*TensorFlow Transform* is a library for preprocessing data with TensorFlow.
`tf.Transform`

is useful for data that requires a full-pass, such as:

- Normalize an input value by mean and standard deviation.
- Convert strings to integers by generating a vocabulary over all input values.
- Convert floats to integers by assigning them to buckets based on the observed data distribution.

TensorFlow has built-in support for manipulations on a single example or a batch
of examples. `tf.Transform`

extends these capabilities to support full-passes
over the example data.

The output of `tf.Transform`

is exported as a
TensorFlow graph to use for training and serving.
Using the same graph for both training and serving can prevent skew since the
same transformations are applied in both stages.

For an introduction to `tf.Transform`

, see the `tf.Transform`

section of the
TFX Dev Summit talk on TFX
(link).

## Installation

The `tensorflow-transform`

PyPI package is the
recommended way to install `tf.Transform`

:

```
pip install tensorflow-transform
```

### Dependencies

`tf.Transform`

requires TensorFlow but does not depend on the `tensorflow`

PyPI package. See the
TensorFlow install guides for
instructions.

Apache Beam is required to run distributed analysis.
By default, Apache Beam runs in local mode but can also run in distributed mode
using Google Cloud Dataflow.
`tf.Transform`

is designed to be extensible for other Apache Beam runners.

## Compatible versions

The following table is the `tf.Transform`

package versions that are
compatible with each other. This is determined by our testing framework, but
other *untested* combinations may also work.

tensorflow-transform | tensorflow | apache-beam[gcp] |
---|---|---|

GitHub master | nightly (1.x) | 2.6.0 |

0.9.0 | 1.9 | 2.6.0 |

0.8.0 | 1.8 | 2.5.0 |

0.6.0 | 1.6 | 2.4.0 |

0.5.0 | 1.5 | 2.3.0 |

0.4.0 | 1.4 | 2.2.0 |

0.3.1 | 1.3 | 2.1.1 |

0.3.0 | 1.3 | 2.1.1 |

0.1.10 | 1.0 | 2.0.0 |

## Questions

Please direct any questions about working with `tf.Transform`

to
Stack Overflow using the
tensorflow-transform
tag.