TensorFlow Transform (tf.Transform) is a library for preprocessing data with TensorFlow. tf.Transform is useful for preprocessing that requires a full pass the data, such as:

• normalizing an input value by mean and stdev
• integerizing a vocabulary by looking at all input examples for values
• bucketizing inputs based on the observed data distribution

TensorFlow already supports arbitrary manipulations on a single example or batch of examples. tf.Transform extends the capabilities to support full passes over the example data.

The output of tf.Transform is exported as a TensorFlow graph for incorporation into training and serving. Using the same graph for both training and serving can prevent training/serving skew, because the same transforms are performed in both scenarios.

tf.Transform may introduce backwards incompatible changes before version 1.0.

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 requires Apache Beam to run distributed analysis. Apache Beam runs in local mode by default, and can also run in distributed mode using Google Cloud Dataflow. tf.Transform is designed to be extensible to other Apache Beam runners.

For instructions on using tf.Transform see the getting started guide.

This is a table of versions known to be compatible with each other, based on our testing framework. Other combinations may also work, but are untested.