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
0.20.0 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-0.20.0.dev20210826163931-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 a0b89b4e4f7e70bdf7476f038d96cb45bf8b13004f4a97f2c6adc2cc5641973e
MD5 d3c06bfb41b20f198e164d5780bd8b28
BLAKE2b-256 9ec3cf649d9d31e21003b59b7107919863bdcdfa641d5d226617fbe8840959b6

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 15812fa62e7fdbfade9c3c036fae46f95eea200b6b2502ff02fc45b0f6f6ffa9
MD5 8a519f2a92f1473bb83e0f313e735085
BLAKE2b-256 d93d4df8e3b3a1cf74a3f1c2fecfd48c0243ddff9869e9baca0347616f103703

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 0cc87dac0635c0aeba95d4e796cebcf27c9c4588bc5e091bd37c2bf18e15e988
MD5 3b6ed5082d14ddc31f98bf42ebdf61b9
BLAKE2b-256 5a0f8867122b5db34b47dcc4c989f556f10bc7fb3177fb7131c60acab9aabbcb

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 fa9582f86b96cc11b140624f75a9b06c3e4e9af03492f4b6e5ce579365946bc2
MD5 9e3ab932f93aab587bfb480974e43b7c
BLAKE2b-256 c10ffe55b11d097d5056ab2113cdbf66dceab078cc40e5697876476825ac872c

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 df80f663ba33ab352cc140553eed4df2c895f0e10888979492482dca72d972fa
MD5 9a4a67528ae3f0bcbfa8df71664ec65b
BLAKE2b-256 0588827f1f44727794d51e2d5cd3f3e024c6b106edaa6829fefd376dbab324f8

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 2b5a5f72251c2ee91e70fe472ec799ac5a66eb21d8866deaf18ace096a08c139
MD5 f2844d1eb93e1e3bb19b7a900723cb54
BLAKE2b-256 d9030198e236420ead032917d4cb23ea7a7dfb121f5a902fa0b9b763138b0bc8

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 e68280e441a6b1da61ea3cd554d34642f7849c733eea302fc9d665ccf6680d7f
MD5 0a5713d3fca8877a745a6a17e222e629
BLAKE2b-256 0102614b7bc9d4040e080d2fde6505c047bf9748cb0f424c7b0e01fb8cfe2315

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 ff6eb07ba5116960b7ab112b30b97d2bee3a83196ec0ac87a1adb40fb3935f03
MD5 1588e3de23d878b0d3239a9e7f99cfe0
BLAKE2b-256 ab763976839939a8b747e2549e43a7a581b6aefb19ac822c955e8ae3637c98b6

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 9aafd2538ffa57960564c21776e0ea5a09f4d8c9cb8ad6788a4004c7f2784439
MD5 195689c67d27ee86db789480c19a6026
BLAKE2b-256 81379153e2f02b44abc846abc1fc98072f2e4aa5cae210b7e572405f148d2765

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 2eda4dfd1e8d63b0aa35ceb4d7725dd58209c500e3477b12bb169932cf232602
MD5 5fb2779fa762456870b68720d2355079
BLAKE2b-256 b8c04643bce3900926c054ecd30e181784010e6633daaa2cc6e348457239fa9f

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 ecad3d85b2d2276ecc7f1f4c0f4cc2fa734300a89fdd5437605a94f9f7f4c3ed
MD5 e3d57aa7919b203df88d70dc12280282
BLAKE2b-256 95c94dce714c6b43c20c062e451183c05978aa826f6d6fe49c69859342703fc3

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp36-cp36m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.20.0.dev20210826163931-cp36-cp36m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 a829b79b64a8fe6344eb419298a8db03e4aa9f60c865dcebacad41b8de8941ab
MD5 68e42a6b93cf7054bc56e1dc8118d7f1
BLAKE2b-256 54ba9fdce86c00cee5984a88024850454da164f83a5a2f84d80ea280bade7451

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