The document contains the necessary information for setting up the development environment
and building the tensorflow-io
package from source on various platforms. Once the setup is completed please refer to the STYLE_GUIDE for guidelines on adding new ops.
For instructions on how to configure Visual Studio Code for developing TensorFlow I/O, please refer to this doc.
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:
# Install Bazelisk (manage bazel version implicitly)
$ curl -sSOL https://github.com/bazelbuild/bazelisk/releases/download/v1.11.0/bazelisk-linux-amd64
$ sudo mv bazelisk-linux-amd64 /usr/local/bin/bazel
$ sudo chmod +x /usr/local/bin/bazel
$ 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/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/python/ops/version_ops.py
On macOS Catalina 10.15.7, it is possible to build tensorflow-io with
system provided python 3.8.2. Both tensorflow
and bazel
are needed to do so.
NOTE: The system default python 3.8.2 on macOS 10.15.7 will cause regex
installation
error caused by compiler option of -arch arm64 -arch x86_64
(similar to the issue
mentioned in giampaolo/psutil#1832). To overcome this issue
export ARCHFLAGS="-arch x86_64"
will be needed to remove arm64 build option.
#!/usr/bin/env bash
# Disable arm64 build by specifying only x86_64 arch.
# Only needed for macOS's system default python 3.8.2 on macOS 10.15.7
export ARCHFLAGS="-arch x86_64"
# Use following command to check if Xcode is correctly installed:
xcodebuild -version
# Show macOS's default python3
python3 --version
# Install Bazelisk (manage bazel version implicitly)
brew install bazelisk
# Install tensorflow and configure bazel
sudo ./configure.sh
# Add any optimization on bazel command, e.g., --compilation_mode=opt,
# --copt=-msse4.2, --remote_cache=, etc.
# export BAZEL_OPTIMIZATION=
# Build shared libraries
bazel build -s --verbose_failures $BAZEL_OPTIMIZATION //tensorflow_io/... //tensorflow_io_gcs_filesystem/...
# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core`, `bazel-bin/tensorflow_io/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.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.
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 12.2
Build version 12B45b
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 22.04 requires gcc/g++, git, and python 3. The following will install dependencies and build the shared libraries on Ubuntu 22.04:
#!/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 python-is-python3 libntirpc-dev
# Install Bazelisk (manage bazel version implicitly)
curl -sSOL https://github.com/bazelbuild/bazelisk/releases/download/v1.11.0/bazelisk-linux-amd64
sudo mv bazelisk-linux-amd64 /usr/local/bin/bazel
sudo chmod +x /usr/local/bin/bazel
# Upgrade pip
sudo python3 -m pip install -U pip
# Install tensorflow and configure bazel
sudo ./configure.sh
# Add any optimization on bazel command, e.g., --compilation_mode=opt,
# --copt=-msse4.2, --remote_cache=, etc.
# export BAZEL_OPTIMIZATION=
# Build shared libraries
bazel build -s --verbose_failures $BAZEL_OPTIMIZATION --copt="-Wno-error=array-parameter=" --copt="-I/usr/include/tirpc" //tensorflow_io/... //tensorflow_io_gcs_filesystem/...
# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core`, `bazel-bin/tensorflow_io/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.py
The steps to build shared libraries for CentOS 8 is similar to Ubuntu 20.04 above except that
sudo yum install -y python3 python3-devel gcc gcc-c++ git unzip which make
should be used instead to install gcc/g++, git, unzip/which (for bazel), and python3.
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.
Furthermore, a special flag --//tensorflow_io/core:static_build
has to be passed to Bazel
in order to avoid duplication of symbols in statically linked libraries for file system
plugins.
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 make
# Install Bazelisk (manage bazel version implicitly)
curl -sSOL https://github.com/bazelbuild/bazelisk/releases/download/v1.11.0/bazelisk-linux-amd64
sudo mv bazelisk-linux-amd64 /usr/local/bin/bazel
sudo chmod +x /usr/local/bin/bazel
# 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'
# Add any optimization on bazel command, e.g., --compilation_mode=opt,
# --copt=-msse4.2, --remote_cache=, etc.
# export BAZEL_OPTIMIZATION=
# Build shared libraries, notice the passing of --//tensorflow_io/core:static_build
BAZEL_LINKOPTS="-static-libstdc++ -static-libgcc" BAZEL_LINKLIBS="-lm -l%:libstdc++.a" \
scl enable rh-python36 devtoolset-9 \
'bazel build -s --verbose_failures $BAZEL_OPTIMIZATION --//tensorflow_io/core:static_build //tensorflow_io/...'
# Once build is complete, shared libraries will be available in
# `bazel-bin/tensorflow_io/core`, `bazel-bin/tensorflow_io/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.py'
For Python development, a reference Dockerfile here can be
used to build the TensorFlow I/O package (tensorflow-io
) from source. Additionally, the
pre-built devel images can be used as well:
# Pull (if necessary) and start the devel container
$ docker run -it --rm --name tfio-dev --net=host -v ${PWD}:/v -w /v tfsigio/tfio:latest-devel bash
# Inside the docker container, ./configure.sh will install TensorFlow or use existing install
(tfio-dev) root@docker-desktop:/v$ ./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.
# Reference: https://www.tensorflow.orginstall/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/... //tensorflow_io_gcs_filesystem/...
# 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 built differently due to manylinux2010 requirements. Please check [Build Status and CI] section for more details on how the released whl packages are generated.
It is possible to build python wheels after bazel build is complete with the following command:
$ python 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 python -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 python -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 python
>>> import tensorflow_io as tfio
>>> ...
Some tests require launching a test container or start a local instance of the associated tool before running. For example, to run kafka related tests which will start a local instance of kafka, zookeeper and schema-registry, use:
# Start the local instances of kafka, zookeeper and schema-registry
$ bash -x -e tests/test_kafka/kafka_test.sh
# Run the tests
$ TFIO_DATAPATH=bazel-bin pytest -s -vv tests/test_kafka.py
Testing Datasets
associated with tools such as Elasticsearch
or MongoDB
require docker to be available on the system. In such scenarios, use:
# Start elasticsearch within docker container
$ bash tests/test_elasticsearch/elasticsearch_test.sh start
# Run the tests
$ TFIO_DATAPATH=bazel-bin pytest -s -vv tests/test_elasticsearch.py
# Stop and remove the container
$ bash tests/test_elasticsearch/elasticsearch_test.sh stop
Additionally, testing some features of tensorflow-io
doesn't require you to spin up
any additional tools as the data has been provided in the tests
directory itself.
For example, to run tests related to parquet
dataset's, use:
# Just run the test
$ TFIO_DATAPATH=bazel-bin pytest -s -vv tests/test_parquet.py
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)
})