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CCPi Regularisation Toolkit

Installation

In order to compile C/C++ sources and additional wrappers from source code for numpy 1.24 and python 3.10, the recommended way is:

git clone https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit
cd CCPi-Regularisation-Toolkit
export CCPI_BUILD_ARGS="--numpy 1.24 --python 3.10"
build/jenkins-build.sh

this will install conda build environment and compiles C/C++ and Python wrappers and performs basic tests for environment with python 3.10 and numpy 1.24.

CMake

If you want to build directly using cmake, install CMake (v.>=3) to configure it. Additionally you will need a C compiler, make (on linux) and CUDA SDK where available. The toolkit may be used directly from C/C++ as it is compiled as a shared library (check-out the include files in Core for this)

  1. Clone this repository to a directory, i.e. CCPi-Regularisation-Toolkit,
  2. create a build directory.
  3. Issue cmake to configure (or cmake-gui, or ccmake, or cmake3). Use additional flags to fine tune the configuration.

Flags used during configuration

CMake flag type meaning
BUILD_PYTHON_WRAPPER bool ON|OFF whether to build the Python wrapper
BUILD_MATLAB_WRAPPER bool ON|OFF whether to build the Matlab wrapper
CMAKE_INSTALL_PREFIX path your favourite install directory
MATLAB_DEST_DIR path Matlab modules install directory (default ${CMAKE_INSTALL_PREFIX}/matlab)
BUILD_CUDA bool ON|OFF whether to build the CUDA regularisers
Matlab_ROOT_DIR path Matlab directory
PYTHON_EXECUTABLE path /path/to/python/executable

Here an example of build on Linux (see also run.sh for additional info):

git clone https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit
cd CCPi-Regularisation-Toolkit
cmake -S . -B ./build_proj -DBUILD_MATLAB_WRAPPER=ON -DBUILD_PYTHON_WRAPPER=ON -DBUILD_CUDA=ON -DCMAKE_INSTALL_PREFIX=./install
cmake --build ./build_proj --target install
pip install ./src/Python

Python

Python binaries

Python binaries are distributed via the ccpi conda channel.

conda install ccpi-regulariser -c ccpi -c conda-forge

Python (conda-build)

conda build recipe/ --numpy 1.23 --python 3.10
conda install ccpi-regulariser --use-local --force-reinstall # doesn't work?
conda install -c file://${CONDA_PREFIX}/conda-bld/ ccpi-regulariser --force-reinstall # try this one
cd demos/
python demo_cpu_regularisers.py # to run CPU demo
python demo_gpu_regularisers.py # to run GPU demo

Python (GPU-CuPy)

One can also use some of the GPU modules directly (i.e. without the need of building the package) by using CuPy implementations.

pip install git+https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit

Warning

The above currently doesn't work; see Python build below instead:

Python build

If passed BUILD_PYTHON_WRAPPER=ON, CMake will install libraries in-place under ./src/Python, followed by running python -m pip install ./src/Python. (Note that libraries are also installed under the user-specified ${CMAKE_INSTALL_PREFIX}, which could be set to a throwaway directory, e.g. -DCMAKE_INSTALL_PREFIX=./install).

If Python is not found by CMake you can provide the additional flag to CMake -DPYTHON_EXECUTABLE=/path/to/executable/python.

Tests can also be run in-place after the build:

PYTHONPATH=./src/Python python -m unittest discover ./test

MultiGPU capability (to use in Python with mpi4py)

The toolkit can be used by running in parallel across multiple GPU devices on a PC or a compute node of a cluster. In order to initiate a parallel run on your GPUs you will need an MPI library, such as, mpi4py. See multi_gpu demo script in demos folder, it can be run as

mpirun -np 2 python multi_gpu.py -g -s -gpus 2

where -np parameter defines the total number of processes and -gpus defines the number of available GPUs.

Matlab

Matlab wrapper will install in the ${MATLAB_DEST_DIR} directory, which defaults to ${CMAKE_INSTALL_PREFIX}/matlab

If Matlab is not picked by CMake, you could add -DMatlab_ROOT_DIR=<Matlab directory>.

Linux

Because you've installed the modules in <your favourite install directory> you need to instruct Matlab to look in those directories:

PATH="/path/to/mex/:$PATH" LD_LIBRARY_PATH="/path/to/library:$LD_LIBRARY_PATH" matlab

By default /path/to/mex is ${CMAKE_INSTALL_PREFIX}/bin and /path/to/library/ is ${CMAKE_INSTALL_PREFIX}/lib

Windows

On Windows the dll and the mex modules must reside in the same directory. It is sufficient to add the directory at the beginning of the m-file.

addpath(/path/to/library);

Legacy Matlab installation (partly supported, please use Cmake)

cd src/Matlab/mex_compile
compileCPU_mex.m % to compile CPU modules
compileGPU_mex.m % to compile GPU modules (see instructions in the file)