Skip to main content

TensorFlow IO

Project description




TensorFlow I/O

GitHub CI PyPI CRAN 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 the example of Get Started with TensorFlow with data processing replaced by tensorflow-io:

import tensorflow as tf
import tensorflow_io as tfio

# Read MNIST into Dataset
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').batch(1)

# By default image data is uint8 so convert 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' URL address are directly passes to tfio.IODataset.from_mnist, the API used to create MNIST Dataset. We are able to do that because tensorflow-io support HTTP file system out of the box. There is no need to download and save files to local directory any more. Note we are also passing the compressed files (gzip) as is, since tensorflow-io is able to detect and uncompress automatically for MNIST dataset if needed.

Please check the official documentation for more detailed usages.

Installation

Python Package

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

$ 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 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("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:

TensorFlow I/O Version TensorFlow Compatibility Release Date
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

Lint

TensorFlow I/O's code conforms through Bazel Buildifier, Clang Format, Black, and Pyupgrade. The following will check the source code and report any lint issues:

bazel run //tools/lint:check

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

bazel run //tools/lint:lint

Alternatively, if you only want to perform one lint check individually, then you can selectively pass black, pyupgrade, bazel, or clang from the above commands.

For example, check with black only could be done with:

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

Fix with Bazel Buildifier or Clang Format could be done with:

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

Check lint with Black or Pyupgrade for an individual python file could be done with:

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

Format individual python file with black and pyupgrade could be done with:

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.

Note 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

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

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

# Install bazel 3.0.0:
curl -OL https://github.com/bazelbuild/bazel/releases/download/3.0.0/bazel-3.0.0-installer-darwin-x86_64.sh
sudo bash -x -e bazel-3.0.0-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

If Xcode is installed, but xcodebuild -version is not showing so, you might need to enable Xcode command line with the command xcode-select -s /Applications/Xcode.app/Contents/Developer. Restart terminal might be required to make the above change effective.

Note from the above the generated shared libraries (.so) are located in bazel-bin directory. When running pytest, TFIO_DATAPATH=bazel-bin has to be passed for shared libraries to be located by python.

Linux

Development of tensorflow-io on Linux is similar to development on macOS. The required packages are gcc, g++, git, bazel, and python 3. Newer versions of gcc or python than default system installed versions might be required though. For instructions how to configure Visual Studio code to be able to build and debug TensorFlow I/O see https://github.com/tensorflow/io/blob/master/docs/vscode.md

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:

# 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 3.0.0
curl -sSOL https://github.com/bazelbuild/bazel/releases/download/3.0.0/bazel-3.0.0-installer-linux-x86_64.sh
sudo bash -x -e bazel-3.0.0-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:

# 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 3.0.0
curl -sSOL https://github.com/bazelbuild/bazel/releases/download/3.0.0/bazel-3.0.0-installer-linux-x86_64.sh
sudo bash -x -e bazel-3.0.0-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:

# 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 3.0.0
curl -sSOL https://github.com/bazelbuild/bazel/releases/download/3.0.0/bazel-3.0.0-installer-linux-x86_64.sh
sudo bash -x -e bazel-3.0.0-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 is 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 readon.

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/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_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:

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/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 :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.dev20200718144544-cp38-cp38-win_amd64.whl (17.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

tensorflow_io_nightly-0.15.0.dev20200718144544-cp38-cp38-manylinux2010_x86_64.whl (22.3 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

tensorflow_io_nightly-0.15.0.dev20200718144544-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.dev20200718144544-cp37-cp37m-win_amd64.whl (17.0 MB view details)

Uploaded CPython 3.7m Windows x86-64

tensorflow_io_nightly-0.15.0.dev20200718144544-cp37-cp37m-manylinux2010_x86_64.whl (22.3 MB view details)

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

tensorflow_io_nightly-0.15.0.dev20200718144544-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.dev20200718144544-cp36-cp36m-win_amd64.whl (17.0 MB view details)

Uploaded CPython 3.6m Windows x86-64

tensorflow_io_nightly-0.15.0.dev20200718144544-cp36-cp36m-manylinux2010_x86_64.whl (22.3 MB view details)

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

tensorflow_io_nightly-0.15.0.dev20200718144544-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.dev20200718144544-cp35-cp35m-win_amd64.whl (17.0 MB view details)

Uploaded CPython 3.5m Windows x86-64

tensorflow_io_nightly-0.15.0.dev20200718144544-cp35-cp35m-manylinux2010_x86_64.whl (22.3 MB view details)

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

tensorflow_io_nightly-0.15.0.dev20200718144544-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.dev20200718144544-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 b4f01d22a55e1b1a4f09ad6b0720fab5046ccc99b6000cd4069a83a9841a62f8
MD5 e2113248c86bef4bddcd079435f702c1
BLAKE2b-256 7900a3d4983f55f441e851c2d62feeadfbe738e33f2269c4784ae2a3e2146428

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp38-cp38-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp38-cp38-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 fa629ab4956a48a4403405f0b5950e2f75ba4df36391fbdb862e81d592554dd8
MD5 8c62f8362276058f71b08b2423ede384
BLAKE2b-256 40dbffd613b333787eaa3f7a13d6bfc164c96ba9aa7960bc7aff3a7b7dc5de18

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp38-cp38-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp38-cp38-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 7214d578b4afe7ee6cfaa3b10d616a8f1eb00a33aec6f28398cb63614e7cd833
MD5 d44a56b25b0ecb1145714fe08a8f7643
BLAKE2b-256 552bea4b694d32c3c710a87790d9b8c9e171fcc8e283dc34d3901c580b824e67

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 df22c766dc2cc6e5f858c91f3cdc57e7d8c93046d06285898b82c3a36196ba2b
MD5 0f3eacd528a67d38e9c4d680c2f11466
BLAKE2b-256 b7aa2832088568bc88f584ed3be7de0d3518bf0c89432f9c525a45dbb548ffa5

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp37-cp37m-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 5c8f72fc996597ab7755eb1df74e40d509531f3945674fac7f7ce070947f2d98
MD5 10737d5c5239af1c32ff892b7498d48c
BLAKE2b-256 82cae6c0c5fdcc2b670129b2c3652950eea6153977bdb32f1b57e1a04ab65c5e

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp37-cp37m-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp37-cp37m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 8265d9cdadfc6175d0e59ee2669e64d709925d9f270199120051774830787ca2
MD5 218c09d0a0dee06fee4cd1dec3ba24d8
BLAKE2b-256 3710cceffa63e378aeae87ea6cd93bf6888f46104512fb4513293e0dd691192a

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 8ca5efb2aab5aafcd4891edcc15e6a7bd8d567ce2a2975075fa92396d8628604
MD5 59a157c9e573c89d4c5e73336fe92be1
BLAKE2b-256 52935534813a76fc0d88f86d956cfbde1f3cb0b9b9e653b59ee35481b4b13985

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp36-cp36m-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 897c228aecb5f9b88841d74c366e02d5580244dbe718b446362eb6138565dd83
MD5 13cca128d45316e74e75d9f291ff36bd
BLAKE2b-256 5481e8e56362f8ad82c8a8103a51d7d6e33ca40f892483a9c104f487dc8e3494

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp36-cp36m-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp36-cp36m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 1f3f46d9b84b8cc117ade18d3f8550a89de36859e28476638a676b8820225fcc
MD5 5a8f2ffd6a23c44006a7c8d2be44563c
BLAKE2b-256 d12cb3c1a9ad22dac2c5c425e96c8498e5beb66a4eed92c5bfda534339897c5c

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp35-cp35m-win_amd64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 78f6bcb148d0d2962f7d1fe865cce2900281fa20118787b995e27cff2de402e7
MD5 7ca136b8101ce872e19ddbb86aef0edb
BLAKE2b-256 733992415f45de1f0a4ebab0efd20f22524c47a22dcf43bcb982c4417c907621

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp35-cp35m-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp35-cp35m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 00a25368cb620a7bb5e8cd8a57a32bdc39024c9dbab762af5d0c8b2ccd01dd43
MD5 40aa2487f34fa39a124b4f3473cb0f0a
BLAKE2b-256 0a93ddc35b86e531c235291040616893bb3130507392e567bd5a02fc996c4885

See more details on using hashes here.

File details

Details for the file tensorflow_io_nightly-0.15.0.dev20200718144544-cp35-cp35m-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tensorflow_io_nightly-0.15.0.dev20200718144544-cp35-cp35m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 654f8dcf739b50325aa367e527b775933880578730a64df098294ce352b74e58
MD5 aa01848e5b2435378b6d44f910380de8
BLAKE2b-256 d504ce07c1a4c45f7b25d3edbb0b1ebe51b4b178cf9c7dfc8169527cf5284911

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