Skip to main content

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

Project description

TFX Basic Shared Libraries

Python PyPI

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.

tfx-bsl apache-beam[gcp] pyarrow tensorflow tensorflow-metadata tensorflow-serving-api
GitHub master 2.34.0 2.0.0 nightly (1.x/2.x) 1.5.0 2.7.0
1.5.0 2.34.0 2.0.0 1.15 / 2.7 1.5.0 2.7.0
1.4.0 2.31.0 2.0.0 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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

tfx_bsl-1.5.0-cp38-cp38-win_amd64.whl (1.9 MB view details)

Uploaded CPython 3.8 Windows x86-64

tfx_bsl-1.5.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (19.1 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

tfx_bsl-1.5.0-cp38-cp38-macosx_10_9_x86_64.whl (20.4 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

tfx_bsl-1.5.0-cp37-cp37m-win_amd64.whl (1.9 MB view details)

Uploaded CPython 3.7m Windows x86-64

tfx_bsl-1.5.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (19.1 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64

tfx_bsl-1.5.0-cp37-cp37m-macosx_10_9_x86_64.whl (20.4 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file tfx_bsl-1.5.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: tfx_bsl-1.5.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.5

File hashes

Hashes for tfx_bsl-1.5.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 581e48d90ed6499ac335ef238ec4cf408e15deb02ca80cca3185abd1db761795
MD5 23be1fbccebe1b18d91be6e2d7ff72e7
BLAKE2b-256 653a9af1481b519fd0ab30a7f4c507be78e5b34b7c1cf798b2fed36b87580dee

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.5.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfx_bsl-1.5.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 b80f8f17d00e4e7974e8d629e60cba1f001fab898a39d51cd36f3789c9a6c312
MD5 e3bf6c904f017b61658ad8c8431074c1
BLAKE2b-256 acaa785ba1c2f95ed60169fd34eba0a1e3504eebe072d7019bb82276a6d16d63

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.5.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: tfx_bsl-1.5.0-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 20.4 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.2

File hashes

Hashes for tfx_bsl-1.5.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 38180ea85a2f799eea8e53997afccb47b8143729ef3ad3a15d334b2dad1a6265
MD5 4b11c94d3eb16b0b977e5b376f28e382
BLAKE2b-256 195c8f753b1e05e4ff1f6c7f8cd1534511e3ec65ac2c50c4bca8ececdeb6651a

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.5.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: tfx_bsl-1.5.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.0

File hashes

Hashes for tfx_bsl-1.5.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 04d9734abd72f8576cd14bfca4e9cb58a387af83e4527437526e0062beeb6c0b
MD5 75769d8708641ebaa1e3f3141848f5e3
BLAKE2b-256 67b8ed941546af760ffbd0d306e62316709ce654bc567ecc13a03f1238eec041

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.5.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfx_bsl-1.5.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 eac4b7f5683996b6558984690ffa9aa8ee14a6dda127437554602f808d6f8315
MD5 c9c9a3ac88978370f958bc56215dda0a
BLAKE2b-256 d9e3a2361ee9a7ce949aa84dc8227bb0e4e6066d378ce5006c17b70c4c956cca

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.5.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: tfx_bsl-1.5.0-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 20.4 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.3

File hashes

Hashes for tfx_bsl-1.5.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e45182b42a814bc4824c8014f402febf0b460ec6667ea40973506d817a7a22dc
MD5 91867d44c35923f3ab9de73a542059e3
BLAKE2b-256 b488205c8996871f9317e7aacae777e126ee496a4da749d207c2a86def3b68e1

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page