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
0.18.0 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-0.18.0.dev20210519162459-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 0ccb2ed73c27d6ce0676ed7ca4cd043532d4944a4fc55776bb54395f76564a84
MD5 31fa0182bfc118fd0853c47f5cbe8ec1
BLAKE2b-256 22c31cbcb8ce1543568ac0ffffb5eae81b6eeac22efd9a6926a30d04e45ee790

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 30fd49b7d3b64aa1330a87438390876a76e30c4a64728b8563a6efaf9fb7ac28
MD5 9ceff3adc311d3eb68073097dc861748
BLAKE2b-256 47f0c11b8cfd0903b1da46123a926dae2ddee33bc24dd4d9391ef0020125a222

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 f503b2afa0b38e180c315c95bcc4c3496a3a6b2a935b1c5d8ccda53999d6f83b
MD5 e7f2ab3bfe2959748315799303c21125
BLAKE2b-256 fee5867a83724de4730c6825a669ca24eb695c634bb376c990682043b1eb0fb7

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 cd783e97481a01a6872d5cf3768a107b516b996b37fd3eb0eaa134d3c384cef2
MD5 8d8891a1bcd529fc08bab09496288235
BLAKE2b-256 cc735ec556aaf3e66bae319179f8081042e43570560e867fcd6255b53c3eda6e

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 cf716fd8bb999ff2743b3a644dd223c12732214254eb81c250bf04014bf5f2cf
MD5 6f50f97a30f8cdcfb68e5745c90475e1
BLAKE2b-256 fe28efb75b52b08121d6f2556f4771256262459cc21ae2c1e4c116f7713c2468

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 6c5e56943f95565903e7c5d4b560536920587fe4156ba4e5b75b1f8293d5809f
MD5 590a4f46ea3d39c3b8bd90bd09c0f732
BLAKE2b-256 33088667087073d07fc28f6e859b2c848c1e9ecd5848870497e6f89899e7ddaf

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 87df247c29de191cdcf5b5ebab4816a3f53f78e67fca0f0d34507d504dc109a5
MD5 e02c5158b4d82f39c75de1a7fcadd2d0
BLAKE2b-256 4ebddfd4b336c0f862254b4bc63299b485a9d06c1bd5c1ff2db323e20f59dd82

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 7ac6f08a8fa1a822282b6b0c765a3214cf785612e44524416abe4836861135a8
MD5 8344d1509ae8c434903d879fff0a0781
BLAKE2b-256 6a0e92f0ec7d45301d5b1743ba4461464f1cc55c666fe5e41c99bd2f1d65d0d4

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 8286048d57e758153159f0615f5e152b867bbacf0ad0e19c9f9797d79bc37a9b
MD5 1a911a618b29d873ed8ad1849bb1f83c
BLAKE2b-256 e7e4701e7d7894018b6de2f5b309e5449f866161cd48c36e1e06d169b5653ce2

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 45a2e3b28baea9ec7ced24e72614f333beccd61dac64c7124356ae178c5fa9de
MD5 30b6db09076dccd018b4d39ff513b7a8
BLAKE2b-256 d1dcac235b76ab90448177590e6aaa9dc7134370416908bff7890194528fd499

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 c6111f700aaca551e86dcf11c3524ccd78bc88e74d80d54f38a51b8aba63bbba
MD5 5af103a9dcb5190f9471959fd1a88802
BLAKE2b-256 4db62777475e0a4658bcfffb3aa6595aaa40e42be127b1693d60e39a55b5383a

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp36-cp36m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.18.0.dev20210519162459-cp36-cp36m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 1f7259d8acd452ccd5c499158b97fc097d037ee91b6cf694059522a50f2daef7
MD5 d2af2763c820018cc655b61f9093efb4
BLAKE2b-256 3b9f0dcc0caa71c06c251ffed46c5b42aa578730f6dc8a74687e21d574a27b79

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