tensorflow-data-validation 0.12.0

A library for exploring and validating machine learning data.

TensorFlow Data Validation

TensorFlow Data Validation (TFDV) is a library for exploring and validating machine learning data. It 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 TFDV, see the get started guide and try out the example notebook.

Caution: TFDV may be backwards incompatible before version 1.0.

Installing from PyPI

The recommended way to install TFDV is using the PyPI package:

pip install tensorflow-data-validation

Installing from source

1. Prerequisites

To compile and use TFDV, 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 TFDV 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 the pip package

TFDV uses Bazel to build the pip package from source:

bazel run -c opt tensorflow_data_validation:build_pip_package

You can find the generated .whl file in the dist subdirectory.

4. Install the pip package

pip install dist/*.whl

Supported platforms

Note: TFDV currently requires Python 2.7. Support for Python 3 is coming very soon (tracked here).

TFDV is built and tested on the following 64-bit operating systems:

• macOS 10.12.6 (Sierra) or later.
• Ubuntu 14.04 or later.

Dependencies

TFDV requires TensorFlow but does not depend on the tensorflow PyPI package. See the TensorFlow 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. TFDV is designed to be extensible for other Apache Beam runners.

Compatible versions

The following table shows the 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)	2.10.0
0.12.0	1.12	2.10.0
0.11.0	1.11	2.8.0
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.