/io

Dataset, streaming, and file system extensions maintained by TensorFlow SIG-IO

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TensorFlow I/O

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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 the example of Get Started with TensorFlow with data processing replaced by tensorflow-io:

import tensorflow as tf
import tensorflow_io as tfio

# Read MNIST into Dataset
d_train = tfio.IODataset.from_mnist(
    'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
    'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz').batch(1)

# By default image data is uint8 so conver to float32.
d_train = d_train.map(lambda x, y: (tf.image.convert_image_dtype(x, tf.float32), y))

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)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(d_train, epochs=5, steps_per_epoch=10000)

Note that in the above example, MNIST database files' URL address are directly passes to tfio.IODataset.from_mnist, the API used to create MNIST Dataset. We are able to do that because tensorflow-io support HTTP file system out of the box. There is no need to download and save files to local directory any more. Note we are also passing the compressed files (gzip) as is, since tensorflow-io is able to detect and uncompress automatically for MNIST dataset if needed.

Please check the official documentation for more detailed usages.

Installation

Python Package

The tensorflow-io Python package could be installed with pip directly:

$ pip install tensorflow-io

People who are a little more adventurous can also try our nightly binaries:

$ pip install tensorflow-io-nightly

R Package

Once the tensorflow-io Python package has beem successfully installed, you can then install the latest stable release of the R package via:

install.packages('tfio')

You can also install the development version from Github via:

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:

TensorFlow I/O Version TensorFlow Compatibility Release Date
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 5, 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

Development

Lint

TensorFlow I/O's code conforms through Pylint, Bazel Buildifier, Clang Format, Black, and Pyupgrade. The following will check the source code and report any lint issues:

bazel run //tools/lint:check

For Bazel Buildifier and Clang Format, the following will automatically fix and lint errors:

bazel run //tools/lint:lint

Alternatively, if you only want to perform lint check on one aespect, then you can selectively pass pylint, bazel, or clang from the above commands.

For example, check with Pylint only could be done with:

bazel run //tools/lint:check -- pylint

Fix with Bazel Buildifier or Clang Format could be done with:

bazel run //tools/lint:lint -- bazel clang

Check lint with Black or Pyupgrade for an individual python file could be done with:

bazel run //tools/lint:check -- black pyupgrade -- tensorflow_io/core/python/ops/version_ops.py

Python

macOS

On macOS Catalina or higher, it is possible to build tensorflow-io with system provided python 3 (3.7.3). Both tensorflow and bazel are needed.

Note Xcode installation is needed as tensorflow-io requires Swift for accessing Apple's native AVFoundation APIs.

Note also there is a bug in macOS's native python 3.7.3 that could be fixed with tensorflow/tensorflow#33183 (comment)

# Use following command to check if Xcode is correctly installed:
xcodebuild -version

# macOS's default python3 is 3.7.3
python3 --version

# Install bazel 3.0.0:
curl -OL https://github.com/bazelbuild/bazel/releases/download/3.0.0/bazel-3.0.0-installer-darwin-x86_64.sh
sudo bash -x -e bazel-3.0.0-installer-darwin-x86_64.sh

# Install tensorflow and configure bazel
sudo ./configure.sh

# Build shared libraries
bazel build -s --verbose_failures //tensorflow_io/...

# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core/python/ops/` and it is possible
# to run tests with `pytest`, e.g.:
sudo python3 -m pip install pytest
TFIO_DATAPATH=bazel-bin python3 -m pytest -s -v tests/test_serialization_eager.py

If Xcode is installed, but xcodebuild -version is not showing so, you might need to enable Xcode command line with the command xcode-select -s /Applications/Xcode.app/Contents/Developer. Restart terminal might be required to make the above change effective.

Note from the above the generated shared libraries (.so) are located in bazel-bin directory. When running pytest, TFIO_DATAPATH=bazel-bin has to be passed for shared libraries to be located by python.

Linux

Development of tensorflow-io on Linux is similiar to development on macOS. The required packages are gcc, g++, git, bazel, and python 3. Newer versions of gcc or python than default system installed versions might be required though.

Ubuntu 18.04/20.04

Ubuntu 18.04/20.04 requires gcc/g++, git, and python 3. The following will install dependencies and build the shared libraries on Ubuntu 18.04/20.04:

# Install gcc/g++, git, unzip/curl (for bazel), and python3
sudo apt-get -y -qq update
sudo apt-get -y -qq install gcc g++ git unzip curl python3-pip

# Install Bazel 3.0.0
curl -sSOL https://github.com/bazelbuild/bazel/releases/download/3.0.0/bazel-3.0.0-installer-linux-x86_64.sh
sudo bash -x -e bazel-3.0.0-installer-linux-x86_64.sh

# Upgrade pip
sudo python3 -m pip install -U pip

# Install tensorflow and configure bazel
sudo ./configure.sh

# Build shared libraries
bazel build -s --verbose_failures //tensorflow_io/...

# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core/python/ops/` and it is possible
# to run tests with `pytest`, e.g.:
sudo python3 -m pip install pytest
TFIO_DATAPATH=bazel-bin python3 -m pytest -s -v tests/test_serialization_eager.py
CentOS 8

CentOS 8 requires gcc/g++, git, and python 3. The following will install dependencies and build the shared libraries on CentOS 8:

# Install gcc/g++, git, unzip/which (for bazel), and python3
sudo yum install -y python3 python3-devel gcc gcc-c++ git unzip which

# Install Bazel 3.0.0
curl -sSOL https://github.com/bazelbuild/bazel/releases/download/3.0.0/bazel-3.0.0-installer-linux-x86_64.sh
sudo bash -x -e bazel-3.0.0-installer-linux-x86_64.sh

# Upgrade pip
sudo python3 -m pip install -U pip

# Install tensorflow and configure bazel
sudo ./configure.sh

# Build shared libraries
bazel build -s --verbose_failures //tensorflow_io/...

# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core/python/ops/` and it is possible
# to run tests with `pytest`, e.g.:
sudo python3 -m pip install pytest
TFIO_DATAPATH=bazel-bin python3 -m pytest -s -v tests/test_serialization_eager.py
CentOS 7

On CentOS 7, the default python and gcc version are too old to build tensorflow-io's shared libraries (.so). The gcc provided by Developer Toolset and rh-python36 should be used instead. Also, the libstdc++ has to be linked statically to avoid discrepancy of libstdc++ installed on CentOS vs. newer gcc version by devtoolset.

The following will install bazel, devtoolset-9, rh-python36, and build the shared libraries:

# Install centos-release-scl, then install gcc/g++ (devtoolset), git, and python 3
sudo yum install -y centos-release-scl
sudo yum install -y devtoolset-9 git rh-python36

# Install Bazel 3.0.0
curl -sSOL https://github.com/bazelbuild/bazel/releases/download/3.0.0/bazel-3.0.0-installer-linux-x86_64.sh
sudo bash -x -e bazel-3.0.0-installer-linux-x86_64.sh

# Upgrade pip
scl enable rh-python36 devtoolset-9 \
    'python3 -m pip install -U pip'

# Install tensorflow and configure bazel with rh-python36
scl enable rh-python36 devtoolset-9 \
    './configure.sh'

# Build shared libraries
BAZEL_LINKOPTS="-static-libstdc++ -static-libgcc" BAZEL_LINKLIBS="-lm -l%:libstdc++.a" \
  scl enable rh-python36 devtoolset-9 \
    'bazel build -s --verbose_failures //tensorflow_io/...'

# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core/python/ops/` and it is possible
# to run tests with `pytest`, e.g.:
scl enable rh-python36 devtoolset-9 \
    'python3 -m pip install pytest'
TFIO_DATAPATH=bazel-bin \
  scl enable rh-python36 devtoolset-9 \
    'python3 -m pytest -s -v tests/test_serialization_eager.py'

Docker

For Python development, a reference Dockerfile here can be used to build the TensorFlow I/O package (tensorflow-io) from source:

$ # Build and run the Docker image
$ docker build -f tools/dev/Dockerfile -t tfio-dev .
$ docker run -it --rm --net=host -v ${PWD}:/v -w /v tfio-dev
$ # In Docker, configure will install TensorFlow or use existing install
$ ./configure.sh
$ # Build TensorFlow I/O C++. For compilation optimization flags, the default (-march=native) optimizes the generated code for your machine's CPU type. [see here](https://www.tensorflow.org/install/source#configuration_options)
$ bazel build -c opt --copt=-march=native --copt=-fPIC -s --verbose_failures //tensorflow_io/...
$ # Run tests with PyTest, note: some tests require launching additional containers to run (see below)
$ pytest -s -v tests/
$ # Build the TensorFlow I/O package
$ python setup.py bdist_wheel

A package file dist/tensorflow_io-*.whl will be generated after a build is successful.

NOTE: When working in the Python development container, an environment variable TFIO_DATAPATH is automatically set to point tensorflow-io to the shared C++ libraries built by Bazel to run pytest and build the bdist_wheel. Python setup.py can also accept --data [path] as an argument, for example python setup.py --data bazel-bin bdist_wheel.

NOTE: While the tfio-dev container gives developers an easy to work with environment, the released whl packages are build differently due to manylinux2010 requirements. Please check [Build Status and CI] section for more details on how the released whl packages are generated.

Starting Test Containers

Some tests require launching a test container before running. In order to run all tests, execute the following commands:

$ bash -x -e tests/test_ignite/start_ignite.sh
$ bash -x -e tests/test_kafka/kafka_test.sh start kafka
$ bash -x -e tests/test_kinesis/kinesis_test.sh start kinesis

Running Python and Bazel Style Checks

Style checks for Python and Bazel can be run with the following commands (docker has to be available):

$ bash -x -e .travis/lint.sh

In case there are any Bazel style errors, the following command could be invoked to fix and Bazel style issues:

$ docker run -i -t --rm -v $PWD:/v -w /v --net=host golang:1.12 bash -x -e -c 'go get github.com/bazelbuild/buildtools/buildifier && buildifier $(find . -type f \( -name WORKSPACE -or -name BUILD -or -name *.BUILD \))'

After the command is run, any Bazel files with style issues will have been modified and corrected.

R

We provide a reference Dockerfile here for you so that you can use the R package directly for testing. You can build it via:

docker build -t tfio-r-dev -f R-package/scripts/Dockerfile .

Inside the container, you can start your R session, instantiate a SequenceFileDataset from an example Hadoop SequenceFile string.seq, and then use any transformation functions provided by tfdatasets package on the dataset like the following:

library(tfio)
dataset <- sequence_file_dataset("R-package/tests/testthat/testdata/string.seq") %>%
    dataset_repeat(2)

sess <- tf$Session()
iterator <- make_iterator_one_shot(dataset)
next_batch <- iterator_get_next(iterator)

until_out_of_range({
  batch <- sess$run(next_batch)
  print(batch)
})

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 contribution guidelines for a guide on how to contribute.

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:

bash -x -e .travis/python.release.sh

It takes some time to build, but once complete, there will be python 2.7, 3.5, 3.6, 3.7 compatible whl packages available in wheelhouse directory.

On macOS, the same command could be used though the script expect 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.

Note the above command is also the command we use when releasing packages for Linux and macOS.

TensorFlow I/O uses both Travis CI and Google CI (Kokoro) for continuous integration. Travis CI 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 python version to ensure a good coverage:

Python Ubuntu 16.04 Ubuntu 18.04 macOS + osx9
2.7 ✔️ ✔️ ✔️
3.5 ✔️ N/A ✔️
3.6 N/A ✔️ ✔️
3.7 N/A ✔️ N/A

TensorFlow I/O has integrations with may 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/Inite 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 ✔️ ✔️
Apache Ignite ✔️ ✔️
Prometheus ✔️ ✔️
Google PubSub ✔️ ✔️
Azure Storage ✔️ ✔️
AWS Kinesis ✔️ ✔️
Alibaba Cloud OSS ✔️
Google BigTable/BigQuery to be added

Note:

Community

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License

Apache License 2.0