TensorFlow I/O

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

At the moment TensorFlow I/O supports the following data sources:

tensorflow_io.ignite: Data source for Apache Ignite and Ignite File System (IGFS). Overview and usage guide here.
tensorflow_io.kafka: Apache Kafka stream-processing support.
tensorflow_io.kinesis: Amazon Kinesis data streams support.
tensorflow_io.hadoop: Hadoop SequenceFile format support.
tensorflow_io.arrow: Apache Arrow data format support. Usage guide here.
tensorflow_io.image: WebP and TIFF image format support.
tensorflow_io.libsvm: LIBSVM file format support.
tensorflow_io.video: Video file support with FFmpeg.
tensorflow_io.parquet: Apache Parquet data format support.
tensorflow_io.lmdb: LMDB file format support.
tensorflow_io.mnist: MNIST file format support.
tensorflow_io.pubsub: Google Cloud Pub/Sub support.

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

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.mnist as mnist_io

# Read MNIST into tf.data.Dataset
d_train = mnist_io.MNISTDataset(
    'train-images-idx3-ubyte.gz',
    'train-labels-idx1-ubyte.gz',
    compression_type="GZIP")

# 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)).batch(1)

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 are assumed to have been downloaded and saved to the local directory. Compression files could be processed automatically with compression_type="GZIP".

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")

Developing

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 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++
$ bazel build -s --verbose_failures //tensorflow_io/...
$ # Run tests with PyTest, note: some tests require launching additional containers to run (see below)
$ pytest 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.

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

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)
})

Community

SIG IO Google Group and mailing list: io@tensorflow.org
SIG IO Monthly Meeting Notes
Gitter room: tensorflow/sig-io

License

Apache License 2.0

jjmachan/io