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
0.23.1 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.dev20220121212647-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 25669ebae5e7fedfcae864061b618f132b841e1dfd064da42ce886026022fc46
MD5 ea81cb11ddeaba988b1dfbf38f027a5b
BLAKE2b-256 4e832b75b6d0d34da26d260720f505be32ce8fa1a71c8f73037069d2a1cf31d2

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-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.dev20220121212647-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 2271639be92141820c8ca05a771bf56e520a84f669b0ad1cfadfe245d06e019c
MD5 2338dd66f26ccbd13d9bd0515f197c2f
BLAKE2b-256 dd7a6c386d70874721613848c4f064efdf4ee327c9979298ebbeb70b644c4717

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp310-cp310-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp310-cp310-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 9463e1503c972f6174fba97d481cc9df940dd7c6acf3e3c61cdcdebda8825301
MD5 0619e5d7c0b9aa272b1acb91a2b12e7f
BLAKE2b-256 57641b4549295620debf9057db7befcf93c1b0b02581f499354407a5c89af775

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 5aed16ef61e90e6829d88a0b84b3ca9e7bd159bcc2b0e1fb8749d48a8b0d1c4e
MD5 72667c04f8f2b76c4d87c05fe9d21a24
BLAKE2b-256 7cb351e38e0205f4557ea86897c21e15fca766ef5ab4531fe26795760d81aab3

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-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.dev20220121212647-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 9b6f20678fc6609440ad0cf5a24ba1b868dafe0f855af358f38b25fbe5522c1b
MD5 6d5de38c6df67875d3d2fc719838b21f
BLAKE2b-256 623f9769975fc3826f4fce7d96f9ed6a365275a92a515c1ed0205b891dd1ba67

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 f42e12e7b6b02fc79891e8e72af9cb130fd8818eeca825b08183b90e91955505
MD5 6e8b71cdc3dac1b30148a392f9cbf650
BLAKE2b-256 302d9e18bf79503914b0cd697e9dcf8dfe1bd21c34fbfd0de94cd07640de3e9f

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 4c7779bd438d15b574fbf0b1871d36dbd1f384ae44e2d7d3d3fb7427860febc5
MD5 849f362a0e87fbd7f5fd4ed29a1aa55b
BLAKE2b-256 01fc24a9ca05d21baa0e6fe69260cb2c8661145d8f4de393ebd403ffe03fc986

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-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.dev20220121212647-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 a904de1a4ef9ad5b3e3c56f2289211844b6f42f810518d05c129eede15b4306d
MD5 34fd5b983caeda5febade3905f5909ab
BLAKE2b-256 e22a7ba1fb6350f2a27ca245247760e0518b4a52d26edc5a99e9617a1d19d553

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 6878869984bca5d8e9eff1849d3e1a5cf4ad0aa29f7e2dde7edc4a0deb58598d
MD5 15aa9abdd7541a11f99dec661b0e2ac9
BLAKE2b-256 8073992cd3964dd874de23ed33b54feb61283a538268c7a99b879feb54e98a9f

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 cba23169b69966b75e9ff4e9a4a7f3e5fd774950ea3f3fc78f9a01cb4111e77a
MD5 050cff31084bfbe0977db27e655be019
BLAKE2b-256 b6653b49a9a337c7e4d2f73b4eba1ac38bdb87cfdf759bcf517106007187a3ea

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-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.dev20220121212647-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 ecdbb351709bfcf75f7acbf6a2ccddd1e567c3adb86d2a75f634f950f8de71bc
MD5 7a764f683b9e6d2ce1c3fb171da959a9
BLAKE2b-256 f7548918e2ad571b47ee47cc86d6cde993023826bdc0413e4ad9829c55b25c7f

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220121212647-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 cefde89faeb4a2b292bdb8def1973aa2dfb056185a6c5242ccf76e568c9f9fd3
MD5 ad6a5771ae515727832e734b127bcac0
BLAKE2b-256 767496a80dd859ffb56e3b8a9402ce769193d3bf969d7a9a61b5aad4db514a4e

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