Estimators provide the following benefits :
You can run Estimator-based models on a local host or on a distributed multi-server environment without changing your model. Furthermore, you can run Estimator-based models on CPUs, GPUs, or TPUs without recoding your model.
Estimators provide a safe distributed training loop that controls how and when to:
* Load data
* Handle exceptions
* Create checkpoint files and recover from failures
* Save summaries for TensorBoard
When writing an application with Estimators, you must separate the data input pipeline from the model. This separation simplifies experiments with different datasets.
