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 Full YAML 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

Skip cupy entirely — there is no arm64/macOS build, and it cannot be installed on Apple Silicon. astra-toolbox and tomopy do have osx-arm64 conda-forge builds (CPU-only algorithms), which is all a CPU pipeline needs. mpi4py is required even for a single-process run, since HTTomo’s CLI unconditionally imports mpi4py.MPI. Replace conda with mamba below if it’s available in the environment, for a faster package resolution.

conda create --name httomo python=3.11
conda activate httomo

# numpy must stay below 2.0 — HTTomo's CPU/GPU array-type detection
# (block.is_gpu) relies on numpy.ndarray *not* having a `.device`
# attribute, an assumption NumPy 2.0's Array API support breaks.
conda install -c conda-forge "numpy<2" mpi4py openmpi==4.1.6 \
  "h5py=*=mpi_openmpi*" astra-toolbox tomopy==1.15.3 \
  aiofiles click graypy loguru nvtx pillow pyyaml \
  scikit-image scipy tqdm hdf5plugin pywavelets

# compilers needed to build httomolib's OpenMP-based C extension —
# macOS's system clang has no -fopenmp support
conda install -c conda-forge compilers llvm-openmp

  1. Install HTTomo

pip install httomo httomo-backends httomolib tomobar --no-deps

Verify:

python -m httomo --help

  1. Known issues on this path (as of httomo 3.0 / httomolib 4.0.1)

If h5py ever gets silently swapped back to a non-MPI build by a later conda install (check with python -c "import h5py; print(h5py.get_config().mpi)"), pin it:

conda config --env --append pinned_packages 'h5py=*=mpi_openmpi*'

  1. Optional step. Run HTTomo tests to make sure that everything works correctly.

  2. Running a CPU pipeline

Always validate the pipeline first:

python -m httomo check pipeline.yaml data.nxs

Run serially:

python -m httomo run data.nxs pipeline.yaml ./output --max-memory 10G

Or across multiple CPU cores with MPI (--max-memory is per-process, so divide your budget by the process count):

mpirun -np 4 python -m httomo run data.nxs pipeline.yaml ./output --max-memory 2G

Note

With 16GB of unified memory shared with macOS itself, keep --max-memory well under the physical total (8–10G total budget is a safe starting point) to avoid swapping.