In the root library folder execute:
$ mkdir build
$ cd build
$ cmake ..
$ make check # (optional, runs unit tests)
$ make install
-
GTSAM requires the following libraries to be installed on your system:
-
BOOST version 1.65 or greater (install through Linux repositories or MacPorts). Please see Boost Notes for version recommendations based on your compiler.
-
Cmake version 3.0 or higher
-
Support for XCode 4.3 command line tools on Mac requires CMake 2.8.8 or higher
Optional dependent libraries:
- If TBB is installed and detectable by CMake GTSAM will use it automatically.
Ensure that CMake prints "Use Intel TBB : Yes". To disable the use of TBB,
disable the CMake flag
GTSAM_WITH_TBB
(enabled by default) by providing the argument-DGTSAM_WITH_TBB=OFF
tocmake
. On Ubuntu, TBB may be installed from the Ubuntu repositories, and for other platforms it may be downloaded from https://www.threadingbuildingblocks.org/ - GTSAM may be configured to use MKL by toggling
GTSAM_WITH_EIGEN_MKL
andGTSAM_WITH_EIGEN_MKL_OPENMP
toON
; however, best performance is usually achieved with MKL disabled. We therefore advise you to benchmark your problem before using MKL.
Tested compilers:
- GCC 4.2-7.3
- OS X Clang 2.9-10.0
- OS X GCC 4.2
- MSVC 2010, 2012, 2017
Tested systems:
- Ubuntu 16.04 - 18.04
- MacOS 10.6 - 10.14
- Windows 7, 8, 8.1, 10
-
-
GTSAM makes extensive use of debug assertions, and we highly recommend you work in Debug mode while developing (enabled by default). Likewise, it is imperative that you switch to release mode when running finished code and for timing. GTSAM will run up to 10x faster in Release mode! See the end of this document for additional debugging tips.
-
GTSAM has Doxygen documentation. To generate, run 'make doc' from your build directory after setting the
GTSAM_BUILD_DOCS
andGTSAM_BUILD_[HTML|LATEX]
cmake flags. -
The instructions below install the library to the default system install path and build all components. From a terminal, starting in the root library folder, execute commands as follows for an out-of-source build:
$ mkdir build
$ cd build
$ cmake ..
$ make check (optional, runs unit tests)
$ make install
This will build the library and unit tests, run all of the unit tests, and then install the library itself.
Versions of Boost prior to 1.65 have a known bug that prevents proper "deep" serialization of objects, which means that objects encapsulated inside other objects don't get serialized.
This is particularly seen when using clang
as the C++ compiler.
For this reason we recommend Boost>=1.65, and recommend installing it through alternative channels when it is not available through your operating system's primary package manager.
- MSVC 2013 is not yet supported because it cannot build the serialization module of Boost 1.55 (or earlier).
This section details how to build a GTSAM .sln
file using Visual Studio.
- Visual Studio with C++ CMake tools for Windows
- CMake >= 3.21 is required for Visual Studio installation because custom templates used in the build were added in 3.21. Use
cmake --version
in the VS Developer Command Prompt to ensure you meet this requirement.
- CMake >= 3.21 is required for Visual Studio installation because custom templates used in the build were added in 3.21. Use
- All the other pre-requisites listed above.
- Open Visual Studio.
- Select
Open a local folder
and select the GTSAM source directory. - Go to
Project -> CMake Settings
.
- (Optional) Set
Configuration name
. - (Optional) Set
Configuration type
. - Set the
Toolset
tomsvc_x64_x64
. If you know what toolset you require, then skip this step. - Update the
Build root
to${projectDir}\build\${name}
. - You can optionally create a new configuration for a
Release
build. - Set the necessary CMake variables for your use case. If you are not using Boost, uncheck
GTSAM_ENABLE_BOOST_SERIALIZATION
andGTSAM_USE_BOOST_FEATURES
. - Click on
Show advanced settings
. - For
CMake generator
, select a version which matchesVisual Studio <Version> <Year> Win64
, e.g.Visual Studio 16 2019 Win64
. - Save the settings (Ctrl + S).
- Saving the CMake settings should automatically generate the cache. Otherwise, click on
Project -> Generate Cache
. This will generate the CMake build files (as seen in the Output window).
- If generating the cache yields
CMake Error ... (ADD_CUSTOM_COMMAND)
errors, you have an old CMake. Verify that your CMake is >= 3.21. If Visual Studio says that it is but you're still getting the error, install the latest CMake (tested with 3.31.4) and point to its executable inCMakeSettings > Advanced settings > CMake executable
.
- The last step will generate a
GTSAM.sln
file in thebuild
directory. At this point, GTSAM can be used as a regular Visual Studio project.
To install the Python bindings on Windows:
Install pyparsing(>=2.4.2), pybind-stubgen>=2.5.1 and numpy(>=1.11.0) with the Python environment you wish to develop in. These can all be installed as follows:
pip install -r <gtsam_folder>/python/dev_requirements.txt
- Follow the above steps for GTSAM general installation.
- In the CMake settings variables, set
GTSAM_BUILD_PYTHON
to be true and specify the path to your desired Python environment executable in the "CMake command arguments" field using-DPYTHON_EXECUTABLE="<path to your python.exe>"
- Once the cache is generated, ensure it's using your desired Python executable and the version is correct. It may cause errors later otherwise, such as missing Python packages required to build. If the version is not the one you specified in
DPYTHON_EXECUTABLE
, change the version in all 3 Python version specifiers in the CMake variables (GTSAM_PYTHON_VERSION
,PYBIND11_PYTHON_VERSION
,WRAP_PYTHON_VERSION
) to the exact version of your desired executable. CMake might look for executables in the standard locations first, so ensure it's using the one you want before continuing.
- Build the project (Build > Build All).
- If you encounter the error
C1083 Cannot open include file: 'boost/serialization/export.hpp': No such file or directory
, you need to make changes to the template files that include Boost since your build is not using Boost. Locatepython/gtsam/gtsam.tpl
(andpython/gtsam_unstable/gtsam_unstable.tpl
if you are building unstable) and comment out the line#include <boost/serialization/export.hpp>
near the top of each. Delete the generated build directory (e.g. if you madebuild/x64-Debug
, delete thex64-Debug
folder), regenerate the cache, and build again. - If you encounter an error involving copying
.pyd
files, find the files mentioned (gtsam_py.pyd
andgtsam_unstable_py.pyd
, probably in theDebug
/Release
/etc. folder insidebuild/<your build>/python/gtsam
) and copy them to where they are supposed to be (the source of the copy error, probablybuild/<your build>/python/gtsam
) then rebuild.
- At this point,
gtsam
inbuild/<your build>/python
is available to be used as a Python package. You can usepip install .
in that directory to install the package.
GTSAM has a number of options that can be configured, which is best done with one of the following:
- ccmake the curses GUI for cmake
- cmake-gui a real GUI for cmake
We support several build configurations for GTSAM (case insensitive)
cmake -DCMAKE_BUILD_TYPE=[Option] ..
- Debug (default) All error checking options on, no optimization. Use for development.
- Release Optimizations turned on, no debug symbols.
- Timing Adds ENABLE_TIMING flag to provide statistics on operation
- Profiling Standard configuration for use during profiling
- RelWithDebInfo Same as Release, but with the -g flag for debug symbols
The install folder. The default is typically /usr/local/
.
To configure to install to your home directory, you could execute:
cmake -DCMAKE_INSTALL_PREFIX:PATH=$HOME ..
The Matlab toolbox will be installed in a subdirectory of this folder, called 'gtsam'.
cmake -DGTSAM_TOOLBOX_INSTALL_PATH:PATH=$HOME/toolbox ..
This is a build option to allow for tests in subfolders to be linked against convenience libraries rather than the full libgtsam. Set with the command line as follows:
cmake -DGTSAM_BUILD_CONVENIENCE_LIBRARIES:OPTION=ON ..
- ON (Default): This builds convenience libraries and links tests against them. This option is suggested for gtsam developers, as it is possible to build and run tests without first building the rest of the library, and speeds up compilation for a single test. The downside of this option is that it will build the entire library again to build the full libgtsam library, so build/install will be slower.
- OFF: This will build all of libgtsam before any of the tests, and then link all of the tests at once. This option is best for users of GTSAM, as it avoids rebuilding the entirety of gtsam an extra time.
Enable build and install for libgtsam_unstable library. Set with the command line as follows:
cmake -DGTSAM_BUILD_UNSTABLE:OPTION=ON ..
ON: When enabled, libgtsam_unstable will be built and installed with the same options as libgtsam. In addition, if tests are enabled, the unit tests will be built as well. The Matlab toolbox will also be generated if the matlab toolbox is enabled, installing into a folder called gtsam_unstable
.
OFF (Default) If disabled, no gtsam_unstable
code will be included in build or install.
Whether or not to force building examples, can be true or false.
Whether or not to build tests, can be true or false.
make check
will build and run all of the tests. Note that the tests will only be
built when using the "check" targets, to prevent make install
from building the tests
unnecessarily. You can also run make timing
to build all of the timing scripts.
To run check on a particular module only, run make check.[subfolder]
, so to run
just the geometry tests, run make check.geometry
. Individual tests can be run by
appending .run
to the name of the test, for example, to run testMatrix, run
make testMatrix.run
.
MEX_COMMAND: Path to the mex compiler. Defaults to assume the path is included in your shell's PATH environment variable. mex is installed with matlab at $MATLABROOT/bin/mex
$MATLABROOT can be found by executing the command matlabroot
in MATLAB
Here are some tips to get the best possible performance out of GTSAM.
- Build in
Release
mode. GTSAM will run up to 10x faster compared toDebug
mode. - Enable TBB. On modern processors with multiple cores, this can easily speed up optimization by 30-50%. Please note that this may not be true for very small problems where the overhead of dispatching work to multiple threads outweighs the benefit. We recommend that you benchmark your problem with/without TBB.
- Use
GTSAM_BUILD_WITH_MARCH_NATIVE
. A performance gain of 25-30% can be expected on modern processors. Note that this affects the portability of your executable. It may not run when copied to another system with older/different processor architecture. Also note that all dependent projects must be compiled with the same flag, or seg-faults and other undefined behavior may result. - Possibly enable MKL. Please note that our benchmarks have shown that this helps only in very limited cases, and actually hurts performance in the usual case. We therefore recommend that you do not enable MKL, unless you have benchmarked it on your problem and have verified that it improves performance.
Another useful debugging symbol is _GLIBCXX_DEBUG, which enables debug checks and safe containers in the standard C++ library and makes problems much easier to find.
NOTE: The native Snow Leopard g++ compiler/library contains a bug that makes it impossible to use _GLIBCXX_DEBUG. MacPorts g++ compilers do work with it though.
NOTE: If _GLIBCXX_DEBUG is used to compile gtsam, anything that links against gtsam will need to be compiled with _GLIBCXX_DEBUG as well, due to the use of header-only Eigen.
Intel has a guide for installing MKL on Linux through APT repositories at https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo.
After following the instructions, add the following to your ~/.bashrc
(and afterwards, open a new terminal before compiling GTSAM):
LD_PRELOAD
need only be set if you are building the python wrapper to use GTSAM from python.
source /opt/intel/mkl/bin/mklvars.sh intel64
export LD_PRELOAD="$LD_PRELOAD:/opt/intel/mkl/lib/intel64/libmkl_core.so:/opt/intel/mkl/lib/intel64/libmkl_sequential.so"
To use MKL in GTSAM pass the flag -DGTSAM_WITH_EIGEN_MKL=ON
to cmake.
The LD_PRELOAD
fix seems to be related to a well known problem with MKL which leads to lots of undefined symbol errors, for example:
- https://software.intel.com/en-us/forums/intel-math-kernel-library/topic/300857
- https://software.intel.com/en-us/forums/intel-distribution-for-python/topic/628976
- https://groups.google.com/a/continuum.io/forum/#!topic/anaconda/J3YGoef64z8
Failing to specify LD_PRELOAD
may lead to errors such as:
ImportError: /opt/intel/mkl/lib/intel64/libmkl_vml_avx2.so: undefined symbol: mkl_serv_getenv
or
Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so.
when importing GTSAM using the python wrapper.