easy_rec/python/utils/export_big_model.py [66:100]:
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  if not checkpoint_path:
    checkpoint_path = estimator_utils.latest_checkpoint(
        pipeline_config.model_dir)
  logging.info('checkpoint_path = %s' % checkpoint_path)

  server = None
  cluster = None
  if 'TF_CONFIG' in os.environ:
    # change chief to master
    tf_config = estimator_utils.chief_to_master()
    if tf_config['task']['type'] == 'ps':
      cluster = tf.train.ClusterSpec(tf_config['cluster'])
      server = tf.train.Server(
          cluster, job_name='ps', task_index=tf_config['task']['index'])
      server.join()
    elif tf_config['task']['type'] == 'master':
      if 'ps' in tf_config['cluster']:
        cluster = tf.train.ClusterSpec(tf_config['cluster'])
        server = tf.train.Server(cluster, job_name='master', task_index=0)
        server_target = server.target
        logging.info('server_target = %s' % server_target)

  serving_input = serving_input_fn()
  features = serving_input.features
  inputs = serving_input.receiver_tensors

  if cluster:
    logging.info('cluster = ' + str(cluster))
  with tf.device(
      replica_device_setter(
          worker_device='/job:master/task:0', cluster=cluster)):
    outputs = estimator._export_model_fn(features, None, None,
                                         estimator.params).predictions

  meta_graph_def = export_meta_graph()
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easy_rec/python/utils/export_big_model.py [320:354]:
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  if not checkpoint_path:
    checkpoint_path = estimator_utils.latest_checkpoint(
        pipeline_config.model_dir)
  logging.info('checkpoint_path = %s' % checkpoint_path)

  server = None
  cluster = None
  if 'TF_CONFIG' in os.environ:
    # change chief to master
    tf_config = estimator_utils.chief_to_master()
    if tf_config['task']['type'] == 'ps':
      cluster = tf.train.ClusterSpec(tf_config['cluster'])
      server = tf.train.Server(
          cluster, job_name='ps', task_index=tf_config['task']['index'])
      server.join()
    elif tf_config['task']['type'] == 'master':
      if 'ps' in tf_config['cluster']:
        cluster = tf.train.ClusterSpec(tf_config['cluster'])
        server = tf.train.Server(cluster, job_name='master', task_index=0)
        server_target = server.target
        logging.info('server_target = %s' % server_target)

  serving_input = serving_input_fn()
  features = serving_input.features
  inputs = serving_input.receiver_tensors

  if cluster:
    logging.info('cluster = ' + str(cluster))
  with tf.device(
      replica_device_setter(
          worker_device='/job:master/task:0', cluster=cluster)):
    outputs = estimator._export_model_fn(features, None, None,
                                         estimator.params).predictions

  meta_graph_def = export_meta_graph()
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