Idiomatic multi-gpu usage

[s1ddok]

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?

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

[zero0kiriyu]

I'm experiencing the same issue, memory is only allocated on gpu#0.

[jakebytes]

I've hit the same issue, even passing device_id like this doesn't fix it:

dec = nvimgcodec.Decoder(device_id=device_id)

What does work is setting the default CUDA device like this:

device = torch.device(f"cuda:{device_id}") # set desired device id here
torch.cuda.set_device(device)  # required so nvimgcodec uses the correct device

[zero0kiriyu]

I've hit the same issue, even passing device_id like this doesn't fix it:

dec = nvimgcodec.Decoder(device_id=device_id) What does work is setting the default CUDA device like this:

device = torch.device(f"cuda:{device_id}") # set desired device id here torch.cuda.set_device(device) # required so nvimgcodec uses the correct device

I try this method but it does not work for me