What's the purpose of tf.app.flags in TensorFlow?

Question

I am reading some example codes in Tensorflow, I found following code

flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
flags.DEFINE_integer('max_steps', 2000, 'Number of steps to run trainer.')
flags.DEFINE_integer('hidden1', 128, 'Number of units in hidden layer 1.')
flags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.')
flags.DEFINE_integer('batch_size', 100, 'Batch size.  '
                 'Must divide evenly into the dataset sizes.')
flags.DEFINE_string('train_dir', 'data', 'Directory to put the training data.')
flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '
                 'for unit testing.')

in tensorflow/tensorflow/g3doc/tutorials/mnist/fully_connected_feed.py

But I can't find any docs about this usage of tf.app.flags.

And I found the implementation of this flags is in the tensorflow/tensorflow/python/platform/default/_flags.py

Obviously, this tf.app.flags is somehow used to configure a network, so why is it not in the API docs? Can anyone explain what is going on here?

Answer 1

The tf.app.flags module is presently a thin wrapper around python-gflags, so the documentation for that project is the best resource for how to use it argparse, which implements a subset of the functionality in python-gflags.

Note that this module is currently packaged as a convenience for writing demo apps, and is not technically part of the public API, so it may change in future.

We recommend that you implement your own flag parsing using argparse or whatever library you prefer.

EDIT: The tf.app.flags module is not in fact implemented using python-gflags, but it uses a similar API.