Jump right in using a Jupyter notebook directly in your browser, using binder.

The recommended way to install Stride is through Anaconda’s package manager (version >=4.9), which can be downloaded in:

A Python version above 3.8 is recommended to run Stride.

To install Stride, follow these steps:

git clone
cd stride
conda env create -f environment.yml
conda activate stride
pip install -e .

You can also start using Stride through Docker:

git clone
cd stride
docker-compose up stride

which will start a Jupyter server within the Docker container and display a URL on your terminal that looks something like To access the server, copy-paste the URL shown on the terminal into your browser to start a new Jupyter session.

Additional packages

To access the 3D visualisation capabilities, we also recommend installing MayaVi:

conda install -c conda-forge mayavi

and installing Jupyter notebook is recommended to access all the examples:

conda install -c conda-forge notebook

GPU support

The Devito library uses OpenACC to generate GPU code. The recommended way to access the necessary compilers is to install the NVIDIA HPC SDK.

tar xpzf nvhpc_2022_2211_Linux_x86_64_cuda_multi.tar.gz
cd nvhpc_2022_2211_Linux_x86_64_cuda_multi
sudo ./install

During the installation, select the single system install option.

Once the installation is done, you can add the following lines to your ~/.bashrc:

export PATH=/opt/nvidia/hpc_sdk/Linux_x86_64/22.11/compilers/bin/:$PATH
export LD_LIBRARY_PATH=/opt/nvidia/hpc_sdk/Linux_x86_64/22.11/compilers/lib/:$LD_LIBRARY_PATH
export PATH=/opt/nvidia/hpc_sdk/Linux_x86_64/22.11/comm_libs/mpi/bin/:$PATH
export LD_LIBRARY_PATH=/opt/nvidia/hpc_sdk/Linux_x86_64/22.11/comm_libs/mpi/lib/:$LD_LIBRARY_PATH