Package: tfx-bsl
TFX Basic Shared LibrariesTFX Basic Shared Libraries (tfx_bsl) contains libraries shared by manyTensorFlow eXtended (TFX)components.Only symbols exported by sub-modules undertfx_bsl/publicare 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 oncetfx_bslgoes beyond1.0.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 ontfx_bsl, will depend on a specific minor version of it (e.g.tensorflow_data_validation0.14.* will depend on, and only work with,tfx_bsl0.14.*)Installing from PyPItfx_bslis available as aPyPI package.pipinstalltfx-bslNightly PackagesTFX-BSL also hosts nightly packages athttps://pypi-nightly.tensorflow.orgon
Google Cloud. To install the latest nightly package, please use the following
command:pipinstall--extra-index-urlhttps://pypi-nightly.tensorflow.org/simpletfx-bslThis 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 DockerIf you want to build a TFX component from the master branch, past the latest
release, you may also have to build the latesttfx_bsl, as that TFX component
might have depended on new features introduced past the latesttfx_bslrelease.Building from Docker is the recommended way to buildtfx_bslunder Linux,
and is continuously tested at Google.1. Install DockerPlease first installdockeranddocker-composeby following the
directions.2. Clone thetfx_bslrepositorygitclonehttps://github.com/tensorflow/tfx-bslcdtfx-bslNote that these instructions will install the latest master branch oftfx-bsl.
If you want to install a specific branch (such as a release branch), pass-b <branchname>to thegit clonecommand.3. Build the pip packageThen, run the following at the project root:sudodocker-composebuildmanylinux2010
sudodocker-composerun-ePYTHON_VERSION=${PYTHON_VERSION}manylinux2010wherePYTHON_VERSIONis one of{38, 39}.A wheel will be produced underdist/.4. Install the pip packagepipinstalldist/*.whlBuild from source1. PrerequisitesInstall NumPyIf NumPy is not installed on your system, install it now by followingthese
directions.Install BazelIf Bazel is not installed on your system, install it now by followingthese
directions.2. Clone thetfx_bslrepositorygitclonehttps://github.com/tensorflow/tfx-bslcdtfx-bslNote that these instructions will install the latest master branch oftfx_bslIf you want to install a specific branch (such as a release branch),
pass-b <branchname>to thegit clonecommand.3. Build the pip packagetfx_bslwheel is Python version dependent -- to build the pip package that
works for a specific Python version, use that Python binary to run:pythonsetup.pybdist_wheelYou can find the generated.whlfile in thedistsubdirectory.4. Install the pip packagepipinstalldist/*.whlSupported platformstfx_bslis tested on the following 64-bit operating systems:macOS 10.12.6 (Sierra) or later.Ubuntu 20.04 or later.Windows 7 or later.Compatible versionsThe following table is thetfx_bslpackage versions that are compatible with
each other. This is determined by our testing framework, but otheruntestedcombinations may also work.tfx-bslapache-beam[gcp]pyarrowtensorflowtensorflow-metadatatensorflow-serving-apiGitHub master2.47.010.0.0nightly (2.x)1.14.02.13.01.14.02.47.010.0.02.131.14.02.13.01.13.02.40.06.0.02.121.13.12.9.01.12.02.40.06.0.02.111.12.02.9.01.11.02.40.06.0.01.15 / 2.101.11.02.9.01.10.02.40.06.0.01.15 / 2.91.10.02.9.01.9.02.38.05.0.01.15 / 2.91.9.02.9.01.8.02.38.05.0.01.15 / 2.81.8.02.8.01.7.02.36.05.0.01.15 / 2.81.7.02.8.01.6.02.35.05.0.01.15 / 2.71.6.02.7.01.5.02.34.05.0.01.15 / 2.71.5.02.7.01.4.02.31.05.0.01.15 / 2.61.4.02.6.01.3.02.31.02.0.01.15 / 2.61.2.02.6.01.2.02.31.02.0.01.15 / 2.51.2.02.5.11.1.02.29.02.0.01.15 / 2.51.1.02.5.11.0.02.29.02.0.01.15 / 2.51.0.02.5.10.30.02.28.02.0.01.15 / 2.40.30.02.4.00.29.02.28.02.0.01.15 / 2.40.29.02.4.00.28.02.28.02.0.01.15 / 2.40.28.02.4.00.27.12.27.02.0.01.15 / 2.40.27.02.4.00.27.02.27.02.0.01.15 / 2.40.27.02.4.00.26.12.25.00.17.01.15 / 2.30.27.02.3.00.26.02.25.00.17.01.15 / 2.30.27.02.3.0