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
0.23.0 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-0.23.0.dev20211215004050-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 2c5e96189860919582546c2e4e28f2722aad24da2e991fbcb567beeb2e050250
MD5 78b193ae7907195e188a1047c6f67fc4
BLAKE2b-256 5eb1cd97bca26ee835323701861a1a50c0477a7719dce8f9b2ceac4cf119985a

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 a4d2c4ab1c0385c483038705a08876f5c82ccb8ac261b35d8281dfef5633bd35
MD5 1a995cf285d4191c2dd9453fea0b063f
BLAKE2b-256 9eba4cf60dbf0eb63383ee0fb2243688da1b03d3ef94336a346a1faaf979d6ec

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp310-cp310-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp310-cp310-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 c8deea388da826faa8e0dda9fd23323b7076780a4866ac7d6a0348f96bcf9f0f
MD5 5062d510a354d727b76dc32daba59a61
BLAKE2b-256 c8862795dd38dcc0901388b0f33aad5a8246e958df48d50e4e8c1e5cca096464

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 c2400d6401dce5250ffaf9529873c44e4c118b2eb905311d036e6129a97ff5fa
MD5 79174e81cdaf9464613647c1ae73727c
BLAKE2b-256 5054d04ecf279bc93689682863a9e2b1577c4b3dcd6b9b76cdf47516632f8cf2

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 2cb6c89e7c564119b6d7a51cc7d434325220f975ae632b6cddc589986e235d50
MD5 c6fe0cec63ec5dd27c176fa520797a27
BLAKE2b-256 b040febbcae45914e06978c24d49982235d7784d58e32ecfc98ad819ccf96e59

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 a322c5a53d9b2cf5efbdd91c1ae4daac226a6f7008fd11907c303db34156f03b
MD5 82ee848c5f7741085314f7bb4df1d9d0
BLAKE2b-256 a02e25cb86da1093ddda4695a66839fe3e1111a8f50658d404cd6eac7a3db2db

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 2ba085381a06533acf3568209ad61e747d37d75b2c9a02cc5dcbbccd8bc33a63
MD5 ce5b91f956dfed844272920d546ddf3b
BLAKE2b-256 63864beb0fad42eb5f42a736357114f1e1310d91a1ffc9819ef51b33b8a2d452

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 0a0772c12ae0f485afae71c003df68fb9abd41e0bbdf445900b8824d4a26102d
MD5 03a0bb774d6db271cf4eeb7a5838ba07
BLAKE2b-256 36b92b825380955fbd49853d85b510417999e5286487c76a24c7c4090cbee511

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 aac0767e5081b4f8404c23957101b7d2d75059d3d28bad310b6e075f19cb1af7
MD5 d55e27af9532b28b00c86eea1e93300c
BLAKE2b-256 719698e760acf56e96a246ec1fe92f233043cce2220b45a50c1919eb2877b94e

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 beb9a76b3c6af83c0847c9df6e65a6a5f79ef216c2e0f6b10fc2e44ff0197e9c
MD5 8d2a8e2d8ee12c8a7a03af7b5570a8b4
BLAKE2b-256 0c6005c4b15f15ddd9e2efa36156ce44436731cc020637cd071c56548092d83b

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 56b39ba204005f65f1fd01656213327a83ea37c59de8bceef779a9ef3d309404
MD5 514b12685b7c1a366a8282eab881bdd9
BLAKE2b-256 d7053eda133f9876d507c8955de38a121f818ad031757057e7d18ff37196fd68

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.23.0.dev20211215004050-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 6ab84de16f59e883fcd51a63e099d0dadf055ee3c61fca7a0d6d43f3be223864
MD5 34963f498477d40c88eb136ff974cb1a
BLAKE2b-256 f8a71e98746f13ff367d08dc6600914cc7510e9ee14551c402b3a75038ec904a

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