tf.Transform is a library for doing data preprocessing with TensorFlow. It allows users to combine various data processing frameworks (currently Apache Beam is supported but tf.Transform can be extended to support other frameworks), with TensorFlow, to transform data. Because tf.Transform is built on TensorFlow, it allows users to export a graph which re-creates the transformations they did to their data as a TensorFlow graph. This is important as the user can then incorporate the exported TensorFlow graph into their serving model, thus avoiding skew between the served model and the training data.
tf.Transform may introduce backwards incompatible changes before version 1.0.
While TensorFlow allows users to do arbitrary manipulations on a single instance or batch of instances, some kinds of preprocessing require a full pass over the dataset. For example, normalizing an input value, computing a vocabulary for a string input (and then mapping the string to an int with this vocabulary), or bucketizing an input. While some of these operations can be done with TensorFlow in a streaming manner (e.g. calculating a running mean for normalization), in general it may be preferable or necessary to calculate these with a full pass over the data.
The easiest and recommended way to install tf.Transform is with the PyPI package.
pip install tensorflow-transform
Currently tf.Transform requires that TensorFlow be installed but does not have an explicit dependency on TensorFlow as a package. See TensorFlow documentation for more information on installing TensorFlow.
tf.Transform does though have a dependency on the Google Cloud Dataflow distribution of Apache Beam. Apache Beam is the package used to run distributed pipelines. Apache Beam is able to run pipelines in multiple ways, depending on the "runner" used. While Apache Beam is an open source package, currently the only distribution on PyPI is the Cloud Dataflow distribution. This package can run beam pipelines locally, or on Google Cloud Dataflow.
When a base package for Apache Beam (containing no runners) is available, the tf.Transform package will depend only on this base package, and users will be able to install their own runners. tf.Transform will attempt to be as independent from the specific runner as possible.
Note: If you clonse tf.Transform's implementation and samples from GitHub's
master branch (as opposed to using the released implementation and samples
from PyPI) they will likely only work with TensorFlow's nightly
build.
For instructions on using tf.Transform see the getting started guide.