I'm having a bit of trouble understanding the new tf.contrib.summary API. In the old one, it seemed that all one was supposed to do was to run tf.summary.merge_all() and run that as an op.

But now we have things like tf.contrib.summary.record_summaries_every_n_global_steps, which can be used like this:

import tensorflow.contrib.summary as tfsum

summary_writer = tfsum.create_file_writer(logdir, flush_millis=3000)
summaries = []

# First we create one summary which runs every n global steps
with summary_writer.as_default(), tfsum.record_summaries_every_n_global_steps(30):
    summaries.append(tfsum.scalar("train/loss", loss))

# And then one that runs every single time?
with summary_writer.as_default(), tfsum.always_record_summaries():
    summaries.append(tfsum.scalar("train/accuracy", accuracy))

# Then create an optimizer which uses a global step
step = tf.create_global_step()
train = tf.train.AdamOptimizer().minimize(loss, global_step=step)

And now come a few questions:

1. If we just run session.run(summaries) in a loop, I assume that the accuracy summary would get written every single time, while the loss one wouldn't, because it only gets written if the global step is divisible by 30?
2. Assuming the summaries automatically evaluate their dependencies, I never need to run session.run([accuracy, summaries]) but can just run, session.run(summaries) since they have a dependency in the graph, right?
3. If 2) is true, can't I just add a control dependency to the training step so that the summaries are written on every train run? Or is this a bad practice?
4. Is there any downside to using control dependencies in general for things that are going to be evaluated at the same time anyway?
5. Why does tf.contrib.summary.scalar (and others) take in a step parameter?

By adding a control dependency in 3) I mean doing this:

tf.control_dependencies(summaries):
    train = tf.train.AdamOptimizer().minimize(loss, global_step=step)