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

* Refactor README.md content

* bump to run ci jobs

Author: kvignesh1420

.github/workflows/build.yml

@@ -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
@@ -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}" \

README.md

@@ -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