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

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google_opensource tfx_oss zhitaoli.google zhuo_at_goog

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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 {37, 38, 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 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.

tfx-bsl	apache-beam[gcp]	pyarrow	tensorflow	tensorflow-metadata	tensorflow-serving-api
GitHub master	2.38.0	5.0.0	nightly (1.x/2.x)	1.9.0	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

Project details

These details have been verified by PyPI

Maintainers

google_opensource tfx_oss zhitaoli.google zhuo_at_goog

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

1.16.1

Oct 14, 2024

1.16.0

Oct 3, 2024

1.15.1

Apr 24, 2024

1.15.0

Apr 23, 2024

1.14.0

Aug 11, 2023

1.13.0

Apr 13, 2023

1.12.0

Dec 7, 2022

1.11.0

Nov 14, 2022

1.10.1

Aug 26, 2022

1.10.0

Aug 24, 2022

This version

1.9.0

Jun 28, 2022

1.8.0

May 12, 2022

1.7.0

Mar 1, 2022

1.6.0

Jan 20, 2022

1.5.0

Dec 1, 2021

1.4.0

Oct 27, 2021

1.3.1 yanked

Aug 30, 2021

Reason this release was yanked:

google-cloud-bigquery 2.26 release fixed the issue that this patch was trying to solve

1.3.0

Aug 25, 2021

1.2.0

Jul 28, 2021

1.1.1

Jul 16, 2021

1.1.0

Jun 22, 2021

1.0.0

May 24, 2021

0.30.0

Apr 21, 2021

0.29.0

Mar 24, 2021

0.28.1

Feb 23, 2021

0.28.0

Feb 23, 2021

0.27.1

Jan 28, 2021

0.27.0

Jan 27, 2021

0.26.1

Dec 17, 2020

0.26.0

Dec 11, 2020

0.25.0

Nov 3, 2020

0.24.1

Sep 24, 2020

0.24.0

Sep 9, 2020

0.23.0

Aug 12, 2020

0.22.1

Jun 24, 2020

0.22.0

May 12, 2020

0.21.4

Mar 6, 2020

0.21.3

Feb 28, 2020

0.21.2

Feb 21, 2020

0.21.1

Feb 20, 2020

0.21.0

Jan 14, 2020

0.15.3

Nov 6, 2019

0.15.2

Oct 31, 2019

0.15.1

Oct 21, 2019

0.15.0

Oct 8, 2019

0.15.0.dev0 pre-release

Sep 19, 2019

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tfx-bsl 1.9.0

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Project description

TFX Basic Shared Libraries

Installing from PyPI

Nightly Packages

Build with Docker

1. Install Docker

2. Clone the tfx_bsl repository

3. Build the pip package

4. Install the pip package

Build from source

1. Prerequisites

Install NumPy

Install Bazel

2. Clone the tfx_bsl repository

3. Build the pip package

4. Install the pip package

Supported platforms

Compatible versions

Project details

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Unverified details

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2. Clone the `tfx_bsl` repository

2. Clone the `tfx_bsl` repository