which will train and evaluate a neural network machine learning model that classifies images.

import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=5) model.evaluate(x_test, y_test)

Now run the script passing the server location and port and the husky photo's filename as the parameters.

$ /opt/tensorflow/bin/python3 test_train_mnist.py

Be patient, as this script may take a while before providing any output. When the training is complete you should see the following:

I0000 00:00:1739482012.389276 4284 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process. 1875/1875 [==============================] - 24s 2ms/step - loss: 0.2973 - accuracy: 0.9134 Epoch 2/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.1422 - accuracy: 0.9582 Epoch 3/5 1875/1875 [==============================] - 3s 1ms/step - loss: 0.1076 - accuracy: 0.9687 Epoch 4/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0872 - accuracy: 0.9731 Epoch 5/5 1875/1875 [==============================] - 3s 1ms/step - loss: 0.0731 - accuracy: 0.9771 313/313 [==============================] - 0s 1ms/step - loss: 0.0749 - accuracy: 0.9780

More Features and Examples

If you are interested in learning more about TensorFlow Serving, check out the TensorFlow website.