GPU Acceleration

This is optional. MagScope can work entirely with your computer’s CPU. However, adding GPU support can make image processing faster.

Do you have a supported GPU?

To use GPU-acceleration in MagScope you must have a CUDA-capable GPU and a compatible NVIDIA driver. Most NVIDIA GPUs are supported. See this list from NVIDIA to check your specific GPU.

Recommended install

Before trying to install MagScope with GPU support you should have followed the Getting Started guide and got MagScope to work with just the CPU. With your virtual environment active, install MagScope first:

pip install magscope

Then install the CuPy package that matches your CUDA major version, using CuPy’s [ctk] extra:

CUDA version	Pip command
12.x	pip install "cupy-cuda12x[ctk]"
13.x	pip install "cupy-cuda13x[ctk]"

This is the recommended path for a fresh environment. CuPy will install the CUDA runtime components it needs from PyPI, so you do not usually need a separate system CUDA Toolkit.

Alternative install

If you already manage CUDA separately on your system, MagScope also provides optional dependencies that install the matching CuPy wheel:

CUDA version	Pip command
12.x	pip install "magscope[cu12]"
13.x	pip install "magscope[cu13]"

This path assumes your NVIDIA driver and local CUDA installation are already set up correctly. If you are starting from scratch, prefer the [ctk] install above.

Supported versions

MagScope supports CUDA 12.x and 13.x for GPU acceleration. CUDA 11 support has been removed to match current CuPy support.

If you need to diagnose an existing system CUDA installation, nvcc --version can still be useful, but it is not required for the recommended [ctk] workflow.

To check it worked you can run the following in Python:

import magscope
print(magscope.check_cupy())

This returns True if CuPy is available and usable. Otherwise it returns False.