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


When you use tf.app.run(), you can transfer the variable very conveniently between threads using tf.app.flags. See this for further usage of tf.app.flags.


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

flags = tf.app.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?