third_party/llvm-7.0/llvm/docs/Docker.rst - SwiftShader - Git at Google

 =========================================
 A guide to Dockerfiles for building LLVM
 =========================================

 Introduction
 ============
 You can find a number of sources to build docker images with LLVM components in
 ``llvm/utils/docker``. They can be used by anyone who wants to build the docker
 images for their own use, or as a starting point for someone who wants to write
 their own Dockerfiles.

 We currently provide Dockerfiles with ``debian8`` and ``nvidia-cuda`` base images.
 We also provide an ``example`` image, which contains placeholders that one would need
 to fill out in order to produce Dockerfiles for a new docker image.

 Why?
 ----
 Docker images provide a way to produce binary distributions of
 software inside a controlled environment. Having Dockerfiles to builds docker images
 inside LLVM repo makes them much more discoverable than putting them into any other
 place.

 Docker basics
 -------------
 If you've never heard about Docker before, you might find this section helpful
 to get a very basic explanation of it.
 `Docker <https://www.docker.com/>`_ is a popular solution for running programs in
 an isolated and reproducible environment, especially to maintain releases for
 software deployed to large distributed fleets.
 It uses linux kernel namespaces and cgroups to provide a lightweight isolation
 inside currently running linux kernel.
 A single active instance of dockerized environment is called a *docker
 container*.
 A snapshot of a docker container filesystem is called a *docker image*.
 One can start a container from a prebuilt docker image.

 Docker images are built from a so-called *Dockerfile*, a source file written in
 a specialized language that defines instructions to be used when build
 the docker image (see `official
 documentation <https://docs.docker.com/engine/reference/builder/>`_ for more
 details). A minimal Dockerfile typically contains a base image and a number
 of RUN commands that have to be executed to build the image. When building a new
 image, docker will first download your base image, mount its filesystem as
 read-only and then add a writable overlay on top of it to keep track of all
 filesystem modifications, performed while building your image. When the build
 process is finished, a diff between your image's final filesystem state and the
 base image's filesystem is stored in the resulting image.

 Overview
 ========
 The ``llvm/utils/docker`` folder contains Dockerfiles and simple bash scripts to
 serve as a basis for anyone who wants to create their own Docker image with
 LLVM components, compiled from sources. The sources are checked out from the
 upstream svn repository when building the image.

 The resulting image contains only the requested LLVM components and a few extra
 packages to make the image minimally useful for C++ development, e.g. libstdc++
 and binutils.

 The interface to run the build is ``build_docker_image.sh`` script. It accepts a
 list of LLVM repositories to checkout and arguments for CMake invocation.

 If you want to write your own docker image, start with an ``example/`` subfolder.
 It provides an incomplete Dockerfile with (very few) FIXMEs explaining the steps
 you need to take in order to make your Dockerfiles functional.

 Usage
 =====
 The ``llvm/utils/build_docker_image.sh`` script provides a rather high degree of
 control on how to run the build. It allows you to specify the projects to
 checkout from svn and provide a list of CMake arguments to use during when
 building LLVM inside docker container.

 Here's a very simple example of getting a docker image with clang binary,
 compiled by the system compiler in the debian8 image:

 .. code-block:: bash

     ./llvm/utils/docker/build_docker_image.sh \
 	--source debian8 \
 	--docker-repository clang-debian8 --docker-tag "staging" \
 	-p clang -i install-clang -i install-clang-headers \
 	-- \
 	-DCMAKE_BUILD_TYPE=Release

 Note that a build like that doesn't use a 2-stage build process that
 you probably want for clang. Running a 2-stage build is a little more intricate,
 this command will do that:

 .. code-block:: bash

     # Run a 2-stage build.
     #   LLVM_TARGETS_TO_BUILD=Native is to reduce stage1 compile time.
     #   Options, starting with BOOTSTRAP_* are passed to stage2 cmake invocation.
     ./build_docker_image.sh \
 	--source debian8 \
 	--docker-repository clang-debian8 --docker-tag "staging" \
 	-p clang -i stage2-install-clang -i stage2-install-clang-headers \
 	-- \
 	-DLLVM_TARGETS_TO_BUILD=Native -DCMAKE_BUILD_TYPE=Release \
 	-DBOOTSTRAP_CMAKE_BUILD_TYPE=Release \
 	-DCLANG_ENABLE_BOOTSTRAP=ON -DCLANG_BOOTSTRAP_TARGETS="install-clang;install-clang-headers"

 This will produce a new image ``clang-debian8:staging`` from the latest
 upstream revision.
 After the image is built you can run bash inside a container based on your image
 like this:

 .. code-block:: bash

     docker run -ti clang-debian8:staging bash

 Now you can run bash commands as you normally would:

 .. code-block:: bash

     root@80f351b51825:/# clang -v
     clang version 5.0.0 (trunk 305064)
     Target: x86_64-unknown-linux-gnu
     Thread model: posix
     InstalledDir: /bin
     Found candidate GCC installation: /usr/lib/gcc/x86_64-linux-gnu/4.8
     Found candidate GCC installation: /usr/lib/gcc/x86_64-linux-gnu/4.8.4
     Found candidate GCC installation: /usr/lib/gcc/x86_64-linux-gnu/4.9
     Found candidate GCC installation: /usr/lib/gcc/x86_64-linux-gnu/4.9.2
     Selected GCC installation: /usr/lib/gcc/x86_64-linux-gnu/4.9
     Candidate multilib: .;@m64
     Selected multilib: .;@m64


 Which image should I choose?
 ============================
 We currently provide two images: debian8-based and nvidia-cuda-based. They
 differ in the base image that they use, i.e. they have a different set of
 preinstalled binaries. Debian8 is very minimal, nvidia-cuda is larger, but has
 preinstalled CUDA libraries and allows to access a GPU, installed on your
 machine.

 If you need a minimal linux distribution with only clang and libstdc++ included,
 you should try debian8-based image.

 If you want to use CUDA libraries and have access to a GPU on your machine,
 you should choose nvidia-cuda-based image and use `nvidia-docker
 <https://github.com/NVIDIA/nvidia-docker>`_ to run your docker containers. Note
 that you don't need nvidia-docker to build the images, but you need it in order
 to have an access to GPU from a docker container that is running the built
 image.

 If you have a different use-case, you could create your own image based on
 ``example/`` folder.

 Any docker image can be built and run using only the docker binary, i.e. you can
 run debian8 build on Fedora or any other Linux distribution. You don't need to
 install CMake, compilers or any other clang dependencies. It is all handled
 during the build process inside Docker's isolated environment.

 Stable build
 ============
 If you want a somewhat recent and somewhat stable build, use the
 ``branches/google/stable`` branch, i.e. the following command will produce a
 debian8-based image using the latest ``google/stable`` sources for you:

 .. code-block:: bash

     ./llvm/utils/docker/build_docker_image.sh \
 	-s debian8 --d clang-debian8 -t "staging" \
 	--branch branches/google/stable \
 	-p clang -i install-clang -i install-clang-headers \
 	-- \
 	-DCMAKE_BUILD_TYPE=Release


 Minimizing docker image size
 ============================
 Due to how Docker's filesystem works, all intermediate writes are persisted in
 the resulting image, even if they are removed in the following commands.
 To minimize the resulting image size we use `multi-stage Docker builds
 <https://docs.docker.com/develop/develop-images/multistage-build/>`_.
 Internally Docker builds two images. The first image does all the work: installs
 build dependencies, checks out LLVM source code, compiles LLVM, etc.
 The first image is only used during build and does not have a descriptive name,
 i.e. it is only accessible via the hash value after the build is finished.
 The second image is our resulting image. It contains only the built binaries
 and not any build dependencies. It is also accessible via a descriptive name
 (specified by -d and -t flags).
	=========================================
	A guide to Dockerfiles for building LLVM
	=========================================

	Introduction
	============
	You can find a number of sources to build docker images with LLVM components in
	``llvm/utils/docker``. They can be used by anyone who wants to build the docker
	images for their own use, or as a starting point for someone who wants to write
	their own Dockerfiles.

	We currently provide Dockerfiles with ``debian8`` and ``nvidia-cuda`` base images.
	We also provide an ``example`` image, which contains placeholders that one would need
	to fill out in order to produce Dockerfiles for a new docker image.

	Why?
	----
	Docker images provide a way to produce binary distributions of
	software inside a controlled environment. Having Dockerfiles to builds docker images
	inside LLVM repo makes them much more discoverable than putting them into any other
	place.

	Docker basics
	-------------
	If you've never heard about Docker before, you might find this section helpful
	to get a very basic explanation of it.
	`Docker <https://www.docker.com/>`_ is a popular solution for running programs in
	an isolated and reproducible environment, especially to maintain releases for
	software deployed to large distributed fleets.
	It uses linux kernel namespaces and cgroups to provide a lightweight isolation
	inside currently running linux kernel.
	A single active instance of dockerized environment is called a *docker
	container*.
	A snapshot of a docker container filesystem is called a docker image.
	One can start a container from a prebuilt docker image.

	Docker images are built from a so-called Dockerfile, a source file written in
	a specialized language that defines instructions to be used when build
	the docker image (see `official
	documentation <https://docs.docker.com/engine/reference/builder/>`_ for more
	details). A minimal Dockerfile typically contains a base image and a number
	of RUN commands that have to be executed to build the image. When building a new
	image, docker will first download your base image, mount its filesystem as
	read-only and then add a writable overlay on top of it to keep track of all
	filesystem modifications, performed while building your image. When the build
	process is finished, a diff between your image's final filesystem state and the
	base image's filesystem is stored in the resulting image.

	Overview
	========
	The ``llvm/utils/docker`` folder contains Dockerfiles and simple bash scripts to
	serve as a basis for anyone who wants to create their own Docker image with
	LLVM components, compiled from sources. The sources are checked out from the
	upstream svn repository when building the image.

	The resulting image contains only the requested LLVM components and a few extra
	packages to make the image minimally useful for C++ development, e.g. libstdc++
	and binutils.

	The interface to run the build is ``build_docker_image.sh`` script. It accepts a
	list of LLVM repositories to checkout and arguments for CMake invocation.

	If you want to write your own docker image, start with an ``example/`` subfolder.
	It provides an incomplete Dockerfile with (very few) FIXMEs explaining the steps
	you need to take in order to make your Dockerfiles functional.

	Usage
	=====
	The ``llvm/utils/build_docker_image.sh`` script provides a rather high degree of
	control on how to run the build. It allows you to specify the projects to
	checkout from svn and provide a list of CMake arguments to use during when
	building LLVM inside docker container.

	Here's a very simple example of getting a docker image with clang binary,
	compiled by the system compiler in the debian8 image:

	.. code-block:: bash

	./llvm/utils/docker/build_docker_image.sh \
	--source debian8 \
	--docker-repository clang-debian8 --docker-tag "staging" \
	-p clang -i install-clang -i install-clang-headers \
	-- \
	-DCMAKE_BUILD_TYPE=Release

	Note that a build like that doesn't use a 2-stage build process that
	you probably want for clang. Running a 2-stage build is a little more intricate,
	this command will do that:

	.. code-block:: bash

	# Run a 2-stage build.
	# LLVM_TARGETS_TO_BUILD=Native is to reduce stage1 compile time.
	# Options, starting with BOOTSTRAP_* are passed to stage2 cmake invocation.
	./build_docker_image.sh \
	--source debian8 \
	--docker-repository clang-debian8 --docker-tag "staging" \
	-p clang -i stage2-install-clang -i stage2-install-clang-headers \
	-- \
	-DLLVM_TARGETS_TO_BUILD=Native -DCMAKE_BUILD_TYPE=Release \
	-DBOOTSTRAP_CMAKE_BUILD_TYPE=Release \
	-DCLANG_ENABLE_BOOTSTRAP=ON -DCLANG_BOOTSTRAP_TARGETS="install-clang;install-clang-headers"

	This will produce a new image ``clang-debian8:staging`` from the latest
	upstream revision.
	After the image is built you can run bash inside a container based on your image
	like this:

	.. code-block:: bash

	docker run -ti clang-debian8:staging bash

	Now you can run bash commands as you normally would:

	.. code-block:: bash

	root@80f351b51825:/# clang -v
	clang version 5.0.0 (trunk 305064)
	Target: x86_64-unknown-linux-gnu
	Thread model: posix
	InstalledDir: /bin
	Found candidate GCC installation: /usr/lib/gcc/x86_64-linux-gnu/4.8
	Found candidate GCC installation: /usr/lib/gcc/x86_64-linux-gnu/4.8.4
	Found candidate GCC installation: /usr/lib/gcc/x86_64-linux-gnu/4.9
	Found candidate GCC installation: /usr/lib/gcc/x86_64-linux-gnu/4.9.2
	Selected GCC installation: /usr/lib/gcc/x86_64-linux-gnu/4.9
	Candidate multilib: .;@m64
	Selected multilib: .;@m64


	Which image should I choose?
	============================
	We currently provide two images: debian8-based and nvidia-cuda-based. They
	differ in the base image that they use, i.e. they have a different set of
	preinstalled binaries. Debian8 is very minimal, nvidia-cuda is larger, but has
	preinstalled CUDA libraries and allows to access a GPU, installed on your
	machine.

	If you need a minimal linux distribution with only clang and libstdc++ included,
	you should try debian8-based image.

	If you want to use CUDA libraries and have access to a GPU on your machine,
	you should choose nvidia-cuda-based image and use `nvidia-docker
	<https://github.com/NVIDIA/nvidia-docker>`_ to run your docker containers. Note
	that you don't need nvidia-docker to build the images, but you need it in order
	to have an access to GPU from a docker container that is running the built
	image.

	If you have a different use-case, you could create your own image based on
	``example/`` folder.

	Any docker image can be built and run using only the docker binary, i.e. you can
	run debian8 build on Fedora or any other Linux distribution. You don't need to
	install CMake, compilers or any other clang dependencies. It is all handled
	during the build process inside Docker's isolated environment.

	Stable build
	============
	If you want a somewhat recent and somewhat stable build, use the
	``branches/google/stable`` branch, i.e. the following command will produce a
	debian8-based image using the latest ``google/stable`` sources for you:

	.. code-block:: bash

	./llvm/utils/docker/build_docker_image.sh \
	-s debian8 --d clang-debian8 -t "staging" \
	--branch branches/google/stable \
	-p clang -i install-clang -i install-clang-headers \
	-- \
	-DCMAKE_BUILD_TYPE=Release


	Minimizing docker image size
	============================
	Due to how Docker's filesystem works, all intermediate writes are persisted in
	the resulting image, even if they are removed in the following commands.
	To minimize the resulting image size we use `multi-stage Docker builds
	<https://docs.docker.com/develop/develop-images/multistage-build/>`_.
	Internally Docker builds two images. The first image does all the work: installs
	build dependencies, checks out LLVM source code, compiles LLVM, etc.
	The first image is only used during build and does not have a descriptive name,
	i.e. it is only accessible via the hash value after the build is finished.
	The second image is our resulting image. It contains only the built binaries
	and not any build dependencies. It is also accessible via a descriptive name
	(specified by -d and -t flags).