Installation Guide#
HTTomo is available on PyPI, so it can be installed into either a virtual environment or a conda environment.
However, there are certain constraints under which a virtual environment can be used, due to
the dependence on an MPI implementation, the hdf5 library, CUDA libraries, and whether the user
requires using tomopy
methods in pipelines.
Virtual environment#
A virtual environment can be used if the following conditions are met:
an MPI implementation is installed on the system (ie, OpenMPI)
the hdf5 library is installed on the system
CUDA libraries or CUDA toolkit are installed on the system
methods from
tomopy
are not required to be used in pipelines
$ python -m venv httomo
$ source httomo/bin/activate
$ MPICC=$(type -p mpicc) pip install mpi4py==3.1.6
$ pip install cython numpy pkgconfig setuptools # build dependencies of h5py
$ CC=$(type -p mpicc) HDF5_MPI="ON" HDF5_DIR=/path/to/parallel-hdf5 pip install --no-build-isolation --no-binary=h5py h5py
$ pip install cupy-cuda12x # install cupy-cuda11x if CUDA library/CUDA toolkit version is 11.x
$ pip install aiofiles astra-toolbox ccpi-regularisation-cupy click graypy hdf5plugin loguru nvtx pillow pyyaml scikit-image scipy tomobar tqdm
$ pip install --no-deps httomo httomolib httomolibgpu httomo-backends
Conda environment#
$ conda env create --name httomo
$ conda activate httomo
$ conda install -c conda-forge cupy==12.3.0 openmpi==4.1.6 h5py[build=*openmpi*]
$ conda install -c conda-forge tomopy==1.15 # optional
$ pip install httomo httomolib httomolibgpu
Setup HTTomo development environment:#
$ pip install -e .[dev] # development mode