(pipeline, input_iterator)
return aggregated_outputs
Runs a pipeline saving the output into memory.

  Use this instead of `run_pipeline_serial` to build a dataset on the fly
  without saving it to disk.

  Args:
    pipeline: A Pipeline instance.
    input_iterator: Iterates over the input data. Items returned by it are fed
        directly into the pipeline's `transform` method.

  Returns:
    The aggregated return values of pipeline.transform. Specifically a
    dictionary mapping dataset names to lists of objects. Each name acts
    as a bucket where outputs are aggregated.
Runs a pipeline saving the output into memory.
Runs a pipeline saving the output into memory .
def load_pipeline(pipeline, input_iterator):
  """Runs a pipeline saving the output into memory.

  Use this instead of `run_pipeline_serial` to build a dataset on the fly
  without saving it to disk.

  Args:
    pipeline: A Pipeline instance.
    input_iterator: Iterates over the input data. Items returned by it are fed
        directly into the pipeline's `transform` method.

  Returns:
    The aggregated return values of pipeline.transform. Specifically a
    dictionary mapping dataset names to lists of objects. Each name acts
    as a bucket where outputs are aggregated.
  """
  aggregated_outputs = dict((name, []) for name in pipeline.output_type_as_dict)
  total_inputs = 0
  total_outputs = 0
  stats = []
  for input_object in input_iterator:
    total_inputs += 1
    outputs = _guarantee_dict(pipeline.transform(input_object),
                              list(aggregated_outputs.keys())[0])
    for name, output_list in outputs.items():
      aggregated_outputs[name].extend(output_list)
      total_outputs += len(output_list)
    stats = statistics.merge_statistics(stats + pipeline.get_stats())
    if total_inputs % 500 == 0:
      tf.logging.info('Processed %d inputs so far. Produced %d outputs.',
                      total_inputs, total_outputs)
      statistics.log_statistics_list(stats, tf.logging.info)
  tf.logging.info('\n\nCompleted.\n')
  tf.logging.info('Processed %d inputs total. Produced %d outputs.',
                  total_inputs, total_outputs)
  statistics.log_statistics_list(stats, tf.logging.info)
  return aggregated_outputs
def load_pipeline ( pipeline , input_iterator ) : aggregated_outputs = dict ( ( name , [ ] ) for name in pipeline . output_type_as_dict ) total_inputs = 0 total_outputs = 0 stats = [ ] for input_object in input_iterator : total_inputs += 1 outputs = _guarantee_dict ( pipeline . transform ( input_object ) , list ( aggregated_outputs . keys ( ) ) [ 0 ] ) for name , output_list in outputs . items ( ) : aggregated_outputs [ name ] . extend ( output_list ) total_outputs += len ( output_list ) stats = statistics . merge_statistics ( stats + pipeline . get_stats ( ) ) if total_inputs % 500 == 0 : tf . logging . info ( 'Processed %d inputs so far. Produced %d outputs.' , total_inputs , total_outputs ) statistics . log_statistics_list ( stats , tf . logging . info ) tf . logging . info ( '\n\nCompleted.\n' ) tf . logging . info ( 'Processed %d inputs total. Produced %d outputs.' , total_inputs , total_outputs ) statistics . log_statistics_list ( stats , tf . logging . info ) return aggregated_outputs