Since TorchGeo is written in pure-Python, the easiest way to install it is using pip:
$ pip install torchgeo
If you want to install a development version, you can use a VCS project URL:
$ pip install git+https://github.com/microsoft/torchgeo.git
or a local git checkout:
$ git clone https://github.com/microsoft/torchgeo.git $ cd torchgeo $ pip install .
By default, only required dependencies are installed. TorchGeo has a number of optional dependencies for specific datasets or development. These can be installed with a comma-separated list:
$ pip install torchgeo[datasets] $ pip install torchgeo[style,tests] $ pip install torchgeo[all]
pyproject.toml for a complete list of options. See the pip documentation for more details.
First, you’ll want to configure conda to only use the conda-forge channel:
$ conda config --add channels conda-forge $ conda config --set channel_priority strict
Now, you can install the latest stable release using:
$ conda install torchgeo
The installation of TorchGeo in this manner is not supported on Windows since PyTorch from the conda-forge channel currently does not support Windows. Users are recommended to use pip instead.
Conda does not support development versions or optional dependencies. If you install from conda-forge, only stable releases and required dependencies will be installed. Development versions or optional dependencies can be installed afterwards using pip.
See the conda-forge documentation for more details.
If you are working in an HPC environment or want to install your software from source, the easiest way is with spack:
$ spack install py-torchgeo $ spack load py-torchgeo
Our spack package has a
main version that can be used to install the latest commit:
$ spack install py-torchgeo@main $ spack load py-torchgeo
Optional dependencies can be installed by enabling build variants:
$ spack install py-torchgeo+datasets $ spack install py-torchgeo+style+tests
spack info py-torchgeo for a complete list of variants. See the spack documentation for more details.