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fix: define device once from use_cuda, eliminating hardcoded cuda:1 #9

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Merged
bushidocodes merged 1 commit into from
May 29, 2026
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fix: define device once from use_cuda, eliminating hardcoded cuda:1 #9

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7 changes: 4 additions & 3 deletions My First Malware.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,8 @@
"metadata": {},
"outputs": [],
"source": [
"use_cuda = True"
"use_cuda = True\n",
"device = torch.device(\"cuda\" if use_cuda and torch.cuda.is_available() else \"cpu\")"
]
},
{
Expand Down Expand Up @@ -1824,7 +1825,7 @@
"\n",
" def forward(self, g, feature):\n",
" # Initialize the node features with h.\n",
" g.ndata['h'] = feature.to(torch.device(\"cuda:1\" if torch.cuda.is_available() and use_cuda is True else \"cpu\"))\n",
" g.ndata['h'] = feature.to(device)\n",
" g.update_all(\n",
" message_func = fn.copy_src(src='h', out='m'), \n",
" reduce_func = fn.sum(msg='m', out='h')\n",
Expand Down Expand Up @@ -1916,7 +1917,7 @@
" def __init__(self, learning_rate, model, data_loader, k_fold_number_splits, accuracy_samples, loss_samples):\n",
" super(Trainer, self).__init__()\n",
" self.learning_rate = learning_rate\n",
" self.device = torch.device(\"cuda:1\" if torch.cuda.is_available() and use_cuda is True else \"cpu\")\n",
" self.device = device\n",
" self.model = model.to(self.device)\n",
" self.data_loader = data_loader\n",
" self.loss_func = nn.NLLLoss()\n",
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11 changes: 5 additions & 6 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -120,16 +120,15 @@ For large datasets this step is slow (Androguard can take 10–60 seconds per AP

### GPU configuration

The notebook sets `use_cuda = True` and targets `cuda:1`. If you only have one GPU (device `cuda:0`) or want to use CPU, edit the relevant line in the second cell:
The notebook sets `use_cuda = True` in the second cell and derives a `device` variable from it:

```python
# Single GPU
device = torch.device("cuda:0" if use_cuda and torch.cuda.is_available() else "cpu")

# CPU only
use_cuda = False
use_cuda = True
device = torch.device("cuda" if use_cuda and torch.cuda.is_available() else "cpu")
```

This selects `cuda:0` when a GPU is available, or falls back to CPU automatically. To force CPU-only execution, change `use_cuda = False` in the second cell.

## Results

Trained on the AMD dataset with the default hyperparameters:
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