Q:

Is tf.recorder way of reading data more efficient than feed to placeholder?

I'm dealing with a huge amount of data in Tensorflow. 
One way is to define placeholder and then read my data by my own defined functions outside of the graph, such as a queue and feed a batch every time into the placeholders. 
Another way is to use recorder related built-in classes in Tensorflow to directly read data as tensors.
I searched but failed to find any relavant comparison between the two. Does anyone has idea about their advantages and disadvanteges, especially about the efficiency? Which one do you guys prefer when you use tensorflow?

A:

The different methods of reading data in Tensorflow are compared and discussed here with more comparison here
tfrecord allows to read data in chunks, so you can deal with data that exceed RAM capacity. Also it can be arranged in such way that you read data a separate thread using tf.Coordinator and start_queue_runners. More information can be found here 

