Merge Dockerfiles directory and Build environments directories

CODEOWNERS

@@ -6,6 +6,7 @@
 /directory_template @angerson @perfinion
 /golang_install_guide @wamuir
 /manylinux_2014_docker_images @sub-mod
+/official_build_environments @angerson @perfinion
 /ppc64le @wdirons
 /tekton @perfinion
 /wsl2_gpu_guide @Avditvs

README.md

@@ -59,10 +59,8 @@ Want to add your own project to this list? It's easy: check out
 
 ### WIP / Other
 
-* [**(Experimental) Official Dockerfiles**](experimental_official_dockerfiles):
-  Rework of TensorFlow's Dockerfiles
-* [**(Experimental) Official Bazelrcs**](experimental_official_bazelrcs):
-  Standard configurations for TensorFlow builds
+* [**Official Build Environments**](official_build_environments):
+  (Experimental) Standard Dockerfiles and configurations for TensorFlow builds
 * [**Directory Template**](directory_template): Example short description.
 * [**Tekton CI**](tekton): perfinion's experimental directory for using Tekton 
   CI with TensorFlow

experimental_official_bazelrcs/README.md

-# [Experimental] Official BazelRCs
-
-Standard configurations for TensorFlow builds
-
-Maintainer: @angerson (TensorFlow, SIG Build)
-
-* * *
-
-WIP Bazelrcs for building and using TensorFlow and related packages.
-
-This directory is intended to contain concrete `bazelrc` files for use with
-TensorFlow's CI, and for users to replicate TensorFlow's CI.

experimental_official_dockerfiles/README.md → official_build_environments/README.md

-# [Experimental] Official Dockerfiles
+# Official Dockerfiles
 
-Rework of TensorFlow's Dockerfiles
+**Experimental.**
+
+Standard Dockerfiles and configurations for TensorFlow builds.
 
 Maintainer: @angerson (TensorFlow, SIG Build)
 
 * * *
 
-WIP Dockerfiles for building and using TensorFlow and related packages.
+This directory contains experimental work on standardizing TF's development
+environment for ease of maintenance and contribution.
+
+Each directory is for a TF branch, and its subdirectories are for files relating
+to each platform (Windows, MacOS, Linux). Only linux has progress right now.
 
 Nothing here is constant! If you see something that doesn't make sense or could
 be better, chances are it could be improved.
 
+## Dockerfiles
+
+Dockerfiles for building and using TensorFlow and related packages.
+
 The idea here is to create an easy-to-maintain Docker image that can build
 TensorFlow and related tools. Challenges include reducing image size (CUDA for
 instance is very big) and supporting manylinux2010 (which is why it uses
@@ -26,6 +36,7 @@ Initial versions of the `devel` images are available at
 You can build the images yourself like this:
 
 ```
+$ cd master/linux
 $ DOCKER_BUILDKIT=1 docker build --target=devel --build-arg PYTHON_VERSION=python3.8 -t tensorflow-build-devel .
 ```
 

experimental_official_dockerfiles/master/Dockerfile → official_build_environments/master/linux/Dockerfile

@@ -46,6 +46,8 @@ RUN /setup.python.sh $PYTHON_VERSION devel.requirements.txt
 
 # Setup build 
 COPY devel.user_tools /user_tools
+COPY ../../experimental_official_bazelrcs/master /bazelrcs
+RUN touch /bazelrcs/mounted.bazelrc
 
 ################################################################################
 FROM nvidia/cuda:11.2.1-cudnn8-runtime-ubuntu20.04 as runtime

experimental_official_bazelrcs/linux.cpu.bazelrc → official_build_environments/master/linux/devel.user_tools/cpu.bazelrc

 # This bazelrc can build a CPU-supporting TF package.
 # Hopefully it's compatible with manylinux2010.
 
+# Convenient cache configurations
+# Use a cache directory mounted to /tf/cache. Very useful!
+build:sigbuild_local_cache --disk_cache=/tf/cache
+# Use the public-access TF DevInfra cache (read only)
+build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache" --remote_upload_local_results=false
+# Write to the TF DevInfra cache (only works for internal TF CI)
+build:sigbuild_remote_cache_push --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache" --google_default_credentials
+
 # Use Python 3.X as installed in container image
 build --action_env PYTHON_BIN_PATH="/usr/bin/python3"
 build --action_env PYTHON_LIB_PATH="/usr/lib/tf_python"

experimental_official_bazelrcs/linux.gpu.bazelrc → official_build_environments/master/linux/devel.user_tools/gpu.bazelrc

 # This bazelrc can build a GPU-supporting TF package.
 # Hopefully it's compatible with manylinux2010.
 
+# Convenient cache configurations
+# Use a cache directory mounted to /tf/cache. Very useful!
+build:sigbuild_local_cache --disk_cache=/tf/cache
+# Use the public-access TF DevInfra cache (read only)
+build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache" --remote_upload_local_results=false
+# Write to the TF DevInfra cache (only works for internal TF CI)
+build:sigbuild_remote_cache_push --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache" --google_default_credentials
+
 # Use Python 3.X as installed in container image
 build --action_env PYTHON_BIN_PATH="/usr/bin/python3"
 build --action_env PYTHON_LIB_PATH="/usr/lib/tf_python"

official_build_environments/master/linux/devel.user_tools/mounted.bazelrc

+# You can mount your own bazelrc over this file in Docker with
+# -v $HOME/my/bazelrc:/user_tools/mounted.bazelrc