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 {35, 36, 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 2.29.0 2.0.0 nightly (1.x/2.x) 1.1.0 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-1.1.0-cp38-cp38-win_amd64.whl (1.8 MB view details)

Uploaded CPython 3.8 Windows x86-64

tfx_bsl-1.1.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (19.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

tfx_bsl-1.1.0-cp38-cp38-macosx_10_9_x86_64.whl (20.3 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

tfx_bsl-1.1.0-cp37-cp37m-win_amd64.whl (1.8 MB view details)

Uploaded CPython 3.7m Windows x86-64

tfx_bsl-1.1.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (19.0 MB view details)

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

tfx_bsl-1.1.0-cp37-cp37m-macosx_10_9_x86_64.whl (20.3 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

tfx_bsl-1.1.0-cp36-cp36m-win_amd64.whl (1.8 MB view details)

Uploaded CPython 3.6m Windows x86-64

tfx_bsl-1.1.0-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (19.0 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.12+ x86-64

tfx_bsl-1.1.0-cp36-cp36m-macosx_10_9_x86_64.whl (20.3 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

File details

Details for the file tfx_bsl-1.1.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: tfx_bsl-1.1.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.8 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.8.5

File hashes

Hashes for tfx_bsl-1.1.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 76444c35712ebed60f5920dc2f52e86878782b2ca483e0fb0c05819e07b16f6d
MD5 ee331ca3dfedf2c190adaec61207969f
BLAKE2b-256 b7fe9c8631718ae86ab4d0dd816f51da3273ab1a2d4297b193708b110a200278

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.1.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfx_bsl-1.1.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 22844b5641e8f6b06513f28f9a1d61401692aca35e40718ae83558e976261da1
MD5 c6d7517edda3a0a950a307d11ed66999
BLAKE2b-256 b41a68e019225773997ffa9744f82ec3505460020003be05b13312f71fafdc29

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.1.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: tfx_bsl-1.1.0-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 20.3 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.8.2

File hashes

Hashes for tfx_bsl-1.1.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 8f77a3edd383ad0121537662fb84e60644faa14ef727e3dbd955a7d6cf76c83b
MD5 185b46b032a8ed43021a52ea6356d8c5
BLAKE2b-256 97e2bd030fd8f27084a0489614bd6c3129be04bccfd5e5cf74ada181cb77ba35

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.1.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: tfx_bsl-1.1.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 1.8 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.7.0

File hashes

Hashes for tfx_bsl-1.1.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 37cd0f68b67c133ff8000e6a0331cf907c91cd6d3133363a7a485400f421ae13
MD5 be7d37555c312361ac1b71d7233d73f1
BLAKE2b-256 52e0215dcf9faa74a2306acb2b32d0d378c948f32c568fa00d6046a97edb65ac

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.1.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfx_bsl-1.1.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 29b99386476d0f17a152ef5b3b38bc9e62fc89a4789aaa76fb110919acfb1516
MD5 6e1dc133d0a99567e1c15707d4def35d
BLAKE2b-256 65bbc2164101365997e8a7ea25275ab0ae12def11ef91e5127a0ed36a6939c51

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.1.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: tfx_bsl-1.1.0-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 20.3 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.7.3

File hashes

Hashes for tfx_bsl-1.1.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 f98ef4b3a426d83a39bbbe637b5ef2ee5042de3067b7f83f089b12a2cf17b161
MD5 09414c7531fa5dbb40fd66836febdcdb
BLAKE2b-256 20f2a487dabc9ab942d650863cc6359d2fa5576233699a2c2a5a7f6e7eae5f34

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.1.0-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: tfx_bsl-1.1.0-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 1.8 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.6.5

File hashes

Hashes for tfx_bsl-1.1.0-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 dd4fea3bbbcc78de574e8d217433b86b0baaf0ccbf4de8e7258611c20c4f4358
MD5 b04cd3150b1f6f269a0afedf45e21bf3
BLAKE2b-256 c7b702fd37f1f17de9c795865e93a8e2a9bf1b94e86e614590c2d7cc852c4426

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.1.0-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfx_bsl-1.1.0-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 c351bbce4afee427e4a330bae60704ef3967229b2bca016c142948aa49117f2f
MD5 25469c363b435ab2979e505da1f162da
BLAKE2b-256 b64dc9169016869df039d8c482cf703e542a2b76db2c5b5b383a052a8185b25d

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.1.0-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: tfx_bsl-1.1.0-cp36-cp36m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 20.3 MB
  • Tags: CPython 3.6m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.6.8

File hashes

Hashes for tfx_bsl-1.1.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 5b5aa832f65e4cd65803680ab9565a6b9486c1790cc6f432794facb0b97459af
MD5 b9b399d07a0e20a77da3d6f7b4ba2729
BLAKE2b-256 2f49a8a74aafe5671cb4b0652ce4d76a6d84a4f18c651d2bb4f15f0d6cef1efe

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