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.dev20211110002806-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 d2ca0cafb54789a04c6ee070bc610f164a9eb1549c9946b99932df6e74f7ebd1
MD5 b74a24d9228f5aa282df2ce6ad2eb9d8
BLAKE2b-256 3e5d7037ecf2a730302b3d4f72cac817e2ab52ac76cd27a55194c19836f95513

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-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.dev20211110002806-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 fd21f7371a2020285268d3025269212f0138f53db4fc28fe87495f14857de23b
MD5 2de2167e25827c198b9c949c16b77cb9
BLAKE2b-256 8f5215647773a50043e9f0d53cbdd3ac21328abf1efe1944373bb210a41b7be0

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 a31416b6b431e4077680a0574a61e7fd5bb8ee4f35088b44a3be0646394a3b6c
MD5 9b09cde4baa3c86e80f396b1a3896753
BLAKE2b-256 2b54b32b2190c75a6cd66e566048a4b1276dfac643f5749fe23317a63a8dc9e0

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 2195846eb74a73cb2fc2b43aeb2ac5e1fdafda1980c84419fc989ad1cdaf0690
MD5 fe1d7f1567e77db9b53a853524e915ab
BLAKE2b-256 23a18334b48e33546b769ff3dd15ed57db5b2e845b92eb0025fe790fa8c9fa3a

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-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.dev20211110002806-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 e06b9e0bcfccad69a1edb291d45cfbd0f567eb1f7539bdbe797b37f78b2a3e30
MD5 3471b29872ed87ddedb404a2bf348e69
BLAKE2b-256 bb79db5dec7a570bed42ff66f0da93872771fb8752e4902318623f9f885c27b3

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 653a181e31d279935eda6c554f3d47d5802971bcfdafaf35a0f363be58ab71ea
MD5 452306295a2f8039343df5cd47ebdfda
BLAKE2b-256 5ec9eab3d5c705cc8a18edf27a9399449b469ecb53a086a7929b9a1d2ef63eb2

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 b0679b78e08b7b78395d66227a74c095893713532468c8adcc94be6bb86df019
MD5 e0113d7cc55275020b9dde583c963ed9
BLAKE2b-256 6b4bc27065e909e36727e9a63047698a911faad794fdb00375a122d400c93c53

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-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.dev20211110002806-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 1e4c9dbf0d3a01a019d2a7d8dccb3fe05a14a038ca6cfca9dd887904056d4c14
MD5 fa7c782c434c61768e0c03e1dddd76ed
BLAKE2b-256 82e3b482d1e1abf28969661bc1232ac2376201c44afad96e05616d8c60624a4e

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.21.0.dev20211110002806-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 83fedd89cf23efee52e493b452ed2a6b0765c7c015da6a4b4661123c3f96d311
MD5 9d49c6605ed32bda8afd8feed507832c
BLAKE2b-256 aea0190de9fdc74aa43859732ac557f6f4050cb22d03a05b38a8f644fd7c75e2

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