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A guide to Dockerfiles for building LLVM
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.
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
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 <>`_ 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
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 <>`_ 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.
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.
Inside each subfolder we host Dockerfiles for two images:
- ``build/`` image is used to compile LLVM, it installs a system compiler and all
build dependencies of LLVM. After the build process is finished, the build
image will have an archive with compiled components at ``/tmp/clang.tar.gz``.
- ``release/`` image usually only contains LLVM components, compiled by the
``build/`` image, and also libstdc++ and binutils to make image minimally
useful for C++ development. The assumption is that you usually want clang to
be one of the provided components.
To build both of those images, use ```` script.
It will checkout LLVM sources and build clang in the ``build`` container, copy results
of the build to the local filesystem and then build the ``release`` container using
those. The ```` 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 incomplete Dockerfiles with (very few) FIXMEs explaining the steps
you need to take in order to make your Dockerfiles functional.
The ``llvm/utils/`` 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/ \
--source debian8 \
--docker-repository clang-debian8 --docker-tag "staging" \
-p clang -i install-clang -i install-clang-headers \
-- \
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.
./ \
--source debian8 \
--docker-repository clang-debian8 --docker-tag "staging" \
-p clang -i stage2-install-clang -i stage2-install-clang-headers \
-- \
-DCLANG_ENABLE_BOOTSTRAP=ON -DCLANG_BOOTSTRAP_TARGETS="install-clang;install-clang-headers"
This will produce two images, a release image ``clang-debian8:staging`` and a
build image ``clang-debian8-build: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
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
<>`_ 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
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/ \
-s debian8 --d clang-debian8 -t "staging" \
--branch branches/google/stable \
-p clang -i install-clang -i install-clang-headers \
-- \
Minimizing docker image size
Due to Docker restrictions we use two images (i.e., build and release folders)
for the release image to be as small as possible. It's much easier to achieve
that using two images, because Docker would store a filesystem layer for each
command in the Dockerfile, i.e. if you install some packages in one command,
then remove those in a separate command, the size of the resulting image will
still be proportinal to the size of an image with installed packages.
Therefore, we strive to provide a very simple release image which only copies
compiled clang and does not do anything else.
Docker 1.13 added a ``--squash`` flag that allows to flatten the layers of the
image, i.e. remove the parts that were actually deleted. That is an easier way
to produce the smallest images possible by using just a single image. We do not
use it because as of today the flag is in experimental stage and not everyone
may have the latest docker version available. When the flag is out of
experimental stage, we should investigate replacing two images approach with
just a single image, built using ``--squash`` flag.