Skip to main content

Struct2Tensor is a package for parsing and manipulating structured data for TensorFlow

Project description

Struct2Tensor

Python PyPI

Introduction

struct2tensor is a library for parsing structured data inside of tensorflow. In particular, it makes it easy to manipulate structured data, e.g., slicing, flattening, copying substructures, and so on, as part of a TensorFlow model graph. The notebook in 'examples/prensor_playground.ipynb' provides a few examples of struct2tensor in action and an introduction to the main concepts. You can run the notebook in your browser through Google's colab environment, or download the file to run it in your own Jupyter environment.

There are two main use cases of this repo:

  1. To create a PIP package. The PIP package contains plug-ins (OpKernels) to an existing tensorflow installation.
  2. To staticlly link with tensorflow-serving.

As these processes are independent, one can follow either set of directions below.

Use a pre-built Linux PIP package.

From a virtual environment, run:

pip install struct2tensor

Nightly Packages

Struct2Tensor 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 struct2tensor

This will install the nightly packages for the major dependencies of Fairness Indicators such as TensorFlow Metadata (TFMD).

Creating a PIP package.

The struct2tensor PIP package is useful for creating models. It works with tensorflow 2.x.

In order to unify the process, we recommend compiling struct2tensor inside a docker container.

Downloading the Code

Go to your home directory.

Download the source code.

git clone https://github.com/google/struct2tensor.git
cd ~/struct2tensor

Use docker-compose

Install docker-compose.

Use it to build a pip wheel for Python 3.6 with tensorflow version 2:

docker-compose build manylinux2010
docker-compose run -e PYTHON_VERSION=36 -e TF_VERSION=RELEASED_TF_2 manylinux2010

Or build a pip wheel for Python 3.7 with tensorflow version 2 (note that if you run one of these docker-compose commands after the other, the second will erase the result from the first):

docker-compose build manylinux2010
docker-compose run -e PYTHON_VERSION=37 -e TF_VERSION=RELEASED_TF_2 manylinux2010

This will create a manylinux package in the ~/struct2tensor/dist directory.

Creating a static library

In order to construct a static library for tensorflow-serving, we run:

bazel build -c opt struct2tensor:prensor_kernels_and_ops

This can also be linked into another library.

TensorFlow Serving docker image

struct2tensor needs a couple of custom TensorFlow ops to function. If you train a model with struct2tensor and wants to serve it with TensorFlow Serving, the TensorFlow Serving binary needs to link with those custom ops. We have a pre-built docker image that contains such a binary. The Dockerfile is available at tools/tf_serving_docker/Dockerfile. The image is available at gcr.io/tfx-oss-public/s2t_tf_serving.

Please see the Dockerfile for details. But in brief, the image exposes port 8500 as the gRPC endpoint and port 8501 as the REST endpoint. You can set two environment variables MODEL_BASE_PATH and MODEL_NAME to point it to your model (either mount it to the container, or put your model on GCS). It will look for a saved model at ${MODEL_BASE_PATH}/${MODEL_NAME}/${VERSION_NUMBER}, where VERSION_NUMBER is an integer.

Compatibility

struct2tensor tensorflow
0.37.0 2.7.0
0.36.0 2.7.0
0.35.0 2.6.0
0.34.0 2.6.0
0.33.0 2.5.0
0.32.0 2.5.0
0.31.0 2.5.0
0.30.0 2.4.0
0.29.0 2.4.0
0.28.0 2.4.0
0.27.0 2.4.0
0.26.0 2.3.0
0.25.0 2.3.0
0.24.0 2.3.0
0.23.0 2.3.0
0.22.0 2.2.0
0.21.1 2.1.0
0.21.0 2.1.0
0.0.1.dev* 1.15

Project details


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

struct2tensor-0.37.0-cp38-cp38-manylinux2010_x86_64.manylinux_2_12_x86_64.whl (2.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

struct2tensor-0.37.0-cp38-cp38-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

struct2tensor-0.37.0-cp37-cp37m-manylinux2010_x86_64.manylinux_2_12_x86_64.whl (2.2 MB view details)

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

struct2tensor-0.37.0-cp37-cp37m-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file struct2tensor-0.37.0-cp38-cp38-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.

File metadata

File hashes

Hashes for struct2tensor-0.37.0-cp38-cp38-manylinux2010_x86_64.manylinux_2_12_x86_64.whl
Algorithm Hash digest
SHA256 4ad449d546567f2696f5085fc66be356cb053c2d989ac58b700a3a4c5ed2fa53
MD5 8654589cd5ed9e1c8065d2aae4a48b32
BLAKE2b-256 85d0812ac47dfc080c61ab53484f22a8e067013903d655151c8255aab469df17

See more details on using hashes here.

File details

Details for the file struct2tensor-0.37.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: struct2tensor-0.37.0-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.8 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.2

File hashes

Hashes for struct2tensor-0.37.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 6677dd7086815416acc9375b770ee01a3cd5f1326453f99a1e234901704925b9
MD5 48963e0d593f0c372748c6fc4553b4f3
BLAKE2b-256 fd958a43c7751f1a94629ca077749045d0e5d2c26de34bce2b03a33f04af3db3

See more details on using hashes here.

File details

Details for the file struct2tensor-0.37.0-cp37-cp37m-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.

File metadata

File hashes

Hashes for struct2tensor-0.37.0-cp37-cp37m-manylinux2010_x86_64.manylinux_2_12_x86_64.whl
Algorithm Hash digest
SHA256 4b4ee7cea4d82d5548c191c0d4c597166696f581a0b1e9248876c2916785dd2c
MD5 01c17972b4222db31023acf49ee46e5e
BLAKE2b-256 cbc3afee802d32fed59f23005102ac555774bba7282732bf7514ba326fa58dc4

See more details on using hashes here.

File details

Details for the file struct2tensor-0.37.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: struct2tensor-0.37.0-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.8 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.3

File hashes

Hashes for struct2tensor-0.37.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 8cbe92c16b504069ca77800c1ed83a04c3ab86140c0059a6d72ba872fee97f68
MD5 f39de6f4aa86a59780522e01d47a8743
BLAKE2b-256 41c5f306825e02bfb592a86886ac15d6b050afba92cfcfa7c274dfbed24b151f

See more details on using hashes here.

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