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[docs] Restructure README.md content (tensorflow#1257)
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* Refactor README.md content

* bump to run ci jobs
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@kvignesh1420 @i-ony
kvignesh1420 authored and i-ony committed Mar 15, 2021
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4 changes: 2 additions & 2 deletions .github/workflows/build.yml
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Expand Up @@ -76,7 +76,7 @@ jobs:
- run: |
set -x -e
bash -x -e .github/workflows/build.space.sh
python3 .github/workflows/build.instruction.py README.md "##### Ubuntu 20.04" > source.sh
python3 .github/workflows/build.instruction.py docs/development.md "##### Ubuntu 20.04" > source.sh
cat source.sh
docker run -i --rm -v $PWD:/v -w /v --net=host ubuntu:20.04 \
bash -x -e source.sh
Expand All @@ -89,7 +89,7 @@ jobs:
- run: |
set -x -e
bash -x -e .github/workflows/build.space.sh
python3 .github/workflows/build.instruction.py docs/development.md "##### Ubuntu 20.04" > source.sh
python3 .github/workflows/build.instruction.py docs/development.md "##### CentOS 7" > source.sh
cat source.sh
docker run -i --rm -v $PWD:/v -w /v --net=host \
-e BAZEL_OPTIMIZATION="${BAZEL_OPTIMIZATION}" \
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296 changes: 0 additions & 296 deletions README.md
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Expand Up @@ -138,302 +138,6 @@ of releases [here](https://github.com/tensorflow/io/releases).
| 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
```

### Notebooks/Tutorials
If you are updating or creating a notebook, please refer to the tutorials and instructions mentioned [here](https://github.com/tensorflow/io/tree/master/docs/tutorials).

### Python

#### macOS

## Performance Benchmarking

# Show macOS's default python3
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 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:
```sh
#!/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

The steps to build shared libraries for CentOS 8 is similiar to Ubuntu 20.04 above
excpet 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:
```sh
#!/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 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, 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 --//tensorflow_io/core:static_build //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](tools/docker/devel.Dockerfile) 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:
```sh
# 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/...


# 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.

#### Starting Test Containers

Some tests require launching a test container before running. In order
to run all tests, execute the following commands:

```sh
$ bash -x -e tests/test_ignite/start_ignite.sh
$ bash -x -e tests/test_kafka/kafka_test.sh
$ bash -x -e tests/test_kinesis/kinesis_test.sh
```

### R

We provide a reference Dockerfile [here](R-package/scripts/Dockerfile) for you
so that you can use the R package directly for testing. You can build it via:
```sh
$ 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](https://wiki.apache.org/hadoop/SequenceFile)
[string.seq](R-package/tests/testthat/testdata/string.seq), and then use any [transformation functions](https://tensorflow.rstudio.com/tools/tfdatasets/articles/introduction.html#transformations) provided by [tfdatasets package](https://tensorflow.rstudio.com/tools/tfdatasets/) on the dataset like the following:

```r
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
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