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.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.tensorflow_io.json: JSON file 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 |  |
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.shIt 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:
- Offical PubSub Emulator by Google Cloud for Cloud PubSub.
- Official Azurite Emulator by Azure for Azure Storage.
- None-official LocalStack emulator by LocalStack for AWS Kinesis.
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_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.
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.
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 and Bazel can be run with the following commands (docker has to be available):
$ bash -x -e .travis/lint.shIn 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.
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)
})- SIG IO Google Group and mailing list: [email protected]
- SIG IO Monthly Meeting Notes
- Gitter room: tensorflow/sig-io
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