Tensorflow serve on Sagemaker

save-model-to-sagemaker.py

def export_h5_to_pb(path_to_h5, export_path):

    # Set the learning phase to Test since the model is already trained.
    K.set_learning_phase(0)

    # Load the Keras model
    keras_model = load_model(path_to_h5)

    # Build the Protocol Buffer SavedModel at 'export_path'
    builder = saved_model_builder.SavedModelBuilder(export_path)

    # Create prediction signature to be used by TensorFlow Serving Predict API
    signature = predict_signature_def(inputs={"images": keras_model.input},
                                      outputs={"scores": keras_model.output})

    with K.get_session() as sess:
        # Save the meta graph and the variables
        builder.add_meta_graph_and_variables(sess=sess, tags=[tag_constants.SERVING],
                                         signature_def_map={"serving_default": signature})

    builder.save()