TensorFlow Data Validation

TensorFlow Data Validation is a library for exploring and validating machine learning data. tf.DataValidation is designed to be highly scalable and to work well with TensorFlow and TensorFlow Extended (TFX).

TF Data Validation includes:

• Scalable calculation of summary statistics of training and test data.
• Integration with a viewer for data distributions and statistics, as well as faceted comparison of pairs of features (Facets)
• Automated data-schema generation to describe expectations about data like required values, ranges, and vocabularies
• A schema viewer to help you inspect the schema.
• Anomaly detection to identify anomalies, such as missing features, out-of-range values, or wrong feature types, to name a few.
• An anomalies viewer so that you can see what features have anomalies and learn more in order to correct them.

For instructions on using TF Data Validation, see the get started guide.

Caution: tf.DataValidation may be backwards incompatible before version 1.0.

Installing from PyPI

The recommended way to install TensorFlow Data Validation is using the PyPI package:

pip install tensorflow-data-validation

Installing from source

1. Prerequisites

To compile and use TensorFlow Data Validation, you need to set up some prerequisites.

Install NumPy

If NumPy is not installed on your system, install it now by following these directions.

Install Bazel

If bazel is not installed on your system, install it now by following these directions.

2. Clone the TensorFlow Data Validation repository

git clone https://github.com/tensorflow/data-validation
cd data-validation

Note that these instructions will install the latest master branch of TensorFlow Data Validation. If you want to install a specific branch (such as a release branch), pass -b <branchname> to the git clone command.

3. Build and install pip package

TensorFlow Data Validation uses Bazel to build and install the pip package from source:

bazel run -c opt tensorflow_data_validation:pip_installer

Note that the previous command performs a pip install in the current python environment. You can find the installed .whl file in the dist subdirectory. It is also possible to pass options to the executed pip install through the environment variable TFDV_PIP_INSTALL_OPTIONS.

Dependencies

tf.DataValidation requires TensorFlow but does not depend on the tensorflow PyPI package. See theTensorFlow install guides for instructions on how to get started with TensorFlow.

Apache Beam is required; it's the way that efficient distributed computation is supported. By default, Apache Beam runs in local mode but can also run in distributed mode using Google Cloud Dataflow. tf.DataValidation is designed to be extensible for other Apache Beam runners.

Compatible versions

The following table shows the tf.DataValidation package versions that are compatible with each other. This is determined by our testing framework, but other untested combinations may also work.

tensorflow-data-validation	tensorflow	apache-beam[gcp]
GitHub master	nightly (1.x)	2.6.0
0.9.0	1.9	2.6.0

Questions

Please direct any questions about working with TF Data Validation to Stack Overflow using the tensorflow-data-validation tag.