Metadata-Version: 2.1 Name: tensorflow-io-gcs-filesystem Version: 0.31.0 Summary: TensorFlow IO Home-page: https://github.com/tensorflow/io Download-URL: https://github.com/tensorflow/io/tags Author: Google Inc. Author-email: opensource@google.com Project-URL: Source, https://github.com/tensorflow/io Project-URL: Bug Reports, https://github.com/tensorflow/io/issues Project-URL: Documentation, https://tensorflow.org/io Keywords: tensorflow io machine learning Classifier: Development Status :: 4 - Beta Classifier: Intended Audience :: Developers Classifier: Intended Audience :: Education Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: Apache Software License Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3 :: Only Classifier: Topic :: Scientific/Engineering Classifier: Topic :: Scientific/Engineering :: Mathematics Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence Classifier: Topic :: Software Development Classifier: Topic :: Software Development :: Libraries Classifier: Topic :: Software Development :: Libraries :: Python Modules Requires-Python: >=3.7, <3.12 Description-Content-Type: text/markdown License-File: LICENSE Provides-Extra: tensorflow Requires-Dist: tensorflow (<2.12.0,>=2.11.0) ; extra == 'tensorflow' Provides-Extra: tensorflow-aarch64 Requires-Dist: tensorflow-aarch64 (<2.12.0,>=2.11.0) ; extra == 'tensorflow-aarch64' Provides-Extra: tensorflow-cpu Requires-Dist: tensorflow-cpu (<2.12.0,>=2.11.0) ; extra == 'tensorflow-cpu' Provides-Extra: tensorflow-gpu Requires-Dist: tensorflow-gpu (<2.12.0,>=2.11.0) ; extra == 'tensorflow-gpu' Provides-Extra: tensorflow-rocm Requires-Dist: tensorflow-rocm (<2.12.0,>=2.11.0) ; extra == 'tensorflow-rocm'


----------------- # TensorFlow I/O [![GitHub CI](https://github.com/tensorflow/io/workflows/GitHub%20CI/badge.svg?branch=master)](https://github.com/tensorflow/io/actions?query=branch%3Amaster) [![PyPI](https://badge.fury.io/py/tensorflow-io.svg)](https://pypi.org/project/tensorflow-io/) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/tensorflow/io/blob/master/LICENSE) [![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/io) TensorFlow 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 found [here](https://www.tensorflow.org/io/api_docs/python/tfio). The use of tensorflow-io is straightforward with keras. Below is an example to [Get Started with TensorFlow](https://www.tensorflow.org/tutorials/quickstart/beginner) with the data processing aspect replaced by tensorflow-io: ```python import tensorflow as tf import tensorflow_io as tfio # 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(lambda x, y: (tf.image.convert_image_dtype(x, tf.float32), y)) # prepare batches the data just like any other tf.data.Dataset d_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 above [MNIST](http://yann.lecun.com/exdb/mnist/) example, the URL's to access the dataset files are passed directly to the `tfio.IODataset.from_mnist` API call. This is due to the inherent support that `tensorflow-io` provides for `HTTP`/`HTTPS` file system, thus eliminating the need for downloading and saving datasets on a local directory. NOTE: Since `tensorflow-io` is 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 official [documentation](https://www.tensorflow.org/io) for more detailed and interesting usages of the package. ## Installation ### Python Package The `tensorflow-io` Python package can be installed with pip directly using: ```sh $ pip install tensorflow-io ``` People who are a little more adventurous can also try our nightly binaries: ```sh $ pip install tensorflow-io-nightly ``` To ensure you have a version of TensorFlow that is compatible with TensorFlow-IO, you can specify the `tensorflow` extra requirement during install: ``` pip install tensorflow-io[tensorflow] ``` Similar extras exist for the `tensorflow-gpu`, `tensorflow-cpu` and `tensorflow-rocm` packages. ### Docker Images In addition to the pip packages, the docker images can be used to quickly get started. For stable builds: ```sh $ docker pull tfsigio/tfio:latest $ docker run -it --rm --name tfio-latest tfsigio/tfio:latest ``` For nightly builds: ```sh $ docker pull tfsigio/tfio:nightly $ docker run -it --rm --name tfio-nightly tfsigio/tfio:nightly ``` ### R Package Once the `tensorflow-io` Python package has been successfully installed, you can install the development version of the R package from GitHub via the following: ```r if (!require("remotes")) install.packages("remotes") remotes::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. You can find the list of releases [here](https://github.com/tensorflow/io/releases). | TensorFlow I/O Version | TensorFlow Compatibility | Release Date | | --- | --- | --- | | 0.31.0 | 2.11.x | Feb 25, 2022 | | 0.30.0 | 2.11.x | Jan 20, 2022 | | 0.29.0 | 2.11.x | Dec 18, 2022 | | 0.28.0 | 2.11.x | Nov 21, 2022 | | 0.27.0 | 2.10.x | Sep 08, 2022 | | 0.26.0 | 2.9.x | May 17, 2022 | | 0.25.0 | 2.8.x | Apr 19, 2022 | | 0.24.0 | 2.8.x | Feb 04, 2022 | | 0.23.1 | 2.7.x | Dec 15, 2021 | | 0.23.0 | 2.7.x | Dec 14, 2021 | | 0.22.0 | 2.7.x | Nov 10, 2021 | | 0.21.0 | 2.6.x | Sep 12, 2021 | | 0.20.0 | 2.6.x | Aug 11, 2021 | | 0.19.1 | 2.5.x | Jul 25, 2021 | | 0.19.0 | 2.5.x | Jun 25, 2021 | | 0.18.0 | 2.5.x | May 13, 2021 | | 0.17.1 | 2.4.x | Apr 16, 2021 | | 0.17.0 | 2.4.x | Dec 14, 2020 | | 0.16.0 | 2.3.x | Oct 23, 2020 | | 0.15.0 | 2.3.x | Aug 03, 2020 | | 0.14.0 | 2.2.x | Jul 08, 2020 | | 0.13.0 | 2.2.x | May 10, 2020 | | 0.12.0 | 2.1.x | Feb 28, 2020 | | 0.11.0 | 2.1.x | Jan 10, 2020 | | 0.10.0 | 2.0.x | Dec 05, 2019 | | 0.9.1 | 2.0.x | Nov 15, 2019 | | 0.9.0 | 2.0.x | Oct 18, 2019 | | 0.8.1 | 1.15.x | Nov 15, 2019 | | 0.8.0 | 1.15.x | Oct 17, 2019 | | 0.7.2 | 1.14.x | Nov 15, 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 | ## Performance Benchmarking We use [github-pages](https://tensorflow.github.io/io/dev/bench/) to document the results of API performance benchmarks. The benchmark job is triggered on every commit to `master` branch and facilitates tracking performance w.r.t commits. ## 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](CONTRIBUTING.md) for a guide on how to contribute. - [development doc](docs/development.md) for instructions on the development environment setup. - [tutorials](docs/tutorials) for a list of tutorial notebooks and instructions on how to write one. ### Build Status and CI | Build | Status | | --- | --- | | Linux CPU Python 2 | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/io/ubuntu-py2.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/io/ubuntu-py2.html) | | Linux CPU Python 3 | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/io/ubuntu-py3.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/io/ubuntu-py3.html) | | Linux GPU Python 2| [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/io/ubuntu-gpu-py2.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/io/ubuntu-gpu-py2.html) | | Linux GPU Python 3| [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/io/ubuntu-gpu-py3.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/io/ubuntu-gpu-py3.html) | 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: ```sh #!/usr/bin/env bash ls dist/* for f in dist/*.whl; do docker run -i --rm -v $PWD:/v -w /v --net=host quay.io/pypa/manylinux2010_x86_64 bash -x -e /v/tools/build/auditwheel repair --plat manylinux2010_x86_64 $f done sudo chown -R $(id -nu):$(id -ng) . ls wheelhouse/* ``` It takes some time to build, but once complete, there will be python `3.5`, `3.6`, `3.7` compatible whl packages available in `wheelhouse` directory. On macOS, the same command could be used. However, the script expects `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. See [.github/workflows/build.yml](.github/workflows/build.yml) Auditwheel 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: | Python | Ubuntu 18.04| Ubuntu 20.04 | macOS + osx9 | Windows-2019 | | ------- | ----- | ------- | ------- | --------- | | 2.7 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | N/A | | 3.7 | :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 System | Emulator| CI Integration | Offline | | ------- | ----- | ----- | ----- | ----- | | Apache 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/BigQuery | | to be added | | | | Elasticsearch (experimental) | :heavy_check_mark: | |:heavy_check_mark:| | | MongoDB (experimental) | :heavy_check_mark: | |:heavy_check_mark:| | References for emulators: - Official [PubSub Emulator](https://cloud.google.com/sdk/gcloud/reference/beta/emulators/pubsub/) by Google Cloud for Cloud PubSub. - Official [Azurite Emulator](https://github.com/Azure/Azurite) by Azure for Azure Storage. - None-official [LocalStack emulator](https://github.com/localstack/localstack) by LocalStack for AWS Kinesis. ## Community * SIG IO [Google Group](https://groups.google.com/a/tensorflow.org/forum/#!forum/io) and mailing list: [io@tensorflow.org](io@tensorflow.org) * SIG IO [Monthly Meeting Notes](https://docs.google.com/document/d/1CB51yJxns5WA4Ylv89D-a5qReiGTC0GYum6DU-9nKGo/edit) * Gitter room: [tensorflow/sig-io](https://gitter.im/tensorflow/sig-io) ## Additional Information * [Streaming Machine Learning with Tiered Storage and Without a Data Lake](https://www.confluent.io/blog/streaming-machine-learning-with-tiered-storage/) - [Kai Waehner](https://github.com/kaiwaehner) * [TensorFlow with Apache Arrow Datasets](https://medium.com/tensorflow/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f) - [Bryan Cutler](https://github.com/BryanCutler) * [How to build a custom Dataset for Tensorflow](https://towardsdatascience.com/how-to-build-a-custom-dataset-for-tensorflow-1fe3967544d8) - [Ivelin Ivanov](https://github.com/ivelin) * [TensorFlow on Apache Ignite](https://medium.com/tensorflow/tensorflow-on-apache-ignite-99f1fc60efeb) - [Anton Dmitriev](https://github.com/dmitrievanthony) ## License [Apache License 2.0](LICENSE)