Stride is an open-source library for medical ultrasound modelling and tomography. It lets users easily prototype medical imaging algorithms with only a few lines of Python code that can run seamlessly on a Jupyter notebook, a multi-node CPU cluster or a DGX station with production-grade performance. Stride provides end-to-end definition of the imaging process using state-of-the-art reconstruction algorithms, and the flexibility to (re)define every step of the optimisation.

• Documentation

The recommended way to install Stride is through Anaconda's package manager (version >=4.9), which can be downloaded in Anaconda or Miniconda. A Python version above 3.7 is recommended to run Stride.

To install Stride, follow these steps:

git clone [email protected]:trustimaging/stride.git
cd stride
conda env create -f environment.yml
conda activate stride
pip install -e .

The easiest way to start working with Stride is to open the Jupyter notebooks under examples/stride/breast2D or examples/stride/breast3D.

You can also execute the corresponding Python scrips from any terminal. To perform a forward run on the breast2D example:

cd examples/stride/breast2D
mrun python 03_script_foward.py

You can control the number of workers and threads per worker by running:

mrun -nw 2 -nth 5 python 03_script_foward.py

You can configure the devito solvers using environment variables. For example, to run the same code on a GPU with OpenACC you can:

export DEVITO_COMPILER=pgcc
export DEVITO_LANGUAGE=openacc
export DEVITO_PLATFORM=nvidiaX
mrun -nw 1 -nth 5 python 03_script_foward.py

Once you've run anastasio2D forward, you can run the corresponding inverse problem by doing:

mrun python 04_script_inverse.py

You can also open our interactive Jupyter notebooks in the public binder.

The documentation for Stride is available online here.

You can also build and access the documentation by running:

cd docs
make html

and opening the generated build/index.html in your browser.

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/21.2/nvhpc_2021_212_Linux_x86_64_cuda_multi.tar.gz
tar xpzf nvhpc_2021_212_Linux_x86_64_cuda_multi.tar.gz
cd nvhpc_2021_212_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_64/2021/compilers/bin/:$PATH
export LD_LIBRARY_PATH=/opt/nvidia/hpc_sdk/Linux_x86_64/2021/compilers/lib/:$LD_LIBRARY_PATH
export PATH=/opt/nvidia/hpc_sdk/Linux_x86_64/2021/comm_libs/mpi/bin/:$PATH
export LD_LIBRARY_PATH=/opt/nvidia/hpc_sdk/Linux_x86_64/2021/comm_libs/mpi/lib/:$LD_LIBRARY_PATH