[usmannk]

> That may be different now with the various flavors of TF (like TF Eager)

Unfortunately, if anything I think it's the opposite. The constant creation and deprecation of TF flavors (tf-eager, tf-slim, tf-learn, keras, tf-estimator, tf.contrib [RIP]) has made reading tensorflow code online somewhat disastrous. Everybody, including the TF team, is using a different API and it's difficult to keep all of them straight. It seems that you're doomed to end up using some combination of many of the above in a way that makes sense to you and your team, adding another confusing model to the pile.

[halflings]

Agree overall, but tf.eager doesn't have much to do with the rest of the list.

tf.contrib is just a module where user-contributed code was stored, which included both low-level constructs and higher level APIs. tf.estimator is an abstraction that is mostly used for productionizing models. tf.slim/tf.learn were indeed redundant with keras (a library developped externally), but were necessary steps before keras became part of tensorflow.