The Estimators API in tf.contrib.learn (See tutorial here) is a very convenient way to get started using TensorFlow. The really cool thing from my perspective about the Estimators API is that using it is a very easy way to create distributed TensorFlow models. Many of the TensorFlow samples that you see floating around on the internets are not distributed — they assume that you will be running the code on a single machine. People start with such code and then are immeasurably saddened to learn that the low-level TensorFlow code doesn’t actually work on their complete dataset. They then have to do lots of work to add distributed training code around the original sample, and who wants to edit somebody else’s code?
So, please, please, please, if you see a TensorFlow sample that doesn’t use the Estimators API, ignore it. It will be a lot of work to make it work on your production (read: large) datasets — there will be monitors, coordinators, parameter servers, and all kinds of systems programming craziness that you don’t want to have to dive into. Start with the Estimator API and use the Experiment class.
Read more at Dataconomy
