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.dev20220103232417-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 2a08fc8febbb68d3ff299ff331214882cf783ecdc32d371ec10d59cc8a8a98dc
MD5 d59bcba5f6fa684043d3e0da2b4a6c19
BLAKE2b-256 e75450b7b4f4ecb8a9a0da67a8183654edb9ae0b2689a926752f73c9b24a01da

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-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.dev20220103232417-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 647ace8bfc59ca6323cff98e1921f5a0e72a6e75f817429d37b0d62b8c42faee
MD5 7bd2f3d1fe20f17200b7799aeb53d77c
BLAKE2b-256 aca94e2d24e83682cc4b623ab5a04bd42bae41d11d77fc863b0375f7aa0b6a0f

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp310-cp310-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp310-cp310-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 6b95c0d4111ea66d9b658d4aa3a3eac07b57cb83818c931760758f3dabe6d526
MD5 b686b185530b94b3d361df21127862cc
BLAKE2b-256 8935e5e4fa59f3b6798cb19abf67177e00cb2b881bf8ac442c39bf747f6cc720

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 ec08942a51acd3dc93e93c0c6fe8e45b6b411e349d399eedd1079bea0ef02696
MD5 123ed673b2bebd00c1187e146918dc8d
BLAKE2b-256 80e35b552c175b0b6fdf6c7a537bdb5a5e0e5a0bc699d7b82f3126262f2ee34f

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-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.dev20220103232417-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 e508d2a27b10a8676361c2bdb5138f3875b7271498ccfb65890cc83a0ebb9781
MD5 d49ba9ec41a55e238e615bb16d7baa9e
BLAKE2b-256 6486d3bad4fa476573390efbb1613c593b499a328bd98861fcaa8e6380fe9aa5

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 12b855c2f31c096482fbe227646437dd37cb3455998c2dc276ee30376d4705ec
MD5 5d35bb3cf78ba30b16081493d6e626d5
BLAKE2b-256 70aac4eb4a51b03ec74a6e4c5de409341d376eae1e10b9495d3cdd1ad183f20f

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 2d0af680822610d73b1405980869afc9afafec6886b51b99541eb310ef8a4cf9
MD5 764b8ae8363adcec79cfdee033a122c9
BLAKE2b-256 7e6507e70772d35f2f68352249be174b68c65807ac144650c6ff1e2769f83b65

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-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.dev20220103232417-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 7d090271d1b66510ba25a1ad51cf0cb1a4a185cafd8e97d306be0934b9f89b85
MD5 835f8e14bc68a2c1b007c8200f9f8cce
BLAKE2b-256 7d20d775fd9ec0250ccf8d60b5d2b0ef6b995c7b4d4e69ef3d0adcb7dba69edc

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 07dcd1a339de011e07b79190e22e6375e9c2cd7115a9bc98da4c25140eb54b95
MD5 beea90826be5dee79c2a811bdd437cae
BLAKE2b-256 cbe2c295db61401870a5dd73d69320f3195b2e151f02b605b884a287a2d450c7

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 b0590d5a335ff991a470c2915295184713a4bc72fca4d2269ed2a40bf523b5eb
MD5 f81c09bfb9c02bc83c2c948bf09f89c7
BLAKE2b-256 ec24c0ea3b66050a3f23bd83a5991bb8e60ff11299b32f898cfe4159312e6b08

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-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.dev20220103232417-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 63d417dcb5d73fde7bf50766b4d83da04dce79613b3f6c91e60fc93b9b5decdc
MD5 94e25036a832d12633076450027ef474
BLAKE2b-256 c6d91cb35e2cb84470f4553df1bda67ea96ded409ea341d4d408b5b25ef64d20

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.24.0.dev20220103232417-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 30181d56df21290913472c210f496b67cf85e2c9eefb547cb0e5191b7a1450b7
MD5 4a1611b7ffeeb1c00ebe9934dd23a54e
BLAKE2b-256 7cce45b97013114272e89c2c829d0dbc7cc6fa6b776cc0e729452e223b5a04ec

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