Package: tensorflow-io-plugin-gs-nightly
TensorFlow I/OTensorFlow 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 foundhere.The use of tensorflow-io is straightforward with keras. Below is an example
toGet Started with TensorFlowwith
the data processing aspect replaced by tensorflow-io:importtensorflowastfimporttensorflow_ioastfio# Read the MNIST data into the IODataset.dataset_url="https://storage.googleapis.com/cvdf-datasets/mnist/"d_train=tfio.IODataset.from_mnist(dataset_url+"train-images-idx3-ubyte.gz",dataset_url+"train-labels-idx1-ubyte.gz",)# Shuffle the elements of the dataset.d_train=d_train.shuffle(buffer_size=1024)# By default image data is uint8, so convert to float32 using map().d_train=d_train.map(lambdax,y:(tf.image.convert_image_dtype(x,tf.float32),y))# prepare batches the data just like any other tf.data.Datasetd_train=d_train.batch(32)# Build the model.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),])# Compile the model.model.compile(optimizer="adam",loss="sparse_categorical_crossentropy",metrics=["accuracy"])# Fit the model.model.fit(d_train,epochs=5,steps_per_epoch=200)In the aboveMNISTexample, the URL's
to access the dataset files are passed directly to thetfio.IODataset.from_mnistAPI call.
This is due to the inherent support thattensorflow-ioprovides forHTTP/HTTPSfile system,
thus eliminating the need for downloading and saving datasets on a local directory.NOTE: Sincetensorflow-iois able to detect and uncompress the MNIST dataset automatically if needed,
we can pass the URL's for the compressed files (gzip) to the API call as is.Please check the officialdocumentationfor more
detailed and interesting usages of the package.InstallationPython PackageThetensorflow-ioPython package can be installed with pip directly using:$pipinstalltensorflow-ioPeople who are a little more adventurous can also try our nightly binaries:$pipinstalltensorflow-io-nightlyDocker ImagesIn addition to the pip packages, the docker images can be used to quickly get started.For stable builds:$dockerpulltfsigio/tfio:latest
$dockerrun-it--rm--nametfio-latesttfsigio/tfio:latestFor nightly builds:$dockerpulltfsigio/tfio:nightly
$dockerrun-it--rm--nametfio-nightlytfsigio/tfio:nightlyR PackageOnce thetensorflow-ioPython package has been successfully installed, you
can install the development version of the R package from GitHub via the following:if(!require("remotes"))install.packages("remotes")remotes::install_github("tensorflow/io",subdir="R-package")TensorFlow Version CompatibilityTo ensure compatibility with TensorFlow, it is recommended to install a matching
version of TensorFlow I/O according to the table below. You can find the list
of releaseshere.TensorFlow I/O VersionTensorFlow CompatibilityRelease Date0.17.12.4.xApr 16, 20210.17.02.4.xDec 14, 20200.16.02.3.xOct 23, 20200.15.02.3.xAug 03, 20200.14.02.2.xJul 08, 20200.13.02.2.xMay 10, 20200.12.02.1.xFeb 28, 20200.11.02.1.xJan 10, 20200.10.02.0.xDec 05, 20190.9.12.0.xNov 15, 20190.9.02.0.xOct 18, 20190.8.11.15.xNov 15, 20190.8.01.15.xOct 17, 20190.7.21.14.xNov 15, 20190.7.11.14.xOct 18, 20190.7.01.14.xJul 14, 20190.6.01.13.xMay 29, 20190.5.01.13.xApr 12, 20190.4.01.13.xMar 01, 20190.3.01.12.0Feb 15, 20190.2.01.12.0Jan 29, 20190.1.01.12.0Dec 16, 2018Performance BenchmarkingWe usegithub-pagesto document the results of API performance benchmarks. The benchmark job is triggered on every commit tomasterbranch and
facilitates tracking performance w.r.t commits.ContributingTensorflow I/O is a community led open source project. As such, the project
depends on public contributions, bug-fixes, and documentation. Please see:contribution guidelinesfor a guide on how to contribute.development docfor instructions on the development environment setup.tutorialsfor a list of tutorial notebooks and instructions on how to write one.Build Status and CIBuildStatusLinux CPU Python 2Linux CPU Python 3Linux GPU Python 2Linux GPU Python 3Because 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:#!/usr/bin/env bashlsdist/*forfindist/*.whl;dodockerrun-i--rm-v$PWD:/v-w/v--net=hostquay.io/pypa/manylinux2010_x86_64bash-x-e/v/tools/build/auditwheelrepair--platmanylinux2010_x86_64$fdonesudochown-R$(id-nu):$(id-ng).
lswheelhouse/*It takes some time to build, but once complete, there will be python3.5,3.6,3.7compatible whl packages available inwheelhousedirectory.On macOS, the same command could be used. However, the script expectspythonin shell
and will only generate a whl package that matches the version ofpythonin shell. If
you want to build a whl package for a specific python then you have to alias this version
of python topythonin shell. See.github/workflows/build.ymlAuditwheel step for instructions how to do that.Note the above command is also the command we use when releasing packages for Linux and macOS.TensorFlow I/O uses both GitHub Workflows and Google CI (Kokoro) for continuous integration.
GitHub Workflows 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 python3 versions to ensure a good coverage:PythonUbuntu 18.04Ubuntu 20.04macOS + osx9Windows-20192.7:heavy_check_mark::heavy_check_mark::heavy_check_mark:N/A3.7:heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark:3.8:heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark:TensorFlow I/O has integrations with many 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/Ignite 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 SystemEmulatorCI IntegrationOfflineApache Kafka:heavy_check_mark::heavy_check_mark:Apache Ignite:heavy_check_mark::heavy_check_mark:Prometheus:heavy_check_mark::heavy_check_mark:Google PubSub:heavy_check_mark::heavy_check_mark:Azure Storage:heavy_check_mark::heavy_check_mark:AWS Kinesis:heavy_check_mark::heavy_check_mark:Alibaba Cloud OSS:heavy_check_mark:Google BigTable/BigQueryto be addedElasticsearch (experimental):heavy_check_mark::heavy_check_mark:MongoDB (experimental):heavy_check_mark::heavy_check_mark:References for emulators:OfficialPubSub Emulatorby Google Cloud for Cloud PubSub.OfficialAzurite Emulatorby Azure for Azure Storage.None-officialLocalStack emulatorby LocalStack for AWS Kinesis.CommunitySIG IOGoogle Groupand mailing list:io@tensorflow.orgSIG IOMonthly Meeting NotesGitter room:tensorflow/sig-ioAdditional InformationStreaming Machine Learning with Tiered Storage and Without a Data Lake-Kai WaehnerTensorFlow with Apache Arrow Datasets-Bryan CutlerHow to build a custom Dataset for Tensorflow-Ivelin IvanovTensorFlow on Apache Ignite-Anton DmitrievLicenseApache License 2.0