development.md

Development

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.

IDE Setup

For instructions on how to configure Visual Studio Code for developing TensorFlow I/O, please refer to this doc.

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:

# 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

Python

macOS

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.

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 12.2
Build version 12B45b

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 20.04

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

# Alias python3 to python, needed by bazel
sudo ln -s /usr/bin/python3 /usr/bin/python

# 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

CentOS 8

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.

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.

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'

Docker

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.

Python Wheels

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
>>> ...

Testing

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

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)
})