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

To ensure you have a version of TensorFlow that is compatible with TensorFlow-IO, you can specify the tensorflow extra requirement during install:

pip install tensorflow-io[tensorflow]

Similar extras exist for the tensorflow-gpu, tensorflow-cpu and tensorflow-rocm packages.

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
0.24.0 2.8.x Feb 04, 2022
0.23.1 2.7.x Dec 15, 2021
0.23.0 2.7.x Dec 14, 2021
0.22.0 2.7.x Nov 10, 2021
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-0.24.0.dev20220209230704-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 b56f497965cdf591dfac26fd65e4f07bfda584b0bd9a22c1021f83c2a4d71435
MD5 962b89410751c66b7b7832b1b4016446
BLAKE2b-256 8c3ea3ddb2c7f6836ec4e94e4ee950bdd8d5407aa220a4f3cb1d8f0165614362

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 c8116f4b4ad38e79d73357bc5ed0c6a61cae16110c642da5c77165b5a1bd163a
MD5 1a9a54e041a5de620a92d72230330b99
BLAKE2b-256 8ac9e0182f643bff337e59a42d4242b346b2c1cd6570551050f68f8943130e60

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp310-cp310-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp310-cp310-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 adb84d6364a6a48e5ac3d99c01fe725437c8698f7fd36b53804e765f86327ff2
MD5 fdfd656a43ae64b80215129332725059
BLAKE2b-256 cb184ba64204db8c651b9e12c48b2ed5099a57dce91e5a4160a382e85acbd8db

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 fc3412d4f2e807bdd412de2e330eb632a41a4fefa7ad9401996efc7c1331f647
MD5 127c31d70188e6dae4cdba7c221d4223
BLAKE2b-256 deeb7d37f7760fcf5ad28102abb510b42776905c2ef582f0f0a4dd2e8b1e845d

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 958b0e842103ad6b63ab1789e46b79f716736a9eb673fab8589941be46334575
MD5 180d35622ecd7f2181c3f9529b20c794
BLAKE2b-256 f5465366add63f3a2db50bed444330ba897ae014715420e4b36064966b9b1e81

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 e34630c1f8dd958e82877bc408ee8ae830b18fc8a83cbe657a0939ec6dfe358a
MD5 5bc5f4b9a1f4dad2c4677ab881c811d8
BLAKE2b-256 c7b07b81f1a1376c31101fe1a914b1916558f2637fe9503b972b091c6678486b

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 c11947e76ec782fd100da92be98e8f487764856708cc8ee3710cf1e58766863f
MD5 96b2e7b73fcb1959289aa314d1954581
BLAKE2b-256 3a0eb0b21c8297044241c9ce5e34639279439ebc98f33d3532049cfc298cbc46

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 12efcc8a2b1671b25d03fad258e8a3ade8256c85e85c164bb373aff9fb0a659f
MD5 37ab3be27dc7a5e6315024d1dcb5254c
BLAKE2b-256 2f3a05ba44c554cfa785996ec1a006680a8213ae214d55f379e9ae3693ca0751

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 5be120131a891aaca2c11e2c660e83544fa55aebd2d06ef8ed70f6083412d7cd
MD5 54f60b827a80cb1ef497bcbdffb16bfb
BLAKE2b-256 4f2012bec33c53eea69218b5b7b89dd57f30381d8e446b44bfc741eca24f1cb3

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 45fc36cb5d9fa1a6543f1c0cc4441e81d8ef28d5ae03927e1af86b22703913c2
MD5 c31adefff2e8ae828bd11f795f2961cc
BLAKE2b-256 576106a1ae29570b0f783f8e81cba78691f007078c851576e3a2faa99852de9a

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 ac9d4ddba07624eacbe12ff9550f6670a3963e1e944e464ad3f513e0e5eab53e
MD5 e39456bb45fb89858cd9d1d0f6265dae
BLAKE2b-256 d9f8545860dd6e12d1d3e67ff79536725f86e160183ab9937c9231b5b472065a

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220209230704-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 5390351a2d5dbfeae6df10cdbf54e481ea7b0e5028781bb900e0f1dee3ce82b3
MD5 61edfc66469c54ee701bbcde03b237b1
BLAKE2b-256 9dc1e048d625b87bae6a5865668e653ade58a64c522529bae15a0e34ce144722

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