Prerequisites and supported platforms

Contents

Prerequisites and supported platforms#

Requirements#

Requirement	Version
Python	3.10 or later; each wheel is tested on 3.10 and 3.14
PyTorch	2.2 or later (2.3 on macOS, the first to carry `libomp.dylib`; 2.7.1 on Linux, the first whose `libgomp` is named `libgomp.so.1`), CPU or CUDA build
NumPy, SciPy	NumPy 1.24 and SciPy 1.10 or later; MRI-NUFFT, a dependency, raises these to NumPy 2.2 and SciPy 1.13
TorchSim, MRI-NUFFT	TorchSim 0.0.8 and MRI-NUFFT 1.0 or later, installed as dependencies

Platforms#

Platform	Distribution	Notes
Linux x86-64, glibc 2.28 or later	Wheel	CPU build on PyPI; CUDA build attached to the GitHub release of each version
macOS 11 or later on Apple silicon	Wheel	CPU only; OpenMP is the runtime PyTorch installs (see The OpenMP runtime)
Linux aarch64	Source distribution	BART and FINUFFT are compiled on installation
macOS on Intel	Source distribution	BART and FINUFFT are compiled on installation
Windows 10 or later on x86-64	Wheel	CPU only; OpenMP is the runtime PyTorch installs

A source installation needs the toolchain listed under Source builds. Apple MPS devices are not supported; the device paths are CPU and CUDA.

Optional components#

Extra	Installs	Needed for
`mkl`	Intel MKL (Linux x86-64)	MKL as the source of BLAS, LAPACK and FFT routines, and of FINUFFT’s FFT
`deepinv`	DeepInverse	`bartorch.interop.to_deepinv()`, and DeepInverse’s denoisers for `ImplicitPrior`
`learning`	Lightning, TorchIO	The training stages and the complex-valued augmentation in `bartorch.learning` (Learning)
`correct`	SimpleITK	Bias field and gradient nonlinearity correction in `bartorch.tools`
`motion`	SimpleITK	Rigid registration of navigator planes in `bartorch.tools`
`pyhysco`	PyHySCO (GPL-3.0-only)	`bartorch.tools.correct_susceptibility()`

PyHySCO is not distributed with bartorch; it is imported only when correct_susceptibility() is called. The spiral deblurring of deblur() uses Triton on a CUDA device when it is installed.

The examples additionally need brainweb-dl, matplotlib and cmap; the learned-regularization examples lightning, torchio, monai and deepinv, and the tours SimpleITK (Examples).