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 -i 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 with Docker

If you want to build a TFX component from the master branch, past the latest
release, you may also have to build the latest tfx_bsl, as that TFX component
might have depended on new features introduced past the latest tfx_bsl
release.

Building from Docker is the recommended way to build tfx_bsl under Linux,
and is continuously tested at Google.

1. Install Docker

Please first install docker and
docker-compose by following the
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

Then, run the following at the project root:

sudo docker-compose build manylinux2010
sudo docker-compose run -e PYTHON_VERSION=${PYTHON_VERSION} manylinux2010

where PYTHON_VERSION is one of {37, 38}.

A wheel will be produced under dist/.

4. Install the pip package

pip install dist/*.whl

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.

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:

python setup.py bdist_wheel

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 16.04 or later.
• Windows 7 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.