Machine Learning Platform for AI:Export TensorFlow models in the SavedModel format

Before you use the built-in official processor in Elastic Algorithm Service (EAS) of Machine Learning Platform for AI (PAI) to deploy a TensorFlow model service online, you must export the model in the SavedModel format. The SavedModel format is defined and recommended by TensorFlow. The SavedModel format uses the following directory structure:

assets/
variables/
    variables.data-00000-of-00001
    variables.index
saved_model.pb|saved_model.pbtxt

The directory structure includes the following directories:

For more information about how to use TensorFlow to export models in the SavedModel format, visit Saving and Restoring. If the model is simple, you can use the following method to export it in the SavedModel format:

tf.saved_model.simple_save(
  session,
  "./savedmodel/",
  inputs={"image": x},   ## x specifies the input variables of the model.
  outputs={"scores": y}  ## y specifies the output variables of the model.
)

When you call the online prediction service, you must set the signature_name parameter for the model in the request. If the model is exported by calling the simple_save() method, the default value of the signature_name parameter is serving_default.

If the model is complex, you can manually export it in the SavedModel format, as shown in the following sample code:

print('Exporting trained model to', export_path)
  builder = tf.saved_model.builder.SavedModelBuilder(export_path)
  tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
  tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
  prediction_signature = (
      tf.saved_model.signature_def_utils.build_signature_def(
          inputs={'images': tensor_info_x},
          outputs={'scores': tensor_info_y},
          method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
  legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
  builder.add_meta_graph_and_variables(
      sess, [tf.saved_model.tag_constants.SERVING],
      signature_def_map={
          'predict_images':
              prediction_signature,
      },
      legacy_init_op=legacy_init_op)
  builder.save()
  print('Done exporting!')

Take note of the following parameter descriptions:

You can call the model.save() method of Keras to convert a Keras model to the H5 format. However, a Keras model must be converted to the SavedModel format for online prediction. To implement the format conversion, you can call the load_model() method to load the H5 model, and then export it in the SavedModel format, as shown in the following sample code:

import tensorflow as tf
with tf.device("/cpu:0"):
    model = tf.keras.models.load_model('./mnist.h5')
    tf.saved_model.simple_save(
      tf.keras.backend.get_session(),
      "./h5_savedmodel/",
      inputs={"image": model.input},
      outputs={"scores": model.output}
    )

When you call the tf.train.Saver() method during model training, a model is saved in the Checkpoint format. You must convert it to the SavedModel format for online prediction. You can call the saver.restore() method to load the Checkpoint model as tf.Session, and then export the model in the SavedModel format, as shown in the following sample code:

import tensorflow as tf
# variable define ...
saver = tf.train.Saver()
with tf.Session() as sess:
  # Initialize v1 since the saver will not.
    saver.restore(sess, "./lr_model/model.ckpt")
    tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
    tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
    tf.saved_model.simple_save(
      sess,
      "./savedmodel/",
      inputs={"image": tensor_info_x},
      outputs={"scores": tensor_info_y}
    )