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

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for google_opensource from gravatar.com google_opensource Avatar for tfx_oss from gravatar.com tfx_oss Avatar for zhitaoli.google from gravatar.com zhitaoli.google Avatar for zhuo_at_goog from gravatar.com zhuo_at_goog

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache Software License (Apache 2.0)
  • Author: Google LLC
  • Tags tfx, bsl
  • Requires: Python >=3.6, <4
Classifiers

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 2.31.0 2.0.0 nightly (1.x/2.x) 1.2.0 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

Verified details

These details have been verified by PyPI
Maintainers
Avatar for google_opensource from gravatar.com google_opensource Avatar for tfx_oss from gravatar.com tfx_oss Avatar for zhitaoli.google from gravatar.com zhitaoli.google Avatar for zhuo_at_goog from gravatar.com zhuo_at_goog

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache Software License (Apache 2.0)
  • Author: Google LLC
  • Tags tfx, bsl
  • Requires: Python >=3.6, <4
Classifiers

Release history Release notifications | RSS feed

1.16.1

1.16.0

1.15.1

1.15.0

1.14.0

1.13.0

1.12.0

1.11.0

1.10.1

1.10.0

1.9.0

1.8.0

1.7.0

1.6.0

1.5.0

1.4.0

1.3.1 yanked

Reason this release was yanked:

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

This version

1.3.0

1.2.0

1.1.1

1.1.0

1.0.0

0.30.0

0.29.0

0.28.1

0.28.0

0.27.1

0.27.0

0.26.1

0.26.0

0.25.0

0.24.1

0.24.0

0.23.0

0.22.1

0.22.0

0.21.4

0.21.3

0.21.2

0.21.1

0.21.0

0.15.3

0.15.2

0.15.1

0.15.0

0.15.0.dev0 pre-release

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-1.3.0-cp38-cp38-win_amd64.whl (1.8 MB view details)

Uploaded CPython 3.8 Windows x86-64

tfx_bsl-1.3.0-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-1.3.0-cp38-cp38-macosx_10_9_x86_64.whl (20.3 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

tfx_bsl-1.3.0-cp37-cp37m-win_amd64.whl (1.8 MB view details)

Uploaded CPython 3.7m Windows x86-64

tfx_bsl-1.3.0-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-1.3.0-cp37-cp37m-macosx_10_9_x86_64.whl (20.3 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

tfx_bsl-1.3.0-cp36-cp36m-win_amd64.whl (1.8 MB view details)

Uploaded CPython 3.6m Windows x86-64

tfx_bsl-1.3.0-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-1.3.0-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-1.3.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: tfx_bsl-1.3.0-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/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.8.5

File hashes

Hashes for tfx_bsl-1.3.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 f1da91c9b07b2749887dba6499d8b9b1947111d9c9fd2da577db69b208302b1e
MD5 ae6698a98196d12eb1e5d38a2eb57e2f
BLAKE2b-256 fa63ae2ad622a025dcc1206e7d875d3b96f90a3d7e3d78dc222d4e8d16cbc787

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.3.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfx_bsl-1.3.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 ad43cc4946e5a3ff66af3548aea4af8f4862bc88fab655222c2c2e6c58d00658
MD5 d47eb5d8d893ffeef128cb117c05a1d6
BLAKE2b-256 a135515e2a32f3b2fedd61b85784993980cb3eb38919d2c174633d77ee2d0a9a

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.3.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: tfx_bsl-1.3.0-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/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.8.2

File hashes

Hashes for tfx_bsl-1.3.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 2ed90cad1d59c6a5506d0154bb90bde09501b2628ae07aaa20fa9c5e6320ad62
MD5 b9e8cfc85126c9ac7d859adf444c41e1
BLAKE2b-256 6f14fba1e393bae6762bf4b16d9510bd7ddd43ea95a1e4374520b99b53ed30e5

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.3.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: tfx_bsl-1.3.0-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/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.7.0

File hashes

Hashes for tfx_bsl-1.3.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 e9ebecd1da5e6a42ea0ba8a4a9f25dbc4c892f0109952543e472b54a66a5c941
MD5 5d95adbdf6c7992d200f28472537d560
BLAKE2b-256 1965ad2503d45650c29f4f7dd16aae9a5bafdf32a0b2d505b36abb6774850e23

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.3.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfx_bsl-1.3.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 0e1011945aa3757001d56c22b0fb3adb5582547e9ee5eaf98d7d1f52ab72bf80
MD5 586a8ac90e8a8fba60f718c6af278d21
BLAKE2b-256 b3bd3890857d37ddd65fd689d774bb665a2ae04e2e8d872a33b3754595f62a45

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.3.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: tfx_bsl-1.3.0-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/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.7.3

File hashes

Hashes for tfx_bsl-1.3.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c1176eac98b44253bfb3fe8dd79eb8364e3a85719f2a019c4b56b24fc8703af0
MD5 16dd534b4f60b85349aebee2960df26c
BLAKE2b-256 86162918b03c5ff0fd7878564ca7129f7c377e7cb4acc7568afa6db1fce9a8c8

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.3.0-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: tfx_bsl-1.3.0-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/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.6.5

File hashes

Hashes for tfx_bsl-1.3.0-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 707aa815816b6e34eca999f6a2f446300a7bc99a9c21923badf3b22023a3aa94
MD5 c9eee3690be9def7d031f1b0c771e804
BLAKE2b-256 b6ac3fba8a6278bf965a07136e135cb46969d059b26276e92b8fce4e79596c79

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.3.0-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfx_bsl-1.3.0-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 343816202db4eb4339dfd9a35385344509294b191ef9476dd2984677d517b8ad
MD5 1c3682c7d5be2c1a548c2edf46588669
BLAKE2b-256 8dab7e2a68f8f51ee917b9a5bf4a13f560891298c90b461ec4abbf240b54b3a7

See more details on using hashes here.

Provenance

File details

Details for the file tfx_bsl-1.3.0-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: tfx_bsl-1.3.0-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/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.6.8

File hashes

Hashes for tfx_bsl-1.3.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e8d45030a5dc9aaebcfbc4965ec64cd92f25f54930b340582bbd25bc4129cae3
MD5 b8166d14d86c726c19f74508a07ae226
BLAKE2b-256 04891b83748baac0b1f298e85499f2dfd1d7347b18865fd27482c17ab543bbfd

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