Setting up a development environment
The minimum requirements to build GDAL are:
CMake >= 3.10, and an associated build system (make, ninja, Visual Studio, etc.)
PROJ >= 6.0
Additional requirements to run the GDAL test suite are:
SWIG >= 4, for building bindings to other programming languages
Python >= 3.6
Python packages listed in autotest/requirements.txt
A number of optional libraries are also strongly recommended for most builds: SQLite3, expat, libcurl, zlib, libtiff, libgeotiff, libpng, libjpeg, etc. Consult Raster drivers and Vector drivers pages for information on dependencies of optional drivers.
Vagrant is a tool that works with a virtualization product such as VirtualBox to create a reproducible development environment. GDAL includes a Vagrant configuration file that sets up an Ubuntu virtual machine with a comprehensive set of dependencies.
Once Vagrant has been installed and the GDAL source downloaded, the virtual machine can be set up by running the following from the source root directory:
# VAGRANT_VM_CPU=number_of_cpus vagrant up
The source root directory is exposed inside the virtual machine at
/vagrant, so changes made to
GDAL source files on the host are seen inside the VM. To rebuild GDAL after changing source files,
you can connect to the VM and re-run the build command:
vagrant ssh cmake --build .
Note that the following directories on the host will be created (and can be removed if the Vagrant environment is no longer needed):
../apt-cache/ubuntu/jammy64: contains a cache of Ubuntu packages of the VM, to allow faster VM reconstruction
build_vagrant: CMake build directory
ccache_vagrant: CCache directory
The Linux environments used for building and testing GDAL on GitHub Actions are defined by Docker images that can be pulled to any machine for development. The Docker image used for each build is specified in linux_build.yml. As an example, the following commands can be run from the GDAL source root to build and test GDAL using the clang address sanitizer (ASAN) in the same environment that is used in GitHub Actions:
docker run -it \ -v $(pwd):/gdal:rw \ ghcr.io/osgeo/gdal-deps:ubuntu20.04-master cd /gdal mkdir build-asan cd build-asan ../.github/workflows/asan/build.sh ../.github/workflows/asan/test.sh
To avoid built objects being owned by root, it may be desirable to add
-u):$(id -g) -v /etc/passwd:/etc/passwd to the
docker run command above.
Building on Windows with Conda dependencies and Visual Studio
It is less appropriate for Debug builds of GDAL, than other methods, such as using vcpkg.
Install GDAL dependencies
Start a Conda enabled console and assuming there is a c:\dev directory
cd c:\dev conda create --name gdal conda activate gdal conda install --yes --quiet curl libiconv icu git python=3.7 swig numpy pytest zlib clcache conda install --yes --quiet -c conda-forge compilers conda install --yes --quiet -c conda-forge \ cmake proj geos hdf4 hdf5 \ libnetcdf openjpeg poppler libtiff libpng xerces-c expat libxml2 kealib json-c \ cfitsio freexl geotiff jpeg libpq libspatialite libwebp-base pcre postgresql \ sqlite tiledb zstd charls cryptopp cgal librttopo libkml openssl xz
compilers package will install
vs2017_win-64 (at time of writing)
to set the appropriate environment for cmake to pick up. It is also possible
to use the
vs2019_win-64 package if Visual Studio 2019 is to be used.
Checkout GDAL sources
cd c:\dev git clone https://github.com/OSGeo/gdal.git
From a Conda enabled console
conda activate gdal cd c:\dev\gdal cmake -S . -B build -DCMAKE_PREFIX_PATH:FILEPATH="%CONDA_PREFIX%" \ -DCMAKE_C_COMPILER_LAUNCHER=clcache -DCMAKE_CXX_COMPILER_LAUNCHER=clcache cmake --build build --config Release -j 8