Platform For AI:model export and deployment by using PAI-TensorFlow

Export general models in the SavedModel format

• SavedModel format

  In versions later than TensorFlow 1.0, SavedModel rather than SessionBundle is recommended to save models. The following code shows the structure of a SavedModel directory:

  assets/assets.extra/variables/ variables.data-xxxxx-of-xxxxx variables.indexsaved_model.pb

  For more information about the subdirectories and files in the directory, see TensorFlow SavedModel documentation or SavedModel introduction.

• Export models in the SavedModel format

  Sample code:

  class Softmax(object):
      def __init__(self):
          self.weights_ = tf.Variable(tf.zeros([FLAGS.image_size, FLAGS.num_classes]),
                  name='weights')
          self.biases_ = tf.Variable(tf.zeros([FLAGS.num_classes]),
                  name='biases')
  
      def signature_def(self):
          images = tf.placeholder(tf.uint8, [None, FLAGS.image_size],
              name='input')
          normalized_images = tf.scalar_mul(1.0 / FLAGS.image_depth,
              tf.to_float(images))
          scores = self.scores(normalized_images)
          tensor_info_x = tf.saved_model.utils.build_tensor_info(images)
          tensor_info_y = tf.saved_model.utils.build_tensor_info(scores)
          return 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)
      def savedmodel(self, sess, signature, path):
          export_dir = os.path.join(path, str(FLAGS.model_version))
          builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
          builder.add_meta_graph_and_variables(
              sess, [tf.saved_model.tag_constants.SERVING],
              signature_def_map={
                  'predict_images':
                      signature,
              },
              clear_devices=True)
          builder.save()
  
  model = Softmax()
  signature = model.signature_def()
  
  model.savedmodel(sess, signature, mnist.export_path())

  In the preceding code:

  • The Softmax class encapsulates a machine learning model. weights and biases are the main model parameters.

  • The signature_def method describes how to obtain the output logic from a placeholder by using data standardization and forward computation during prediction. It also describes how to use the placeholder as the input and the result as the output to create a SignatureDef.

  Export models in the SavedModel format to Object Storage Service (OSS):

  Run the following command to train and export the models in the SavedModel format:

  PAI -name tensorflow
      -Dscript="file://path/to/mnist_savedmodel_oss.py"
      -Dbuckets="oss://mnistdataset/?host=oss-test.aliyun-inc.com&role_arn=acs:ram::127488******:role/odps"
      -DcheckpointDir="oss://mnistdataset/?host=oss-test.aliyun-inc.com&role_arn=acs:ram::127488*********:role/odps";

Save a model as a checkpoint and restore a model from a checkpoint

• Save a model as a checkpoint

  The following example shows how to use TensorFlow to save models in non-interactive mode:

  # -*- coding: utf-8 -*-
  # usage
  # pai -name tensorflow -DcheckpointDir="oss://tftest/examples/?host=oss-test.aliyun-inc.com&role_arn=acs:ram::****:role/odps" -Dscript="file:///path/to/save_model.py";
  import tensorflow as tf
  import json
  import os
  tf.app.flags.DEFINE_string("checkpointDir", "", "oss info")
  FLAGS = tf.app.flags.FLAGS
  print("checkpoint dir:" + FLAGS.checkpointDir)
  # Define variables.
  counter = tf.Variable(1, name="counter")
  one = tf.constant(2)
  sum = tf.add(counter, one)
  new_counter = tf.assign(counter, sum)
  saver = tf.train.Saver()
  init_op = tf.global_variables_initializer()
  with tf.Session() as sess:
      sess.run(init_op)
      coord = tf.train.Coordinator()
      threads = tf.train.start_queue_runners(coord=coord)
      ret = sess.run(new_counter)
      print("Counter:%d" % ret)
      ckp_path = os.path.join(FLAGS.checkpointDir, "model.ckpt")
      save_path = saver.save(sess, ckp_path)
      print("Model saved in file: %s" % save_path)
      coord.request_stop()
      coord.join(threads)

  You can use tf.app.flags.DEFINE_string() and tf.app.flags.FLAGS to obtain the value of checkpointDir in the PAI command. checkpointDir specifies that the model is to be stored in OSS.

  The following code calculates new_counter and saves the counter variable in the model. The variable value is 3. save_path = saver.save(sess, ckp_path) specifies the OSS path to which the model is stored.

  ret = sess.run(new_counter)
  print("Counter:%d" % ret)
  ckp_path = os.path.join(FLAGS.checkpointDir, "model.ckpt")
  save_path = saver.save(sess, ckp_path)
  print("Model saved in file: %s" % save_path)

• Restore a model from a checkpoint

  The Saver class of TensorFlow can also be used to restore models. The following example shows how to use TensorFlow to restore models:

  # -*- coding: utf-8 -*-
  # usage
  # pai -name tensorflow -Dbuckets="oss://tftest/examples/?host=oss-test.aliyun-inc.com&role_arn=acs:ram::***:role/odps" -Dscript="file:///path/to/restore_model.py";
  import tensorflow as tf
  import json
  import os
  tf.app.flags.DEFINE_string("buckets", "", "oss info")
  FLAGS = tf.app.flags.FLAGS
  print("buckets:" + FLAGS.buckets)
  # Define variables.
  counter = tf.Variable(1, name="counter")
  saver = tf.train.Saver()
  init_op = tf.global_variables_initializer()
  with tf.Session() as sess:
      sess.run(init_op)
      coord = tf.train.Coordinator()
      threads = tf.train.start_queue_runners(coord=coord)
      ret = sess.run(counter)
      print("Before restore counter:%d" % ret)
      print("Model restore from file")
      ckp_path = os.path.join(FLAGS.buckets, "model.ckpt")
      saver.restore(sess, ckp_path)
      ret = sess.run(counter)
      print("After restore counter:%d" % ret)
      coord.request_stop()
      coord.join(threads)

  You can use tf.app.flags.DEFINE_string() and tf.app.flags.FLAGS to obtain the value of buckets in the Platform for AI (PAI) command. buckets specifies that the model is to be restored from OSS.

  In the following code, a variable named counter is defined. The initial value is 1. Call saver.restore(sess, ckp_path) to restore the model from the specified OSS path. Then, run ret = sess.run(counter). The value of the counter variable is still 3 after restoration.

  ret = sess.run(counter)
  print("Before restore counter:%d" % ret)
  print("Model restore from file")
  ckp_path = os.path.join(FLAGS.buckets, "model.ckpt")
  saver.restore(sess, ckp_path)
  ret = sess.run(counter)
  print("After restore counter:%d" % ret)

Deploy PAI-TensorFlow models to EAS