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.ffmpeg: Video and Audio 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.cifar: CIFAR file format support.tensorflow_io.pubsub: Google Cloud Pub/Sub support.tensorflow_io.bigtable: Google Cloud Bigtable support.tensorflow_io.oss: Alibaba Cloud Object Storage Service (OSS) support. Usage guide here.tensorflow_io.avro: Apache Avro file format support.tensorflow_io.audio: WAV file format support.tensorflow_io.grpc: gRPC server Dataset, support for streaming Numpy input.tensorflow_io.hdf5: HDF5 file format support.tensorflow_io.text: Text file with archive support.tensorflow_io.pcap: Pcap network packet capture file support.tensorflow_io.azure: Microsoft Azure Storage support. Usage guide here.tensorflow_io.bigquery: Google Cloud BigQuery support.tensorflow_io.gcs: GCS Configuration support.tensorflow_io.prometheus: Prometheus observation data support.tensorflow_io.dicom: DICOM Image file format support. Usage guide here.
The tensorflow-io Python package could be installed with pip directly:
$ pip install tensorflow-ioPeople who are a little more adventurous can also try our nightly binaries:
$ pip install tensorflow-io-nightlyThe 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',
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 are assumed to have been downloaded and saved to the local directory. Compression files (e.g. gzip) could be detected and uncompressed automatically.
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")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 |
|---|---|---|
| 0.7.0 | 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 |
|---|---|
| Linux CPU Python 2 |  |
| Linux CPU Python 3 |  |
| Linux GPU Python 2 |  |
| Linux GPU Python 3 |  |
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_wheelA 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.
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 kinesisStyle checks for Python can be run with the following commands:
$ curl -o .pylint -sSL https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/tools/ci_build/pylintrc
$ find . -name '*.py' | xargs pylint --rcfile=.pylintWe 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)
})- SIG IO Google Group and mailing list: io@tensorflow.org
- SIG IO Monthly Meeting Notes
- Gitter room: tensorflow/sig-io