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docs: note CUDA_VISIBLE_DEVICES workaround for multi-GPU systems #75

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docs: note CUDA_VISIBLE_DEVICES workaround for multi-GPU systems #75

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Expand Up @@ -123,6 +123,26 @@ ks = SkalaKS(mol, xc="skala-1.1")
ks.kernel()
```

### Known issue: multiple visible GPUs

Skala uses a single GPU, but importing `gpu4pyscf` allocates memory on **every**
visible CUDA device. This can conflict with PyTorch and with other processes
sharing those GPUs (e.g. in MPI-parallel workloads).

Restrict CUDA to one device **before** launching Python:

```bash
CUDA_VISIBLE_DEVICES=0 python my_script.py
```

For MPI-parallel runs, assign one GPU per local rank:

```bash
mpirun -np 4 bash -c 'CUDA_VISIBLE_DEVICES=$OMPI_COMM_WORLD_LOCAL_RANK python my_script.py'
```

Tracked upstream at [pyscf/gpu4pyscf#435](https://github.com/pyscf/gpu4pyscf/issues/435).

## Getting started: ASE calculator

Skala also provides an [ASE](https://wiki.fysik.dtu.dk/ase/) calculator for energy, force, and geometry optimization workflows:
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