Skip to main content

TensorFlow IO

Project description




TensorFlow I/O

GitHub CI PyPI License Documentation

TensorFlow I/O is a collection of file systems and file formats that are not available in TensorFlow's built-in support. A full list of supported file systems and file formats by TensorFlow I/O can be found here.

The use of tensorflow-io is straightforward with keras. Below is an example to Get Started with TensorFlow with the data processing aspect replaced by tensorflow-io:

import tensorflow as tf
import tensorflow_io as tfio

# Read the MNIST data into the IODataset.
dataset_url = "https://storage.googleapis.com/cvdf-datasets/mnist/"
d_train = tfio.IODataset.from_mnist(
    dataset_url + "train-images-idx3-ubyte.gz",
    dataset_url + "train-labels-idx1-ubyte.gz",
)

# Shuffle the elements of the dataset.
d_train = d_train.shuffle(buffer_size=1024)

# By default image data is uint8, so convert to float32 using map().
d_train = d_train.map(lambda x, y: (tf.image.convert_image_dtype(x, tf.float32), y))

# prepare batches the data just like any other tf.data.Dataset
d_train = d_train.batch(32)

# Build the model.
model = tf.keras.models.Sequential(
    [
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(512, activation=tf.nn.relu),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(10, activation=tf.nn.softmax),
    ]
)

# Compile the model.
model.compile(
    optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
)

# Fit the model.
model.fit(d_train, epochs=5, steps_per_epoch=200)

In the above MNIST example, the URL's to access the dataset files are passed directly to the tfio.IODataset.from_mnist API call. This is due to the inherent support that tensorflow-io provides for HTTP/HTTPS file system, thus eliminating the need for downloading and saving datasets on a local directory.

NOTE: Since tensorflow-io is able to detect and uncompress the MNIST dataset automatically if needed, we can pass the URL's for the compressed files (gzip) to the API call as is.

Please check the official documentation for more detailed and interesting usages of the package.

Installation

Python Package

The tensorflow-io Python package can be installed with pip directly using:

$ pip install tensorflow-io

People who are a little more adventurous can also try our nightly binaries:

$ pip install tensorflow-io-nightly

Docker Images

In addition to the pip packages, the docker images can be used to quickly get started.

For stable builds:

$ docker pull tfsigio/tfio:latest
$ docker run -it --rm --name tfio-latest tfsigio/tfio:latest

For nightly builds:

$ docker pull tfsigio/tfio:nightly
$ docker run -it --rm --name tfio-nightly tfsigio/tfio:nightly

R Package

Once the tensorflow-io Python package has been successfully installed, you can install the development version of the R package from GitHub via the following:

if (!require("remotes")) install.packages("remotes")
remotes::install_github("tensorflow/io", subdir = "R-package")

TensorFlow Version Compatibility

To ensure compatibility with TensorFlow, it is recommended to install a matching version of TensorFlow I/O according to the table below. You can find the list of releases here.

TensorFlow I/O Version TensorFlow Compatibility Release Date
0.21.0 2.6.x Sep 12, 2021
0.20.0 2.6.x Aug 11, 2021
0.19.1 2.5.x Jul 25, 2021
0.19.0 2.5.x Jun 25, 2021
0.18.0 2.5.x May 13, 2021
0.17.1 2.4.x Apr 16, 2021
0.17.0 2.4.x Dec 14, 2020
0.16.0 2.3.x Oct 23, 2020
0.15.0 2.3.x Aug 03, 2020
0.14.0 2.2.x Jul 08, 2020
0.13.0 2.2.x May 10, 2020
0.12.0 2.1.x Feb 28, 2020
0.11.0 2.1.x Jan 10, 2020
0.10.0 2.0.x Dec 05, 2019
0.9.1 2.0.x Nov 15, 2019
0.9.0 2.0.x Oct 18, 2019
0.8.1 1.15.x Nov 15, 2019
0.8.0 1.15.x Oct 17, 2019
0.7.2 1.14.x Nov 15, 2019
0.7.1 1.14.x Oct 18, 2019
0.7.0 1.14.x Jul 14, 2019
0.6.0 1.13.x May 29, 2019
0.5.0 1.13.x Apr 12, 2019
0.4.0 1.13.x Mar 01, 2019
0.3.0 1.12.0 Feb 15, 2019
0.2.0 1.12.0 Jan 29, 2019
0.1.0 1.12.0 Dec 16, 2018

Performance Benchmarking

We use github-pages to document the results of API performance benchmarks. The benchmark job is triggered on every commit to master branch and facilitates tracking performance w.r.t commits.

Contributing

Tensorflow I/O is a community led open source project. As such, the project depends on public contributions, bug-fixes, and documentation. Please see:

Build Status and CI

Build Status
Linux CPU Python 2 Status
Linux CPU Python 3 Status
Linux GPU Python 2 Status
Linux GPU Python 3 Status

Because of manylinux2010 requirement, TensorFlow I/O is built with Ubuntu:16.04 + Developer Toolset 7 (GCC 7.3) on Linux. Configuration with Ubuntu 16.04 with Developer Toolset 7 is not exactly straightforward. If the system have docker installed, then the following command will automatically build manylinux2010 compatible whl package:

#!/usr/bin/env bash

ls dist/*
for f in dist/*.whl; do
  docker run -i --rm -v $PWD:/v -w /v --net=host quay.io/pypa/manylinux2010_x86_64 bash -x -e /v/tools/build/auditwheel repair --plat manylinux2010_x86_64 $f
done
sudo chown -R $(id -nu):$(id -ng) .
ls wheelhouse/*

It takes some time to build, but once complete, there will be python 3.5, 3.6, 3.7 compatible whl packages available in wheelhouse directory.

On macOS, the same command could be used. However, the script expects python in shell and will only generate a whl package that matches the version of python in shell. If you want to build a whl package for a specific python then you have to alias this version of python to python in shell. See .github/workflows/build.yml Auditwheel step for instructions how to do that.

Note the above command is also the command we use when releasing packages for Linux and macOS.

TensorFlow I/O uses both GitHub Workflows and Google CI (Kokoro) for continuous integration. GitHub Workflows is used for macOS build and test. Kokoro is used for Linux build and test. Again, because of the manylinux2010 requirement, on Linux whl packages are always built with Ubuntu 16.04 + Developer Toolset 7. Tests are done on a variatiy of systems with different python3 versions to ensure a good coverage:

Python Ubuntu 18.04 Ubuntu 20.04 macOS + osx9 Windows-2019
2.7 :heavy_check_mark: :heavy_check_mark: :heavy_check_mark: N/A
3.7 :heavy_check_mark: :heavy_check_mark: :heavy_check_mark: :heavy_check_mark:
3.8 :heavy_check_mark: :heavy_check_mark: :heavy_check_mark: :heavy_check_mark:

TensorFlow I/O has integrations with many systems and cloud vendors such as Prometheus, Apache Kafka, Apache Ignite, Google Cloud PubSub, AWS Kinesis, Microsoft Azure Storage, Alibaba Cloud OSS etc.

We tried our best to test against those systems in our continuous integration whenever possible. Some tests such as Prometheus, Kafka, and Ignite are done with live systems, meaning we install Prometheus/Kafka/Ignite on CI machine before the test is run. Some tests such as Kinesis, PubSub, and Azure Storage are done through official or non-official emulators. Offline tests are also performed whenever possible, though systems covered through offine tests may not have the same level of coverage as live systems or emulators.

Live System Emulator CI Integration Offline
Apache Kafka :heavy_check_mark: :heavy_check_mark:
Apache Ignite :heavy_check_mark: :heavy_check_mark:
Prometheus :heavy_check_mark: :heavy_check_mark:
Google PubSub :heavy_check_mark: :heavy_check_mark:
Azure Storage :heavy_check_mark: :heavy_check_mark:
AWS Kinesis :heavy_check_mark: :heavy_check_mark:
Alibaba Cloud OSS :heavy_check_mark:
Google BigTable/BigQuery to be added
Elasticsearch (experimental) :heavy_check_mark: :heavy_check_mark:
MongoDB (experimental) :heavy_check_mark: :heavy_check_mark:

References for emulators:

Community

Additional Information

License

Apache License 2.0

Release history Release notifications | RSS feed

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

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 770ce4fa1e6ddcb6dc543ca550d6ef450141b7bc27c46d552bff253ce7385ae1
MD5 4b8d4c3dbaba58cb95cf7fbd2db38b88
BLAKE2b-256 5c2d42c4374ffe058b351a7cbe36cb4b154d64c857e34ea2b772caffc50c7b6e

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 f228f7da7d520efff366ae69d83b52651670e7d54205e5babc09d7083cb9149c
MD5 6c40aa15869d0a111ced8063945bd930
BLAKE2b-256 d903b6707f3ea9a330b0b581794986fd2e7d07fc26e3cfbfa0fb88972e0f5e08

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 f4999d3b2abc82d8ac15bf752de6890fda5e8fe8fa462b517575176226456b3b
MD5 0c37d86d8a4a1b04458a9d2b72773753
BLAKE2b-256 e33d267579d34fad14ae6902d148c06944fbeaebcacf0f26f4198557cf0a6dd9

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 e92e8982a2cdb8a856a526554f5648b94cc1877f0b61697c9d1828fe1bd2fddd
MD5 5a126d974ed2fe4dec55a60cb0434b88
BLAKE2b-256 ce66f97c0653bd1f099ee12c2761a6c398365bec8293ce17d7de8aa0e729cee0

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 df4d641a38d74c63305ca2ecbf463516b25a54f5ecafa2fc9139a1023546856d
MD5 195d3fb0ae2f04917eb77797c187a2dd
BLAKE2b-256 3ed0eff57b2a80eb76454039894bb0613f2ff3cadbd58bfa618ac2f3c5282db0

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 d186ec9fbd97833d131ad6ee8a7ddc980e517cabd4f6c811b9c04eb8e00a1a71
MD5 14825ddd3e2423e3e7b5f679480eb344
BLAKE2b-256 a904e0f541e0cbc58ba3fcb00eb00461e947eb406ab16ff74d80653fdc87e63c

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 f1873ef72ba52fde7d7ab1ee64343ab98d5734eba41259e090606ee55ed1dc2a
MD5 1bb35141875ef435237491b69feb00c0
BLAKE2b-256 f826f11b9cacccfc725324e3d094961dbd7e36db82031594457779f69e91f252

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 8725223bb3f977fc5dad491a8c03323bba77c2c60c19595ff5cc94685bafeef6
MD5 dc90089953d920a60d4b60364160d129
BLAKE2b-256 48cfe347bbada0ef3d629c9f41c31061da36ef8bebbe5087d25205d4ae98619e

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 efe30245399a2adef217a984d79e67ebbe6bfb345d791cba11b0ebc5b33b38a9
MD5 8f0c886a64d3893f3a9b95354541be57
BLAKE2b-256 d35588aa5a819df8e4cd41ad434b1337131d5654e61e9a5edf61ac1f8507d0d9

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 e025c6f65c205647788c99e497d7ab3c1e7c0de0717774143d523d755b5fd603
MD5 94c116fe098b9665b60cfefdb617b766
BLAKE2b-256 696176c473e7bf40a396deb8af9865e418829b80b856a719f9623475637fa8f5

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 4a87b61cf5a296d1a8a125ed16e1b40d42c3475ba56d7c472ac650974964533f
MD5 c979c9e3ad815630734c0072d0e42bc0
BLAKE2b-256 d6c191f0622a05a6977585b65fc5e0605fe261b6f88f38cce8ba8bffc7438601

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp36-cp36m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20210913003830-cp36-cp36m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 0c7f00e9391fc91858337b5f829b66c2d991aafeea526099ef2fe16ed85dcf26
MD5 1f05ead615fffa0c4a371ced67be3c46
BLAKE2b-256 3f8f09016594fe3db77418c56f247cd0bfbd9302c0b736e2bfc4184e0e8e96cc

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