Tensorflow Serving Test (Preprocessing)

infer.sh

curl "http://localhost:8501/v1/models/test-model:predict" -XPOST -d '{"instances": ["test"]}' | jq

run-tf-serving.sh

#!/bin/bash
docker run -t --rm -p 8501:8501 -v `pwd`/test-model:/models/test-model -eMODEL_NAME=test-model tensorflow/serving

train.py

import tensorflow as tf


(x_train, y_train), (x_test, y_test) = tf.keras.datasets.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=1)
model.evaluate(x_test, y_test, verbose=2)


@tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string)])
def serving(input_variable):
    def _input_to_feature(_):
        return tf.random.uniform((1, 28, 28))

    input_tensor = tf.map_fn(_input_to_feature, input_variable, dtype=tf.float32)

    # Predict
    predictions = model(input_tensor)

    return {"predictions": predictions}


tf.saved_model.save(model, "./test-model/1", signatures=serving)