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227 | 227 | "metadata": {},
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228 | 228 | "source": [
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229 | 229 | "## Run OpenVINO model\n",
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230 |
| - "[back to top ⬆️](#Table-of-contents:)" |
| 230 | + "[back to top ⬆️](#Table-of-contents:)\n", |
| 231 | + "\n", |
| 232 | + "Select device from dropdown list for running inference using OpenVINO" |
| 233 | + ] |
| 234 | + }, |
| 235 | + { |
| 236 | + "cell_type": "code", |
| 237 | + "execution_count": 7, |
| 238 | + "metadata": {}, |
| 239 | + "outputs": [ |
| 240 | + { |
| 241 | + "data": { |
| 242 | + "application/vnd.jupyter.widget-view+json": { |
| 243 | + "model_id": "a56d78fc8ba3470cbcca4840e8f05caa", |
| 244 | + "version_major": 2, |
| 245 | + "version_minor": 0 |
| 246 | + }, |
| 247 | + "text/plain": [ |
| 248 | + "Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')" |
| 249 | + ] |
| 250 | + }, |
| 251 | + "execution_count": 7, |
| 252 | + "metadata": {}, |
| 253 | + "output_type": "execute_result" |
| 254 | + } |
| 255 | + ], |
| 256 | + "source": [ |
| 257 | + "import ipywidgets as widgets\n", |
| 258 | + "\n", |
| 259 | + "core = ov.Core()\n", |
| 260 | + "\n", |
| 261 | + "device = widgets.Dropdown(\n", |
| 262 | + " options=core.available_devices + [\"AUTO\"],\n", |
| 263 | + " value='AUTO',\n", |
| 264 | + " description='Device:',\n", |
| 265 | + " disabled=False,\n", |
| 266 | + ")\n", |
| 267 | + "\n", |
| 268 | + "device" |
231 | 269 | ]
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232 | 270 | },
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233 | 271 | {
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239 | 277 | },
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240 | 278 | {
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241 | 279 | "cell_type": "code",
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242 |
| - "execution_count": 7, |
| 280 | + "execution_count": 9, |
243 | 281 | "metadata": {
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244 | 282 | "id": "t_hOCui8YzZz"
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245 | 283 | },
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248 | 286 | "core = ov.Core()\n",
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249 | 287 | "\n",
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250 | 288 | "# Compile OpenVINO model for loading on device\n",
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251 |
| - "compiled_ov_model = core.compile_model(ov_model, \"CPU\")\n", |
| 289 | + "compiled_ov_model = core.compile_model(ov_model, device.value)\n", |
252 | 290 | "\n",
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253 | 291 | "\n",
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254 | 292 | "class OVModelWrapperResult:\n",
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258 | 296 | "\n",
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259 | 297 | "class OVModelWrapper:\n",
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260 | 298 | " dtype = torch.float32\n",
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261 |
| - " device = \"cpu\"\n", |
| 299 | + " device = model.device\n", |
262 | 300 | "\n",
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263 | 301 | " def __call__(self, **kwargs):\n",
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264 | 302 | " # obtain output tensor for getting predictions\n",
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275 | 313 | },
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276 | 314 | {
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277 | 315 | "cell_type": "code",
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278 |
| - "execution_count": 8, |
| 316 | + "execution_count": 10, |
279 | 317 | "metadata": {
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280 | 318 | "id": "5EuBpVt-aRcy"
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281 | 319 | },
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