Idiomatic multi-gpu usage

Describe the question.

I'm trying to utilize this library in multi-gpu torch settings and so far I'm getting no luck with frequent core dumped error, that doesn't tell me much.

my current approach is this (LLM generated):

            dev_idx = _torch.cuda.current_device() if _torch.cuda.is_available() else 0
            with cp.cuda.Device(dev_idx):
                encoder = nvimgcodec.Encoder()
                output_path = frame_dir / f"{to_stem(name)}.tiff"
                gpu_arr = cp.asarray(result.cpu())
                encoder.write(str(output_path), gpu_arr)
                # Release GPU memory retained by CuPy's pool to avoid OOM across concurrent saves
                try:
                    cp.get_default_memory_pool().free_all_blocks()
                except Exception:
                    pass

from nvidia-smi I see that most of the memory is being allocated on GPU 0 and it feels like nvImageEncoder is not explicitly aware of what GPU it is expected to run on. Can you provide example on how to utilize it and keep tensors on GPU?

Check for duplicates

• I have searched the open bugs/issues and have found no duplicates for this bug report

The issue names no repository files or tests. Start by reproducing the shown PyTorch, CuPy, and nvImageCodec multi-GPU snippet, then inspect how Encoder selects a device. Done means a documented, working usage path that keeps tensors on the intended GPU and avoids the reported core dump.