eii = ExternalInputIterator(batch_size=16,
                            data_file=processed_data_file,
                            image_dir=images_directory)
iterator = iter(eii)

class ExternalSourcePipeline(Pipeline):
    def __init__(self, data_iterator, batch_size, num_threads, device_id):
        super(ExternalSourcePipeline, self).__init__(batch_size,
                                      num_threads,
                                      device_id,
                                      seed=12)
        self.data_iterator = data_iterator
        self.input = ops.ExternalSource()
        self.input_label = ops.ExternalSource()
        self.decode = ops.nvJPEGDecoder(device = "mixed", output_type = types.RGB)
        # resizing is *must* because loaded images maybe of different sizes
        # and to create GPU tensors we need image arrays to be of same size
        self.res = ops.Resize(device="gpu", resize_x=224, resize_y=224, interp_type=types.INTERP_TRIANGULAR)

    def define_graph(self):
        self.jpegs = self.input()
        self.labels = self.input_label()
        images = self.decode(self.jpegs)
        output = self.res(images)
        return (output, self.labels)

    def iter_setup(self):
        # the external data iterator is consumed here and fed as input to Pipeline
        images, labels = self.data_iterator.next()
        self.feed_input(self.jpegs, images)
        self.feed_input(self.labels, labels)