Installation and Deployment

Standard Installation

SModelS is a Python library that requires Python version 2.6 or later, including version 3, which is the default. It depends on the following external Python libraries:

  • unum>=4.0.0
  • numpy>=1.13.0
  • argparse
  • requests>=2.0.0
  • docutils>=0.3
  • scipy>=1.0.0
  • pyslha>=3.1.0

In addition, the cross section computer provided by requires:

  • Pythia 8.2 (requires a C++ compiler) or Pythia 6.4.27 (requires gfortran)
  • NLL-fast 1.2 (7 TeV), 2.1 (8 TeV), and 3.1 (13 TeV) (requires a fortran compiler)

These tools need not be installed separately, as the SModelS build system takes care of that. The current default is that both Pythia6 and Pythia8 are installed together with NLLfast. However, the user can easily adapt the Makefile in the lib/ directory to fit his or her needs. Finally, the database browser provided by requires IPython.

If pip is installed in your machine, installing SModelS should be as easy as:

pip install smodels

In this case, gfortran and g++ need to be installed separately, if one wishes to compute cross sections with pythia6 and pythia8, respectively. Also, it might be necessary to perform:

sudo fixpermissions

in case of system-wide installs. User-specific installations on the other hand:

pip install --user smodels

will install smodels into the user’s ~/.local directory. Depending on your platform, the environment variables $PATH, $PYTHONPATH, $LD_LIBRARY_PATH (or $DYLD_LIBRARY_PATH) might have to be set appropriately.

If Python’s setuptools is installed in your machine, SModelS and its dependencies can be installed with: install

If the python libraries are installed in a system folder (as is the default behavior), it will be necessary to run the install command with superuser privilege. Alternatively, one can run with the “–user” flag: install --user

If setuptools is not installed, you can try to install the external libraries manually and then rerun For Ubuntu, SL6 machines and other platforms, a recipe is given below.

There is also a diagnostic tool available: toolbox

should list and check all internal tools (Pythia and NLL-fast) and external (numpy, scipy, unum, … ) dependencies.

In case everything fails, please contact

Installation on Ubuntu >= 16.04

Installation on Ubuntu machines should be straightforward with superuser privileges (if you do not have superuser privileges see instructions below):

  • sudo apt install gfortran python-setuptools python-scipy python-numpy python-docutils python-argparse
  • install

Note that the last command can be run as superuser, or with the “–user” flag.

Installation on SL7

Installation on an SL7 or CentOS7 is straightforward:

  • yum install gcc-c++ scipy numpy
  • pip install unum pyslha argparse

Installation on SL6

Installation on an SL6 (Scientific Linux 6 or Scientific Linux CERN 6) machine is tricky, because SModelS requires a more recent version of scipy than is provided by SL6. We succeeded to install SModelS on SL6 by doing:

  • yum install gcc-c++ libstdc++-devel libevent-devel python-devel lapack lapack-devel blas blas-devel libgfortran python-distutils-extra

followed by:

  • pip install nose unum argparse numpy pyslha scipy

Note, that these steps can safely be done within a Python virtualenv. Pip can also be called with the “–user” flag.

Installation on SL5 and similar distributions

In some distributions like SL5, the Python default version may be smaller than 2.6. In these cases, virtualenv has to be set up for a Python version >= 2.6. E.g. for Python 2.6, do virtualenv --python=python2.6 <envname>, and modify by hand the first line in the executable from #!/usr/bin/env python3 to #!/usr/bin/env python2.6. Then perform the steps listed under Installation on SL6.

Installation on other platforms or without superuser privileges using Anaconda

Another easy and platform independent way of installing SModelS without superuser priviledges is via Anaconda ( Anaconda provides a local installation of pip as well as several additional python packages. Here we assume a version of gfortran is already installed in your system.

  • download and install Anaconda for Python 3.6 (

  • make sure Anaconda’s bin and lib folders are added to your system and Python paths


and then install SModelS as a user: install --user

In order to make sure all libraries have been correctly installed, you can run: toolBox

Installation of the C++ interface

SModelS v1.1.1 comes with a simple C++ interface, see the cpp directory. Obviously, a C++ compiler is need, alongside with the python developers (header) files (libpython-dev on ubuntu, python-devel on rpm-based distros).

Adding results to the database

The installation procedure explained above also installs SModelS’ database of experimental results in the smodels-database subdirectory. The complete list of analyses and results included in the database can be consulted at We note that all the results in the official database release have been carefully validated and the validation material can be found at

The database can conveniently be updated independently from SModelS code updates. It suffices to unpack any new database tarball and replace the database directory. In the same fashion, one can easily add additional results as explained below.

Adding FastLim data

The official SModelS database can be augmented with data from the fastlim database. A tarball with the properly converted fastlim-1.0 efficiency maps can be found in our download section at The tarball then needs to be exploded in the top level directory of the database.

That is, the following steps need to be performed

mv smodels-v1.1-fastlim-1.0.tgz <smodels-database folder>
cd <smodels-database folder>
tar -xzvf smodels-v1.1-fastlim-1.0.tgz
rm smodels-v1.1-fastlim-1.0.tgz

Efficiencies with a relative statistical uncertainty greater than 25% we consider to be zero. Also, per default we discard zeroes-only results. Once the fastlim folders have been added to the database, SModelS auto-detects fastlim results and issues an acknowledgement. When using these results, please properly cite the fastlim paper; for convenience, a bibtex file is provided in the smodels-fastlim tarball.

Adding one’s own results

The Database of Experimental Results is organized as files in an ordinary directory hierarchy. Therefore, adding additional experimental results is a matter of copying and editing text files. Once the new folders and files have been added following the database structure format, SModelS automatically rebuilds the binary (Pickle) database file. The added results will then be available for using with the the SModelS tools.