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
0.29.0 2.11.x Dec 18, 2022
0.28.0 2.11.x Nov 21, 2022
0.27.0 2.10.x Sep 08, 2022
0.26.0 2.9.x May 17, 2022
0.25.0 2.8.x Apr 19, 2022
0.24.0 2.8.x Feb 04, 2022
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-0.29.0.dev20230110165504-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 5744c9e1912ec2fbc85e3f644becc55b605d223a9576423400afd290f888532f
MD5 979851055374172d1d849c251f92b745
BLAKE2b-256 5e8a49f295979805cc3f7b70c9dba4d9be7bccc2b9a38143a2c50b8723d1ee82

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp311-cp311-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp311-cp311-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 5decefc641f0b429447476cadbd3292dbe34c9e5f18b75d020fa8aab483d7e3d
MD5 11c3a9b1d2824d50664d818bb73d9e7d
BLAKE2b-256 53f5115ef9c4eea518cf663d5727caa60d6c608731dd2369415905eb677938f5

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp311-cp311-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp311-cp311-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 d7ceec420f5b4b3f39221087d4fe0d170e5017eac1e4822d12b0767e6a400e9e
MD5 69409e82652c3421490fc7d268de07b3
BLAKE2b-256 081034dc773306d2d52b7a241ad3199d8b5db63d248a37a1981c93847e39ca26

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 450e1b16248f9760cac8a8a4f275ee9233aa5b073c85a9f2cd8a9dbf03c5cdca
MD5 644190d6b98f42fab58260281147709e
BLAKE2b-256 18fa3aead7ffadb9b800a2a850133017feb161d7eae8e601f243643ea0ca8d90

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 b1054fabe11d19a13fd47e6bb1f0a721d95df8b89feb07015faef0c24055c6af
MD5 b9ef3b3fd7611c1eb637f8b4bf4d1073
BLAKE2b-256 0a8563c0f96b0f2bfee60486215d8972a2cdef775a14e7cf45aa4d47de500e7e

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp310-cp310-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp310-cp310-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 28e8c67d3e21bb95ff8246835be79d4911b2cf642b318518cfb1a3b38c5ba191
MD5 b3b4d2210aede7f58a1ee42c7dc702fb
BLAKE2b-256 374183d9ea55ad4818749be2969cab33b05418cb87705740ada0fb28d4f735c5

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 9eb1e248cb4eb969bab3f0f58586f6ee15b64606d2957b12659d4b0f3888b73b
MD5 e1c74c2b5658e9775aa24685c28357b2
BLAKE2b-256 89cf5cf7a2b5aa506d2fa39381cb6843bac1890c57cd8d483ec93587c1c7c586

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 c50fb890030f667448d97e7f2a727cac8e13128ac4ec86ef5a95e31da3ab01a4
MD5 37a1e0252e083a5d6c6a7c4d426f533f
BLAKE2b-256 5bcf5d8345512c65bef7c86cc2da1c0459645026a46a240effdec28a5428a6b4

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp39-cp39-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 c15d9d44a07cd0b2bfe1ec43c76f530e494e441a8d69a209dbf366b91d766252
MD5 6ae048d9af62803ba33d78d7e67c53cc
BLAKE2b-256 9599ccc596515b5d2e5b8aff80aa71af5fb8f4ce89be2e0618d956082cd3d72d

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 d8f7ad880f0f1df4618d7e246389f1325c8a83689a5135cca18c07cc4b6065f1
MD5 84615118bc7434e215fa803223c06e36
BLAKE2b-256 c1ed6f4422e7828a6daafa1c9832312db5ed537dd144c20020007b735db1db8b

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 5b1f57ede9aeb2f51cdc49a7a52a970c93330bba014dd964374c78f5ec40c196
MD5 5a09aede8902d8e6a5fb78ab397120ba
BLAKE2b-256 cca1a00e108748bc86e58d7e88bf884e7791e5418903523a0bf234a736f36cc5

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 890656c17b809b069ddc07adb8b789b3b05ff47114b8df5eac660982ba4dbb54
MD5 0f986aa3142eb507169e1345241a2161
BLAKE2b-256 b0c785cd7410bef1394705bb976faea2eb75c3829ad98a346f5c85f668ea524a

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 660916c66ecce2d20a42191a8a7e7e83a469717f870a05cd3b44991fab8fb941
MD5 1ba2edf15ac21f5986cc7f50ff50981a
BLAKE2b-256 425c1005b7e48fcb4393db13dc99d3d1f9c00247e4a1bb10c9035a2b7a3ebfbd

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 dcad7daf3973edb4f8292e7dddbe85f8aa99be650ab56a0ca017562ad8167c7f
MD5 45307f46ba32ace49ad981b3fc8bc00f
BLAKE2b-256 d0a26c731f181ca4ec0a96178362fc0d2ea193ca4873ce5c0dce28e68b45c947

See more details on using hashes here.

File details

Details for the file tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_gcs_filesystem_nightly-0.29.0.dev20230110165504-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 97eb8fc4ec8d8c4944483c48d7248333d2e4f23345efdabf84c5d445e83927d7
MD5 fd2d4f760cee1fbc080b2ebc0aa70207
BLAKE2b-256 2f91e3a632c755f6bfd5f8d668512513a274724c23cff790e8ca049d232ccb0f

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