tfx_bsl (TFX Basic Shared Libraries) contains libraries shared by many TFX (TensorFlow eXtended) libraries and components.
Project description
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 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 {39}.
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 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 | 2.47.0 | 10.0.0 | nightly (2.x) | 1.15.0 | 2.15.1 |
| 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 |
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