tfx-bsl 1.21.0

tfx_bsl (TFX Basic Shared Libraries) contains libraries shared by many TFX (TensorFlow eXtended) libraries and components.

TFX Basic Shared Libraries

TFX Basic Shared Libraries (tfx_bsl) contains libraries shared by many TensorFlow eXtended (TFX) components.

Only symbols exported by sub-modules under tfx_bsl/public are intended for direct use by TFX users, including by standalone TFX library (e.g. TFDV, TFMA, TFT) users, TFX pipeline authors and TFX component authors. Those APIs will become stable and follow semantic versioning once tfx_bsl goes beyond 1.0.

APIs under other directories should be considered internal to TFX (and therefore there is no backward or forward compatibility guarantee for them).

Each minor version of a TFX library or TFX itself, if it needs to depend on tfx_bsl, will depend on a specific minor version of it (e.g. tensorflow_data_validation 0.14.* will depend on, and only work with, tfx_bsl 0.14.*)

Installing from PyPI

tfx_bsl is available as a PyPI package.

pip install tfx-bsl

Nightly Packages

TFX-BSL also hosts nightly packages at https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest nightly package, please use the following command:

pip install --extra-index-url https://pypi-nightly.tensorflow.org/simple tfx-bsl

This will install the nightly packages for the major dependencies of TFX-BSL such as TensorFlow Metadata (TFMD).

However it is a dependency of many TFX components and usually as a user you don't need to install it directly.

Build from source

1. 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.

Install cibuildwheel

If you do not already have cibuildwheel installed on your system, you an install it using these directions.

2. Clone the tfx_bsl repository

git clone https://github.com/tensorflow/tfx-bsl
cd tfx-bsl

Note that these instructions will install the latest master branch of tfx_bsl 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

tfx_bsl wheel is Python version dependent -- to build the pip package that works for a specific Python version, use that Python binary to run:

cibuildwheel

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

4. Install the pip package

pip install dist/*.whl

Supported platforms

tfx_bsl is tested on the following 64-bit operating systems:

• macOS 10.12.6 (Sierra) or later.
• Ubuntu 20.04 or later.

Compatible versions

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

tfx-bsl	apache-beam[gcp]	pyarrow	tensorflow	tensorflow-metadata	tensorflow-serving-api
GitHub master	2.64.0	14.0.1	nightly (2.x)	1.21.0	2.19.0
1.21.0	2.64.0	14.0.1	2.21	1.21.0	2.19.0
1.17.1	2.64.0	10.0.1	2.17	1.17.1	2.17.1
1.17.0	2.64.0	10.0.1	2.17	1.17.1	2.17.1
1.16.1	2.59.0	10.0.1	2.16	1.16.1	2.16.1
1.16.0	2.59.0	10.0.1	2.16	1.16.0	2.16.1
1.15.1	2.47.0	10.0.0	2.15	1.15.0	2.15.1
1.15.0	2.47.0	10.0.0	2.15	1.15.0	2.15.1
1.14.0	2.47.0	10.0.0	2.13	1.14.0	2.13.0
1.13.0	2.40.0	6.0.0	2.12	1.13.1	2.9.0
1.12.0	2.40.0	6.0.0	2.11	1.12.0	2.9.0
1.11.0	2.40.0	6.0.0	1.15 / 2.10	1.11.0	2.9.0
1.10.0	2.40.0	6.0.0	1.15 / 2.9	1.10.0	2.9.0
1.9.0	2.38.0	5.0.0	1.15 / 2.9	1.9.0	2.9.0
1.8.0	2.38.0	5.0.0	1.15 / 2.8	1.8.0	2.8.0
1.7.0	2.36.0	5.0.0	1.15 / 2.8	1.7.0	2.8.0
1.6.0	2.35.0	5.0.0	1.15 / 2.7	1.6.0	2.7.0
1.5.0	2.34.0	5.0.0	1.15 / 2.7	1.5.0	2.7.0
1.4.0	2.31.0	5.0.0	1.15 / 2.6	1.4.0	2.6.0
1.3.0	2.31.0	2.0.0	1.15 / 2.6	1.2.0	2.6.0
1.2.0	2.31.0	2.0.0	1.15 / 2.5	1.2.0	2.5.1
1.1.0	2.29.0	2.0.0	1.15 / 2.5	1.1.0	2.5.1
1.0.0	2.29.0	2.0.0	1.15 / 2.5	1.0.0	2.5.1
0.30.0	2.28.0	2.0.0	1.15 / 2.4	0.30.0	2.4.0
0.29.0	2.28.0	2.0.0	1.15 / 2.4	0.29.0	2.4.0
0.28.0	2.28.0	2.0.0	1.15 / 2.4	0.28.0	2.4.0
0.27.1	2.27.0	2.0.0	1.15 / 2.4	0.27.0	2.4.0
0.27.0	2.27.0	2.0.0	1.15 / 2.4	0.27.0	2.4.0
0.26.1	2.25.0	0.17.0	1.15 / 2.3	0.27.0	2.3.0
0.26.0	2.25.0	0.17.0	1.15 / 2.3	0.27.0	2.3.0