Skip to main content

TensorFlow IO

Project description




TensorFlow I/O

GitHub CI PyPI License Documentation

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.

The use of tensorflow-io is straightforward with keras. Below is an example to Get Started with TensorFlow with the data processing aspect replaced by tensorflow-io:

import tensorflow as tf
import tensorflow_io as tfio

# Read the MNIST data into the IODataset.
d_train = tfio.IODataset.from_mnist(
    'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
    'http://yann.lecun.com/exdb/mnist/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 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 the HTTP 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 for more detailed and interesting usages of the package.

Installation

Python Package

The tensorflow-io Python package can be installed with pip directly using:

$ pip install tensorflow-io

People who are a little more adventurous can also try our nightly binaries:

$ pip install tensorflow-io-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:

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.

TensorFlow I/O Version TensorFlow Compatibility Release Date
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

Development

IDE Setup

For instructions on how to configure Visual Studio Code for developing TensorFlow I/O, please refer to https://github.com/tensorflow/io/blob/master/docs/vscode.md

Lint

TensorFlow I/O's code conforms to Bazel Buildifier, Clang Format, Black, and Pyupgrade. Please use the following command to check the source code and identify lint issues:

$ bazel run //tools/lint:check

For Bazel Buildifier and Clang Format, the following command will automatically identify and fix any lint errors:

$ bazel run //tools/lint:lint

Alternatively, if you only want to perform lint check using individual linters, then you can selectively pass black, pyupgrade, bazel, or clang to the above commands.

For example, a black specific lint check can be done using:

$ bazel run //tools/lint:check -- black

Lint fix using Bazel Buildifier and Clang Format can be done using:

$ bazel run //tools/lint:lint -- bazel clang

Lint check using black and pyupgrade for an individual python file can be done using:

$ bazel run //tools/lint:check -- black pyupgrade -- tensorflow_io/core/python/ops/version_ops.py

Lint fix an individual python file with black and pyupgrade using:

$ bazel run //tools/lint:lint -- black pyupgrade --  tensorflow_io/core/python/ops/version_ops.py

Python

macOS

On macOS Catalina or higher, it is possible to build tensorflow-io with system provided python 3 (3.7.3). Both tensorflow and bazel are needed.

NOTE: Xcode installation is needed as tensorflow-io requires Swift for accessing Apple's native AVFoundation APIs. Also there is a bug in macOS's native python 3.7.3 that could be fixed with https://github.com/tensorflow/tensorflow/issues/33183#issuecomment-554701214

#!/usr/bin/env bash

# Use following command to check if Xcode is correctly installed:
xcodebuild -version

# macOS's default python3 is 3.7.3
python3 --version

# Install Bazel version specified in .bazelversion
curl -OL https://github.com/bazelbuild/bazel/releases/download/$(cat .bazelversion)/bazel-$(cat .bazelversion)-installer-darwin-x86_64.sh
sudo bash -x -e bazel-$(cat .bazelversion)-installer-darwin-x86_64.sh

# Install tensorflow and configure bazel
sudo ./configure.sh

# Build shared libraries
bazel build -s --verbose_failures //tensorflow_io/...

# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core/python/ops/` and it is possible
# to run tests with `pytest`, e.g.:
sudo python3 -m pip install pytest
TFIO_DATAPATH=bazel-bin python3 -m pytest -s -v tests/test_serialization_eager.py

NOTE: When running pytest, TFIO_DATAPATH=bazel-bin has to be passed so that python can utilize the generated shared libraries after the build process.

Troubleshoot

If Xcode is installed, but $ xcodebuild -version is not displaying the expected output, you might need to enable Xcode command line with the command:

$ xcode-select -s /Applications/Xcode.app/Contents/Developer.

A terminal restart might be required for the changes to take effect.

Sample output:

$ xcodebuild -version
Xcode 11.6
Build version 11E708

Linux

Development of tensorflow-io on Linux is similar to macOS. The required packages are gcc, g++, git, bazel, and python 3. Newer versions of gcc or python, other than the default system installed versions might be required though.

Ubuntu 18.04/20.04

Ubuntu 18.04/20.04 requires gcc/g++, git, and python 3. The following will install dependencies and build the shared libraries on Ubuntu 18.04/20.04:

#!/usr/bin/env bash

# Install gcc/g++, git, unzip/curl (for bazel), and python3
sudo apt-get -y -qq update
sudo apt-get -y -qq install gcc g++ git unzip curl python3-pip

# Install Bazel version specified in .bazelversion
curl -sSOL https://github.com/bazelbuild/bazel/releases/download/$(cat .bazelversion)/bazel-$(cat .bazelversion)-installer-linux-x86_64.sh
sudo bash -x -e bazel-$(cat .bazelversion)-installer-linux-x86_64.sh

# Upgrade pip
sudo python3 -m pip install -U pip

# Install tensorflow and configure bazel
sudo ./configure.sh

# Build shared libraries
bazel build -s --verbose_failures //tensorflow_io/...

# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core/python/ops/` and it is possible
# to run tests with `pytest`, e.g.:
sudo python3 -m pip install pytest
TFIO_DATAPATH=bazel-bin python3 -m pytest -s -v tests/test_serialization_eager.py
CentOS 8

CentOS 8 requires gcc/g++, git, and python 3. The following will install dependencies and build the shared libraries on CentOS 8:

#!/usr/bin/env bash

# Install gcc/g++, git, unzip/which (for bazel), and python3
sudo yum install -y python3 python3-devel gcc gcc-c++ git unzip which

# Install Bazel version specified in .bazelversion
curl -sSOL https://github.com/bazelbuild/bazel/releases/download/$(cat .bazelversion)/bazel-$(cat .bazelversion)-installer-linux-x86_64.sh
sudo bash -x -e bazel-$(cat .bazelversion)-installer-linux-x86_64.sh

# Upgrade pip
sudo python3 -m pip install -U pip

# Install tensorflow and configure bazel
sudo ./configure.sh

# Build shared libraries
bazel build -s --verbose_failures //tensorflow_io/...

# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core/python/ops/` and it is possible
# to run tests with `pytest`, e.g.:
sudo python3 -m pip install pytest
TFIO_DATAPATH=bazel-bin python3 -m pytest -s -v tests/test_serialization_eager.py
CentOS 7

On CentOS 7, the default python and gcc version are too old to build tensorflow-io's shared libraries (.so). The gcc provided by Developer Toolset and rh-python36 should be used instead. Also, the libstdc++ has to be linked statically to avoid discrepancy of libstdc++ installed on CentOS vs. newer gcc version by devtoolset.

The following will install bazel, devtoolset-9, rh-python36, and build the shared libraries:

#!/usr/bin/env bash

# Install centos-release-scl, then install gcc/g++ (devtoolset), git, and python 3
sudo yum install -y centos-release-scl
sudo yum install -y devtoolset-9 git rh-python36

# Install Bazel version specified in .bazelversion
curl -sSOL https://github.com/bazelbuild/bazel/releases/download/$(cat .bazelversion)/bazel-$(cat .bazelversion)-installer-linux-x86_64.sh
sudo bash -x -e bazel-$(cat .bazelversion)-installer-linux-x86_64.sh

# Upgrade pip
scl enable rh-python36 devtoolset-9 \
    'python3 -m pip install -U pip'

# Install tensorflow and configure bazel with rh-python36
scl enable rh-python36 devtoolset-9 \
    './configure.sh'

# Build shared libraries
BAZEL_LINKOPTS="-static-libstdc++ -static-libgcc" BAZEL_LINKLIBS="-lm -l%:libstdc++.a" \
  scl enable rh-python36 devtoolset-9 \
    'bazel build -s --verbose_failures //tensorflow_io/...'

# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core/python/ops/` and it is possible
# to run tests with `pytest`, e.g.:
scl enable rh-python36 devtoolset-9 \
    'python3 -m pip install pytest'

TFIO_DATAPATH=bazel-bin \
  scl enable rh-python36 devtoolset-9 \
    'python3 -m pytest -s -v tests/test_serialization_eager.py'

Python Wheels

It is possible to build python wheels after bazel build is complete with the following command:

$ python3 setup.py bdist_wheel --data bazel-bin

The .whl file will be available in dist directory. Note the bazel binary directory bazel-bin has to be passed with --data args in order for setup.py to locate the necessary share objects, as bazel-bin is outside of the tensorflow_io package directory.

Alternatively, source install could be done with:

$ TFIO_DATAPATH=bazel-bin python3 -m pip install .

with TFIO_DATAPATH=bazel-bin passed for the same reason.

Note installing with -e is different from the above. The

$ TFIO_DATAPATH=bazel-bin python3 -m pip install -e .

will not install shared object automatically even with TFIO_DATAPATH=bazel-bin. Instead, TFIO_DATAPATH=bazel-bin has to be passed everytime the program is run after the install:

$ TFIO_DATAPATH=bazel-bin python3

>>> import tensorflow_io as tfio
>>> ...

Docker

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/docker/devel.Dockerfile -t tfio-dev .
$ docker run -it --rm --net=host -v ${PWD}:/v -w /v tfio-dev

# Inside the docker container, ./configure.sh will install TensorFlow or use existing install
(tfio-dev) root@docker-desktop:/v$ ./configure.sh

# Clean up exisiting bazel build's (if any)
(tfio-dev) root@docker-desktop:/v$ rm -rf bazel-*

# 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). NOTE: Based on the available resources, please change the number of job workers to -j 4/8/16 to prevent bazel server terminations and resource oriented build errors.

(tfio-dev) root@docker-desktop:/v$ bazel build -j 8 --copt=-msse4.2 --copt=-mavx --compilation_mode=opt --verbose_failures --test_output=errors --crosstool_top=//third_party/toolchains/gcc7_manylinux2010:toolchain //tensorflow_io/...


# Run tests with PyTest, note: some tests require launching additional containers to run (see below)
(tfio-dev) root@docker-desktop:/v$ pytest -s -v tests/
 # Build the TensorFlow I/O package
(tfio-dev) root@docker-desktop:/v$ 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.

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.

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)
})

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 for a guide on how to contribute.

Build Status and CI

Build Status
Linux CPU Python 2 Status
Linux CPU Python 3 Status
Linux GPU Python 2 Status
Linux GPU Python 3 Status

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:

#!/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 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. See .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 python version to ensure a good coverage:

Python Ubuntu 16.04 Ubuntu 18.04 macOS + osx9
2.7 :heavy_check_mark: :heavy_check_mark: :heavy_check_mark:
3.5 :heavy_check_mark: N/A :heavy_check_mark:
3.6 N/A :heavy_check_mark: :heavy_check_mark:
3.7 N/A :heavy_check_mark: 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/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

Note:

Community

More Information

License

Apache License 2.0

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

tensorflow_io_nightly-0.15.0.dev20200919180744-cp38-cp38-win_amd64.whl (17.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp38-cp38-manylinux2010_x86_64.whl (22.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp38-cp38-macosx_10_13_x86_64.whl (19.1 MB view details)

Uploaded CPython 3.8 macOS 10.13+ x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp37-cp37m-win_amd64.whl (17.0 MB view details)

Uploaded CPython 3.7m Windows x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp37-cp37m-manylinux2010_x86_64.whl (22.4 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp37-cp37m-macosx_10_13_x86_64.whl (19.1 MB view details)

Uploaded CPython 3.7m macOS 10.13+ x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp36-cp36m-win_amd64.whl (17.0 MB view details)

Uploaded CPython 3.6m Windows x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp36-cp36m-manylinux2010_x86_64.whl (22.4 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.12+ x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp36-cp36m-macosx_10_13_x86_64.whl (19.1 MB view details)

Uploaded CPython 3.6m macOS 10.13+ x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp35-cp35m-win_amd64.whl (17.0 MB view details)

Uploaded CPython 3.5m Windows x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp35-cp35m-manylinux2010_x86_64.whl (22.4 MB view details)

Uploaded CPython 3.5m manylinux: glibc 2.12+ x86-64

tensorflow_io_nightly-0.15.0.dev20200919180744-cp35-cp35m-macosx_10_13_x86_64.whl (19.1 MB view details)

Uploaded CPython 3.5m macOS 10.13+ x86-64

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 67e951f400f600a34c16caba4a7fc92550aa9d23cc8045ce92aa7095618cf744
MD5 48abab84aa87f9294d48afda29c1a799
BLAKE2b-256 7ef4a251d5e5e8630b540222d422760d30accb67e59640848f895cf33985df32

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp38-cp38-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp38-cp38-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 991baa63917a656cefbb7b7e310057caa0e250a807497abc6a791c02e8737a00
MD5 f337f2b98f45c2d8e173a7c305ec464d
BLAKE2b-256 7a3fb4f883ef687a006342178066889fe5a378d4eaaa159f742a4ede9b4f5aed

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp38-cp38-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp38-cp38-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 167aec0292d51dbe64c053a7ab2ba93790dddd3a1c7b5b721db468929af1dc63
MD5 40d868b21d91c8e7e6300c00cb16a831
BLAKE2b-256 d96ed1fad9c0dc8e02f61033019aa25bcebf6e53537092226308105b6918c2a7

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 340f078624efe350d4af5cb48e2479ee77b3c94bffea19615ef4093aa38fd1ec
MD5 1f33bc1b82052021d576818b9e667abf
BLAKE2b-256 de7abdc973ef5c80e8ba1399fa76714846ddef9594fae0e548a04f13b3036abe

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp37-cp37m-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 06c4ddee398aff123ffcb68469f8da141a7a198f11bcf3db26cd1a637a8886d3
MD5 39a85acb3e13774569afc4010cd16f13
BLAKE2b-256 1a855ee9dcc80f4fad94a6255591d93332b75d4ff4ea0b01a32c6da8220fffa9

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp37-cp37m-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp37-cp37m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 01ae0bfd3cf61a7ab44b405a2d7887f07aa969723f930db4de56e29b0b184881
MD5 b8e8d9bcae7c69b23dbdd14341c99942
BLAKE2b-256 434673f2fbbf704f633c1ecf837f9ead4f80dbff267be4ed91b35194e6a15303

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 d9bd6a8c0bc8c2ea7012da6b5d23b715bb8fb8b4097f53ef7536fa1ad2dc70b1
MD5 2e7d675a7e8d672546d533490d50d562
BLAKE2b-256 5c73645df8b1f22c6fd64e6e9c21a17eba6f83a7a335718a7724a530d97226b2

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp36-cp36m-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 0eae2e1bde8afa7123deb6da557bc984d4ac0744a85aa324a3f05826d72f9a10
MD5 3a73048c4a32064277475fea6f3f6d07
BLAKE2b-256 fa2ac0173498d8d4233b871aff2deb9344b01d112d0407c7409e0ed0f8db51d1

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp36-cp36m-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp36-cp36m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 c2074662f7143de82d1f5ad8aed057b069713ccf07b25fe43613ce3508ffc565
MD5 6df7c9848bdba0451eb558957f8b1105
BLAKE2b-256 b81ac6a9481681e380b86a54d52a9310e3477e171547ed31454f9589c2cfcee1

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp35-cp35m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 a1de908d1af68e410c86f7ee33b13c1af04dfde046fe3ee1cd8d64539eee9628
MD5 f8135c908d218209a61459edf8074a7f
BLAKE2b-256 9a04511bdc132b8ecaba1bc3830f9357f36d0ae5423b24b66e8cae21c62fc417

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp35-cp35m-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp35-cp35m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 562798819ebe880bc051011dac53398a9369187ebe5cdd39470f3d2b79070395
MD5 a83a2d60681f75f9c2919a150e4190f5
BLAKE2b-256 ee5e7c9403eab86fe94fece29a6fe36d841df09725335d8f6d0a85868213a6c2

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200919180744-cp35-cp35m-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200919180744-cp35-cp35m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 0eb2cc0dc608347e30b27ccd460a6d0dc5cba0000aff59e48195b8c6c880d80b
MD5 b8a186d67f183eab81e95f64160c5779
BLAKE2b-256 642bd4607a858e1948a3a40711af24a0f576f8fc140ebd27129eea5b89597d5f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page