macOS (Apple Silicon)

macOS (Apple Silicon)#

Note

HTTomo’s GPU-accelerated methods (httomolibgpu) depend on CuPy, which requires an NVIDIA CUDA GPU. Apple Silicon Macs (M1/M2/M3/M4) have no CUDA support, so this path installs HTTomo in CPU-only mode, using TomoPy for reconstruction instead of the GPU backends. Pipelines must use CPU/TomoPy methods only (see Ready-to-use pipelines for an example CPU pipeline).

This guide has been tested on an M1 MacBook (16GB RAM) running native arm64 conda (not under Rosetta).

Installation steps#

  1. Install a native arm64 conda distribution

Make sure you install the arm64, not Intel/x86_64, build — otherwise everything below runs emulated under Rosetta and is significantly slower:

curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
bash Miniforge3-MacOSX-arm64.sh

  1. Create the environment

HTTomo requires Python 3.12 or later and NumPy 2.4. CuPy and httomolibgpu are not installed because they require an NVIDIA CUDA GPU.

mpi4py is required even for a single-process run because HTTomo imports mpi4py.MPI when its command-line interface starts.

$ conda create --name httomo --channel conda-forge \
    python=3.12 "numpy==2.4.*" \
    mpi4py openmpi==4.1.6 "h5py=*=mpi_openmpi*" \
    tomopy==1.15.3 astra-toolbox \
    aiofiles click graypy loguru nvtx pillow pyyaml \
    scikit-image scipy tqdm hdf5plugin pywavelets \
    compilers llvm-openmp pip
$ conda activate httomo

NumPy 2.x is required by the current HTTomo implementation. In particular, HTTomo uses the numpy.ndarray.device attribute introduced in NumPy 2.0 to identify CPU arrays.

The compilers and llvm-openmp packages are needed when building HTTomolib’s OpenMP-based extension because the system Clang compiler supplied by macOS does not provide OpenMP support by default.

  1. Install HTTomo

Install only the CPU backend packages. --no-deps is required because the published package metadata currently includes CUDA-only dependencies that cannot be installed on Apple Silicon.

$ python -m pip install --no-deps \
    httomo httomo-backends httomolib

Do not install httomolibgpu or tomobar in this environment. Both are GPU-oriented packages with CUDA dependencies.

  1. Verify the installation

Confirm the Python and NumPy versions and verify that parallel HDF5 is enabled:

$ python -c "import sys, numpy; print(sys.version); print(numpy.__version__)"
$ python -c "import h5py; print('Parallel HDF5:', h5py.get_config().mpi)"
$ python -m httomo --help

The first command should report Python 3.12 or later and NumPy 2.4.x. The second command should print Parallel HDF5: True.

Developers who need to run the source test suite should follow Development setup and testing.