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 https://github.com/trustimaging/stride.git cd stride conda env create -f environment.yml conda activate stride pip install -e .
You can also start using Stride through Docker:
git clone https://github.com/trustimaging/stride.git 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 https://127.0.0.1:8888/?token=XXX. To access the server, copy-paste the URL shown on the terminal into your browser to start a new Jupyter session.
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
The Devito library uses OpenACC to generate GPU code. The recommended way to access the necessary compilers is to install the NVIDIA HPC SDK.
wget https://developer.download.nvidia.com/hpc-sdk/22.11/nvhpc_2022_2211_Linux_x86_64_cuda_multi.tar.gz 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