# Reproducible Builds Using Nix¶

## Background¶

Before I was a scientist, I worked for UnixOps supporting research computing– mainly building software. The fact that the University of Colorado, Boulder campus needed almost a dozen system admins to build and rebuild software tells you something about how much of a hassle it is; builds are often cryptic, requirements conflict, and things constantly break between versions.

I’ve used various package managers over the years– homebrew when I had a mac, pip and conda for python, and various linux platform specific managers like pacman and apt-get. None of them have been particularly great; homebrew tends to be a mess and is specific to only OS X. Conda and pip work ok, but they only manage python packages, and reasonably complex software always includes non-python dependencies… linear algebra routines for numerics written in fortran or C, hdf5 libraries, and projection libraries like GDAL come to mind most quickly. Things have actually gotten worse in the last couple years with machine learning; builds now expect CUDA, and some frameworks are working on multi-gpu instances. Getting software up and running is a hassle, to say nothing of trying to have a colleague replicate your setup when you share code.

## Nix¶

For about the past two years I’ve been using nix to create reproducible, portable builds. It is by no means perfect– but it is better than anything else that I’ve tried by far. The major highlights:

• Builds don’t break. They may take sometime to figure out, but once they work, they keep working.

• Builds are portable. If it builds on my linux tablet, it will build on my HPC cluster. I can email a colleague a human readable text file, and they can replicate by working environment using a single command from the command line.

• It’s reasonably cross platform. Linux builds work perfectly between distros. MacOS tends to be compatible for most things…but some of their display stuff is strange enough that tweaks are occasionally needed.

• Declarations are functional. I specify what I want, not how to do it. Nix files are short, and tend to be understandable.

• Multiple builds are both possible, and encouraged. You can maintain libraries built with different options for different projects, even when those libraries conflict. Need hdf4 for a project, but don’t want to break hdf5? No problem, the dependency tree for each install has it’s own namespace.

Despite the above advantages, I maintain a love/hate relationship with nix. Nix is best described as a language, and the documentation is very much geared toward getting you to learn the language, instead of helping you to solve common tasks. Because the real world is messy (new versions that have bugs, old versions that don’t, or vice versa), getting certain things to work can be hacky, and unintuitive.

## A Reproducible CUDA & GPU environment¶

Recently, some of my work has been running Gaussian Process models for stochastic simulation, specifically on flight lines that have acquired lidar data over the Antarctic… which is to say that we have a lot of data, and run time for numeric Monte Carlo simulation is non-trivial. Gaussian Processes has two steps: fitting a model, and running the stochastic simulation. Both take about the same amount of runtime. Running prun in ipython shows that the vast majority of the runtime is dominated by just three function calls: cholesky, cho_solve, and multivariate_normal. CuPy does a great job at handling cholesky– and in fact, multivariate_normal can be rewritten to use cholesky, which is exactly what I’ve done in this PR. However, the last function, cho_solve is a hassle.

When I was working for the Intel Big Data ISTC back in 2013 and 2014, one of the projects that they funded was scalapack, headed up by Jack Dongarra, and he expanded (or rather, branched) that project to a new linear algebra system called magma. Magma is a set of solvers for heterogeneous compute environments, and will used both CPUs and GPUs, in some cases utilizing multiple GPUs. Ideally, a large matrix starts in system memory, and magma sends pieces of the problem to GPUs, so that solvers are using a hybrid approach.

My normal approach to computing with nix is to define a default.nix script in the working directory for my project, and then create the build environment using the nix-shell command within the directory; assuming nix is installed and in your path, it will check for default.nix and execute the build for you, setting all library and executable paths for you, along with whatever environmental variables that you need. Here’s what my default.nix files looks like to build magma (the long literal at the end breaks syntax highlighting):

# Note, nix-channel == 19.03
# magma derivation doesn't find mkl libs properly on 19.09
# ...although magma can be built on 19.09 using openblas

{
stable ? import (builtins.fetchTarball {
url = "https://github.com/NixOS/nixpkgs/archive/19.03.tar.gz";
# Hash obtained using nix-prefetch-url --unpack <url>
sha256 = "0q2m2qhyga9yq29yz90ywgjbn9hdahs7i8wwlq7b55rdbyiwa5dy";
}) {},
current ? import (builtins.fetchTarball {
url = "https://github.com/NixOS/nixpkgs/archive/19.09.tar.gz";
# Hash obtained using nix-prefetch-url --unpack <url>
sha256 = "0mhqhq21y5vrr1f30qd2bvydv4bbbslvyzclhw0kdxmkgg3z4c92";
}) {}
}:

let
mag = pkgs.callPackage ./magma.nix{};
in

with stable;

stdenv.mkDerivation rec {
name = "env" ;
env = buildEnv { name = name; paths = buildInputs; };
buildInputs = [ git hdf5 mkl mag cudatoolkit
(python37.buildEnv.override {
ignoreCollisions = true;
extraLibs = with python37Packages; [
(numpy.override { blas = mkl; })
pandas
#scikitlearn ## Needs custom build
pyproj
gdal
notebook
cython
matplotlib
pip
wheel
];
})
];

shellHook = ''
alias pip="PIP_PREFIX='$(pwd)/_build/pip_packages' \pip" export PYTHONPATH="$(pwd)/_build/pip_packages/lib/python3.7/site-packages:\
$PYTHONPATH" export CUDA_PATH=${pkgs.cudatoolkit}
export LD_LIBRARY_PATH="/run/opengl-driver:$CUDA_PATH/lib:\${pkgs.cudatoolkit.lib}/lib:${mag}/lib" export LDFLAGS="-L/lib -L$/run/opengl-driver"
export MAGMA=${mag} unset SOURCE_DATE_EPOCH''; }  Ok, so what’s going on here? If you’ve ever used nix-shell or nix in general, you’re probably used to the first line of default.nix looking something like this with import <nixpkgs> {};, which is fine…but less portable. Nix packages are pinned to a channel, which updates every 6 months, but this isn’t automatic. If you email someone a nix script with with import <nixpkgs> {};, you have no idea what their nix-channel is set to; are they on stable, unstable, or 18.03? Nix derivations that work for you once, will always work in the future– assuming that you execute them against the same channel. If you use a different channel, they might not build because of changes in the underlying channel, or they might have different behavior. Case in point: the above code builds great for nix-channel 19.03, but doesn’t build using Intel’s math kernel library (MKL) on channel 19.09 as a backend for the cpu portion of magma. Why? Who knows, maybe there’s something I could change in default.nix or magma.nix that would make it use MKL…but I can guarantee that whoever executes the above version will get my same build, so I can sidestep the question of if it will build with MKL in the future because I’m being explicit to use an older version. The first couple lines download a tar archive of what nix-channel looked like for the 2019 March branch, unpacks it, and uses those package definitions, regardless of what channel whoever receives it is on. The other branch ‘current’ isn’t actually needed, but is included to illustrate that you can mix and match packages between branches– if GDAL has a feature in 19.09 that you need (or a bug in 19.03 that you want to avoid), you can build everything using 19.03, and then specify per package when you want to use a newer or older branch. As an explicit example, if you wanted a different build of hdf5, all you would do is change line 27 (emphasized) from hdf5 to current.hdf5 ; python packages are nested one layer of abstraction in, so to change gdal, you’d probably move it out of the python package section to buildInputs and specify current.python36Packages.gdal. Moving on, with stable; sets the default to use stable instead of current. Grammar for nix expects most statements to terminate with a semicolon, although this isn’t the case for individual list items which are white space delimited per item. The block let mag = pkgs.callPackage ./magma.nix{}; in  Imports our own custom package declaration for magma; the above expects one local file to be present, magma.nix, which looks like this: { stdenv, fetchurl, cmake, gfortran, cudatoolkit, libpthreadstubs, openblas , mklSupport ? true, mkl ? null }: assert !mklSupport || mkl != null; with stdenv.lib; let version = "2.5.0"; in stdenv.mkDerivation { pname = "magma"; inherit version; src = fetchurl { url = "https://icl.cs.utk.edu/projectsfiles/magma/downloads/magma-${version}.tar.gz";
sha256 = "0czspk93cv1fy37zyrrc9k306q4yzfxkhy1y4lj937dx8rz5rm2g";
name = "magma-${version}.tar.gz"; }; buildInputs = [ gfortran cudatoolkit libpthreadstubs cmake ] ++ (if mklSupport then [ mkl ] else [ openblas ]); cmakeFlags = [ "-DBUILD_SHARED_LIBS=ON" "-DCMAKE_CXX_FLAGS=-fPIC" "-DCMAKE_C_FLAGS=-fPIC" ]; doCheck = false; MKLROOT = optionalString mklSupport mkl; preConfigure = '' export CC=${cudatoolkit.cc}/bin/gcc CXX=${cudatoolkit.cc}/bin/g++ ''; enableParallelBuilding=true; buildFlags = [ "magma" "magma_sparse" ]; # MAGMA's default CMake setup does not care about installation. So we copy files directly. installPhase = '' mkdir -p$out
mkdir -p $out/include mkdir -p$out/lib
mkdir -p $out/lib/pkgconfig cp -a ../include/*.h$out/include
#cp -a sparse-iter/include/*.h $out/include #cp -a lib/*.a$out/lib
cp -a lib/*.so $out/lib cat ../lib/pkgconfig/magma.pc.in | \ sed -e s:@INSTALL_PREFIX@:"$out":          | \
sed -e s:@CFLAGS@:"-I$out/include": | \ sed -e s:@LIBS@:"-L$out/lib -lmagma -lmagma_sparse": | \
sed -e s:@MAGMA_REQUIRED@::                       \
> $out/lib/pkgconfig/magma.pc ''; meta = with stdenv.lib; { description = "Matrix Algebra on GPU and Multicore Architectures"; license = licenses.bsd3; homepage = http://icl.cs.utk.edu/magma/index.html; platforms = platforms.unix; maintainers = with maintainers; [ tbenst ]; }; }  This file is actually nearly identical to this merged PR, but is slightly modified to build a dynamic library, and also to show how to build things outside of the nixpkgs tree–a useful skill to have when writing your own package derivations from scratch, or when modifying things to your liking. More specifically, you can’t modify anything in the nixpkgs tree that is built when you setup nix; any changes have to specified outside of the tree, either in the default.nix file, or in a local file that it calls. The magma.nix file differs from the github version in that it builds a dynamic library instead of a static one, and tells magma to use MKL instead of libatlas or openblas. Using MKL could actually be specified from the default.nix file substituting (magma.overrides { mklSupport = true;}) in place of mag on the emphasized line…but we would have to import the right nixpkgs branch (unstable), and that still wouldn’t build a shared library. Since the magma build file is being changed anyway, we can just change line 2 in magma.nix from mklSupport ? false to mklSupport ? true and avoid using the override within default.nix. ## Using the environment¶ The point of all this is to make an environment that works for development– the code that is getting hacked on is changing, and isn’t prepackaged or ready to be prepackaged. That’s why the default.nix has the python packages pip and wheel, and why we have this long block that sets environmental variables: shellHook = '' alias pip="PIP_PREFIX='$(pwd)/_build/pip_packages' \pip"
export PYTHONPATH="$(pwd)/_build/pip_packages/lib/python3.7/site-packages:\$PYTHONPATH"
export CUDA_PATH=${pkgs.cudatoolkit} export LD_LIBRARY_PATH="/run/opengl-driver:$CUDA_PATH/lib:\
${pkgs.cudatoolkit.lib}/lib:${mag}/lib"
export LDFLAGS="-L/lib -L$/run/opengl-driver" export MAGMA=${mag}
unset SOURCE_DATE_EPOCH'';


The first two lines tell pip where to build packages (in the current directory under _build), and python where to find them. The next three lines make the CUDA libraries visible to both pip, and the linker that builds the magma library. The CUDA setup is actually divided into a hardware driver, and the CUDA library itself; you can have multiple CUDA libraries, but only one driver. Sadly, the driver is not something that can be reproducibly built with nix, mainly because whatever system you load onto will already have a CUDA driver, and there can’t be multiple instances of these since they interface directly with the hardware. The solution that seems to have the least amount of overhead is to tell nix to expect the driver in “/run/opengl-driver”, and then to soft link wherever the system driver is to that directory. You could absolutely tell nix to build it’s own driver, but this doesn’t work for any multi user systems, so it’s less portable.

A previous version of this script appended the LD_LIBRARY_PATH to the LDFLAGS, but that only works if you have one path in the LD_LIBRARY_PATH, since the linker expects space delimited “-L/\$library_path” instead of the normal colon delimited shell variable. It’s actually extremely annoying that LDFLAGS are even needed, since installers should expect and search the LD_LIBRARY_PATH, but for some pip installs, they don’t pick up nvidia hardware driver. The last entry of LD_LIBRARY_PATH makes sure that when skcuda is run, it loads the magma library.

So what does this all get us? Well, first of all we can clone our branch of CuPy and use it as a regular import (since the PR hasn’t been merged yet). We can also install an editable version of sklearn and replace numpy with CuPy. And we can also install skcuda which has bindings for magma. The first two of those are simple git clone commands followed by pip local installs (sklearn needs to be told to use the installed numpy instead of building a sandbox version), while skcuda will pip install fine the normal way (there is no nix package for it, hence why it is pip installed after the build).

Once the stochastic simulation code is finished, we can roll the git and pip installs into their own nix file which is imported to default.nix. In the meantime, we can move the build from our development box to a HPC or cloud service with a minimum of work– just two files, one nix command, followed by two git pulls and three pip installs to access multi GPU computing from a jupyter notebook.