A High Level Glance¶
DataFlow is a set of extensible tools to help you define your input data with ease and speed.
It provides a uniform interface so that data processing modules can be chained together. It allows you to load and process your data in pure Python and accelerate it by prefetching. See also Input Pipeline and Efficient DataFlow for more details about the efficiency of data processing.
You can use any TF-based symbolic function library to define a model in tensorpack. And
ModelDescis an interface to connect symbolic graph to tensorpack trainers. Model introduces where and how you define the graph for tensorpack trainers to use, and how you can benefit from the small symbolic function library in tensorpack.
Both DataFlow and models can be used outside tensorpack, as just a data processing library and a symbolic function library. Tensopack trainers integrate these two components and add more convenient features.
tensorpack Trainer manages the training loops for you, so you will not have to worry about details such as multi-GPU training. At the same time, it keeps the power of customization through callbacks.
Callbacks are like
tf.train.SessionRunHook, or plugins, or extensions. During training, everything you want to do other than the main iterations can be defined through callbacks. See Callbacks for some examples what you can do.