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55 | 55 | "import azureml.core\n",
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56 | 56 | "import pandas as pd\n",
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57 | 57 | "from azureml.core.workspace import Workspace\n",
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58 |
| - "from azureml.train.automl.run import AutoMLRun\n", |
59 |
| - "import time\n", |
60 | 58 | "import logging"
|
61 | 59 | ]
|
62 | 60 | },
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93 | 91 | "output['Location'] = ws.location\n",
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94 | 92 | "output['Project Directory'] = project_folder\n",
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95 | 93 | "pd.set_option('display.max_colwidth', -1)\n",
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96 |
| - "pd.DataFrame(data=output, index=['']).T" |
| 94 | + "outputDf = pd.DataFrame(data = output, index = [''])\n", |
| 95 | + "outputDf.T" |
97 | 96 | ]
|
98 | 97 | },
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99 | 98 | {
|
|
112 | 111 | "outputs": [],
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113 | 112 | "source": [
|
114 | 113 | "import azureml.dataprep as dprep\n",
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115 |
| - "import os\n", |
116 | 114 | "\n",
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117 | 115 | "file_path = os.path.join(os.getcwd(), \"dflows.dprep\")\n",
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118 | 116 | "\n",
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308 | 306 | " metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
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309 | 307 | " metricslist[int(properties['iteration'])] = metrics\n",
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310 | 308 | "\n",
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311 |
| - "import pandas as pd\n", |
312 | 309 | "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
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313 | 310 | "rundata"
|
314 | 311 | ]
|
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351 | 348 | "description = 'Automated Machine Learning Model'\n",
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352 | 349 | "tags = None\n",
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353 | 350 | "local_run.register_model(description=description, tags=tags)\n",
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354 |
| - "local_run.model_id # Use this id to deploy the model as a web service in Azure" |
| 351 | + "print(local_run.model_id) # Use this id to deploy the model as a web service in Azure" |
355 | 352 | ]
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356 | 353 | },
|
357 | 354 | {
|
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471 | 468 | "> * Explored and reviewed training results\n",
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472 | 469 | "> * Registered the best model\n",
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473 | 470 | "\n",
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474 |
| - "[Deploy your model](02.deploy-models.ipynb) with Azure Machine Learning." |
| 471 | + "You can also try out the [image classification tutorial](img-classification-part1-training.ipynb)." |
475 | 472 | ]
|
476 | 473 | }
|
477 | 474 | ],
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|
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