Submarine model serving

• Save Model to submarine model registry by adding submarine.save_model(model, "tensorflow") in your script

import tensorflow_datasets as tfds
import tensorflow as tf
from tensorflow.keras import layers, models
import submarine


def make_datasets_unbatched():
    BUFFER_SIZE = 10000

    # Scaling MNIST data from (0, 255] to (0., 1.]
    def scale(image, label):
        image = tf.cast(image, tf.float32)
        image /= 255
        return image, label

    datasets, _ = tfds.load(name='mnist', with_info=True, as_supervised=True)

    return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE)


def build_and_compile_cnn_model():
    model = models.Sequential()
    model.add(
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu'))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu'))
    model.add(layers.Flatten())
    model.add(layers.Dense(64, activation='relu'))
    model.add(layers.Dense(10, activation='softmax'))

    model.summary()

    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

    return model


def main():
    strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
        communication=tf.distribute.experimental.CollectiveCommunication.AUTO)

    BATCH_SIZE_PER_REPLICA = 4
    BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync

    with strategy.scope():
        ds_train = make_datasets_unbatched().batch(BATCH_SIZE).repeat()
        options = tf.data.Options()
        options.experimental_distribute.auto_shard_policy = \
            tf.data.experimental.AutoShardPolicy.DATA
        ds_train = ds_train.with_options(options)
        # Model building/compiling need to be within `strategy.scope()`.
        multi_worker_model = build_and_compile_cnn_model()

    class MyCallback(tf.keras.callbacks.Callback):
        def on_epoch_end(self, epoch, logs=None):
            # monitor the loss and accuracy
            submarine.log_metric({"loss": logs["loss"], "accuracy": logs["accuracy"]}, epoch)

    model = multi_worker_model.fit(ds_train, epochs=2, steps_per_epoch=70, callbacks=[MyCallback()])
    submarine.save_model(model, "tensorflow")


if __name__ == '__main__':
    main()

• Check the output model in experiment page

• Click the button and register the model

• Go to the model page

• Deploy your model for serving