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




TensorFlow I/O

Travis-CI Build Status PyPI Status Badge CRAN_Status_Badge

TensorFlow I/O is a collection of file systems and file formats that are not available in TensorFlow's built-in support.

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("devtools")) install.packages("devtools")
devtools::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
0.9.0 2.0.x Oct 18, 2019
0.8.0 1.15.x Oct 17, 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

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:

Development

Python

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.

Community

More Information

License

Apache License 2.0

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