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-cuda13x # install cupy-cuda13x if CUDA library/CUDA toolkit version is 13.x
$ pip install aiofiles astra-toolbox click graypy hdf5plugin loguru nvtx pillow pyyaml scikit-image scipy tomobar tqdm
$ pip install --no-deps httomo httomolib httomolibgpu httomo-backends

Conda environment

By default the cupy installation will install the latest cuda-cudart. This can result in CUDA versions higher than the supported by the GPU device of the system. One can specify the compatible to their system CUDA package, e.g., cuda-cudart==12.9.79.

$ conda create --name httomo
$ conda activate httomo
$ conda install -c conda-forge cupy==14.0.1 openmpi==4.1.6 h5py[build=*openmpi*]
$ conda install -c conda-forge tomopy==1.15.3 # optional
$ pip install aiofiles astra-toolbox click graypy hdf5plugin loguru nvtx pillow pyyaml scikit-image scipy tqdm
$ pip install httomo httomolib httomolibgpu tomobar

Setup HTTomo development environment:

$ pip install -e .[dev] # development mode