Some initial documentation for the TfLite Micro CI system.

tensorflow/lite/micro/CONTRIBUTING.md

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-
    * [Contributing Guidelines](#contributing-guidelines)
       * [General Pull Request Guidelines](#general-pull-request-guidelines)
       * [Guidelines for Specific Contribution Categories](#guidelines-for-specific-contribution-categories)
@@ -18,8 +17,9 @@ https://github.com/ekalinin/github-markdown-toc#auto-insert-and-update-toc
       * [During the PR review](#during-the-pr-review)
       * [Reviewer notes](#reviewer-notes)
       * [Python notes](#python-notes)
+   * [Continuous Integration System](#continuous-integration-system)
 
-<!-- Added by: advaitjain, at: Mon 05 Oct 2020 02:38:02 PM PDT -->
+<!-- Added by: advaitjain, at: Tue 15 Dec 2020 03:06:29 PM PST -->
 
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@@ -305,3 +305,31 @@ that can be expanded and improved as necessary.
     ```
     yapf log_parser.py -i --style='{based_on_style: pep8, indent_width: 2}'
     ```
+# Continuous Integration System
+
+ * As a contributor, please make sure that the TfLite Micro build is green. You
+   can click on the details link to see what the errors are:
+
+   [![TfLite Micro Build](docs/images/tflm_continuous_integration_1.png)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/tflite-micro.html)
+
+
+ * Most of the tests that are run as part of the CI are with the
+ [micro/tools/ci_build/test_all.sh](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/tools/ci_build/test_all.sh) script.
+
+     * There are a few additional tests that use bazel that are not captured by the
+       `test_all.sh` script.
+
+ * If an error is not reproducible on your development machine, you can recreate
+   the docker container that is used on the CI servers with the following
+   commands (run from the root of the tensorflow github repo):
+
+   ```
+   mkdir /tmp/tflm-docker
+   docker build -f tensorflow/tools/ci_build/Dockerfile.micro -t tflm /tmp/tflm-docker
+   docker run -v `pwd`:/tensorflow -it tflm bash
+   ```
+
+   The `docker run` command is mounting the tensorflow repository on your
+   machine to the docker containter. As a result, any changes made within the
+   docker container will also be reflected in the directory in the host machine.
+