Installation

TensorMesh runs on Linux, macOS, and Windows. The only hard requirements are Python ≥ 3.10 and PyTorch ≥ 2.0; everything else (NumPy, SciPy, meshio, torch-sla, …) is pulled in automatically by pip.

If you plan to use the GPU backend, install a CUDA-enabled PyTorch build before installing TensorMesh — follow the official PyTorch installation selector for the right command on your platform / CUDA version.

Install via PyPI

The recommended way to install TensorMesh is from PyPI:

pip install tensormesh-fem             # CPU stack (SciPy + PyTorch Krylov)
pip install "tensormesh-fem[gpu]"      # + all PyPI solver backends (cuDSS + PyAMG)

Use the second form if you have an NVIDIA GPU and want the fastest CUDA direct solver — the [gpu] extra pulls in cuDSS (plus PyAMG) through torch-sla. The quotes are needed because [...] is a shell glob character; see Sparse solvers and GPU acceleration for the per-backend breakdown if you only want one of them.

Either form pulls in all required dependencies, including torch-sla, the differentiable sparse linear algebra library that powers TensorMesh’s solvers. The base pip install tensormesh-fem installs the CPU sparse stack (SciPy + a device-agnostic native-PyTorch Krylov backend); see Sparse solvers and GPU acceleration below for the full extras matrix ([cudss] / [pyamg] / [gpu]) and how to verify which backends are usable on your machine.

Install from source

For development work, or to get the latest unreleased changes, clone the repository and install in editable mode:

git clone https://github.com/camlab-ethz/TensorMesh.git
cd TensorMesh
pip install -e .

The -e (editable) flag means edits to the source tree are picked up without reinstalling. To install with the test dependencies as well:

pip install -e ".[test]"

Sparse solvers and GPU acceleration

The sparse linear-algebra layer that powers TensorMesh’s solvers lives in a standalone library, torch-sla, so that it can evolve independently and serve other projects. torch-sla is a hard, import-time dependency — tensormesh.sparse will not import without it — and is the canonical entry point for both CPU and GPU sparse solves. All current and future solver work lands in torch-sla first; we recommend keeping it up to date.

The base tensormesh-fem wheel pulls the CPU stack (SciPy + native PyTorch Krylov — the latter also runs on CUDA/ROCm devices out of the box). To add another solver backend, install one of the mirrored extras:

Install command	Adds backend	When to pick this
pip install tensormesh-fem	CPU stack	Default; scipy direct/iterative + device-agnostic pytorch Krylov.
pip install "tensormesh-fem[cudss]"	cuDSS (CUDA)	Fastest GPU direct solver (LU / Cholesky / LDLT).
pip install "tensormesh-fem[pyamg]"	PyAMG (CPU)	Algebraic-multigrid V-cycles for large PDE systems.
pip install "tensormesh-fem[gpu]"	All PyPI backends	Convenience extra — installs torch-sla[all] (cuDSS + PyAMG).

These mirror the upstream torch-sla extras ([cudss] / [pyamg] / [all]) — installing tensormesh-fem[gpu] is exactly equivalent to pip install tensormesh-fem torch-sla[all], just spelled in one step. Two further backends — strumpack (portable multifrontal direct, CPU/CUDA/ROCm) and amgx (NVIDIA AmgX) — are compiled extensions shipped as prebuilt wheels on torch-sla’s GitHub Releases, not PyPI.

Inspect what’s installed

After installing, you can see which backends are usable on the current machine — and a one-line install hint for any that are not:

>>> import torch_sla
>>> torch_sla.show_backends()
torch-sla backend status (CUDA: available)
  scipy      [CPU]           available
  pytorch    [CPU/CUDA]      available
  cudss      [CUDA]          available
  pyamg      [CPU]           available
  amgx       [CUDA]          not available — pip install torch-sla[amgx]
  strumpack  [CPU/CUDA/ROCm] not available — pip install torch-strumpack

See Sparse Solvers for the full backend / method matrix and how to pick a non-default backend at solve time.

Other extras

A couple of smaller extras for optional functionality:

Extra	Install command
Plotly for example notebooks	pip install "tensormesh-fem[example]"
Test suite (pytest, pytest-cov)	pip install "tensormesh-fem[test]"

Two further packages are commonly useful but are not declared as extras — install them directly when needed:

pip install gmsh       # external mesh generation / .msh I/O
pip install pyvista    # interactive 3D visualization

Next steps

Once installed, head to Verify Install to run a smoke test, or jump straight into Quickstart for a 2D Poisson walkthrough.