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Platform support

What each install line of the installation page brings on each machine, which dataset type the readers return there, and what was tested.

Machine Install Widget compute (device="auto") read_4dstem(path), Show4DSTEM(path) Tested
Linux, NVIDIA GPU quantem.widget[cuda] CUDA kept encoded on the GPU by quantem.gpu yes
Windows, NVIDIA GPU quantem.widget[cuda] CUDA kept encoded on the GPU by quantem.gpu CPU tested, CUDA untested
Windows on ARM torch from the PyTorch CPU index, then quantem.widget[cpu] (no quantem core) CPU the stand-in, dense read on the CPU; compressed masters need x64 Python CPU tested
Mac, Apple silicon quantem.widget[mps] Apple GPU (MPS) kept encoded on the GPU by quantem.gpu (Metal) yes
Mac, Intel quantem.widget[cpu] (torch 2.2, NumPy 1.26), no quantem.gpu, no quantem core AMD GPU through torch MPS, else CPU the stand-in, dense read on the CPU yes
No GPU quantem.widget[cpu] CPU quantem core Dataset4dstem, dense read on the CPU yes

  • [cpu] (and a plain install) brings torch 2.2 or newer, NumPy, h5py, hdf5plugin and quantem core (hdf5plugin and quantem core not on Windows on ARM, quantem core not on an Intel Mac, see below): every widget, and Show4DSTEM on in-memory arrays, tensors on any device and files read densely into memory.

  • [cuda] adds quantem.gpu with CuPy; [mps] adds it with Metal and MLX. A 4D-STEM file then stays encoded on the GPU and loads in seconds. A wrong pick installs nothing harmful ([cuda] installs nothing extra on a Mac, [mps] nothing outside Apple silicon), and the widget's one-line notice names the right extra.

  • Intel Macs are supported without quantem.gpu: PyTorch publishes no wheel newer than torch 2.2 for them and quantem.gpu needs torch 2.3. The widgets compute on the Mac's AMD GPU through torch MPS (else the CPU), and the first widget prints one line saying so. torch 2.2 imports only with NumPy 1.x on these Macs; the package pins numpy<2 for them.

  • The readers return the dataset type of the machine, so the same code runs everywhere: read_4dstem gives quantem.gpu's dataset when quantem.gpu is installed and a GPU is present, else a quantem core Dataset4dstem; read_image and read_images give quantem core Dataset2d, read_image_stack a Dataset3d. quantem core needs torch 2.7 and NumPy 2 (no Intel Mac wheels) and hdf5plugin 6 (no Windows on ARM wheel), so on those two machines the readers return the widget's stand-in, with the same array, name, sampling, units, metadata, shape, ndim and dtype. Every widget accepts all of these, NumPy arrays and torch tensors.

    from quantem.widget import io

ds = io.read_image(path)
arr = ds.array

A file opened without the GPU path is read whole into memory. Show4DSTEM prints one line with the shape, dtype, size and seconds taken, and refuses with a clear message when the array would not fit in 80% of the available memory. It never bins or crops to make the data fit.