|
@@ -1125,13 +1125,17 @@
|
|
|
"source": [
|
|
"source": [
|
|
|
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
|
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
|
|
"\n",
|
|
"\n",
|
|
|
- "# If you have a Mac with Apple Silicon chip, you can uncomment the next lines of code\n",
|
|
|
|
|
- "# to train the model on the Mac's GPU cores. However, as of this writing, this results in\n",
|
|
|
|
|
- "# larger numerical deviations from the results shown in this chapter, because Apple Silicon\n",
|
|
|
|
|
- "# support in PyTorch is still experimental\n",
|
|
|
|
|
- "\n",
|
|
|
|
|
- "# if torch.backends.mps.is_available():\n",
|
|
|
|
|
- "# device = torch.device(\"mps\")\n",
|
|
|
|
|
|
|
+ "# Note:\n",
|
|
|
|
|
+ "# Uncommenting the following lines will allow the code to run on Apple Silicon chips, if applicable,\n",
|
|
|
|
|
+ "# which is much faster than on an Apple CPU (as measured on an M3 MacBook Air).\n",
|
|
|
|
|
+ "# However, the resulting loss values may be slightly different.\n",
|
|
|
|
|
+ "\n",
|
|
|
|
|
+ "#if torch.cuda.is_available():\n",
|
|
|
|
|
+ "# device = torch.device(\"cuda\")\n",
|
|
|
|
|
+ "#elif torch.backends.mps.is_available():\n",
|
|
|
|
|
+ "# device = torch.device(\"mps\")\n",
|
|
|
|
|
+ "#else:\n",
|
|
|
|
|
+ "# device = torch.device(\"cpu\")\n",
|
|
|
"\n",
|
|
"\n",
|
|
|
"print(\"Device:\", device)"
|
|
"print(\"Device:\", device)"
|
|
|
]
|
|
]
|
|
@@ -2762,7 +2766,7 @@
|
|
|
"name": "python",
|
|
"name": "python",
|
|
|
"nbconvert_exporter": "python",
|
|
"nbconvert_exporter": "python",
|
|
|
"pygments_lexer": "ipython3",
|
|
"pygments_lexer": "ipython3",
|
|
|
- "version": "3.10.6"
|
|
|
|
|
|
|
+ "version": "3.11.4"
|
|
|
}
|
|
}
|
|
|
},
|
|
},
|
|
|
"nbformat": 4,
|
|
"nbformat": 4,
|