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update samples from Release-80 as a part of SDK release

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amlrelsa-ms
2020-12-12 00:45:42 +00:00
parent a79f8c254a
commit a6817063df
2 changed files with 12 additions and 7 deletions
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2
how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
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@@ -199,6 +199,7 @@
"|**n_cross_validations**|Number of cross validation splits.|\n", "|**n_cross_validations**|Number of cross validation splits.|\n",
"|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n", "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|\n", "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
"|**scenario**|We need to set this parameter to 'Latest' to enable some experimental features. This parameter should not be set outside of this experimental notebook.|\n",
"\n", "\n",
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)" "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
] ]
@@ -227,6 +228,7 @@
" compute_target = compute_target,\n", " compute_target = compute_target,\n",
" training_data = train_data,\n", " training_data = train_data,\n",
" label_column_name = label,\n", " label_column_name = label,\n",
" scenario='Latest',\n",
" **automl_settings\n", " **automl_settings\n",
" )" " )"
] ]

17
how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
View File

@@ -276,16 +276,17 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from azureml.exceptions import ComputeTargetException\n", "from azureml.core.compute import ComputeTarget, AksCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n", "\n",
"aks_name = \"my-aks\"\n", "aks_name = \"my-aks-insights\"\n",
"\n", "\n",
"creating_compute = False\n", "creating_compute = False\n",
"try:\n", "try:\n",
" aks_target = ComputeTarget(ws, aks_name)\n", " aks_target = ComputeTarget(ws, aks_name)\n",
" print(\"Using existing AKS cluster {}.\".format(aks_name))\n", " print(\"Using existing AKS compute target {}.\".format(aks_name))\n",
"except ComputeTargetException:\n", "except ComputeTargetException:\n",
" print(\"Creating a new AKS cluster {}.\".format(aks_name))\n", " print(\"Creating a new AKS compute target {}.\".format(aks_name))\n",
"\n", "\n",
" # Use the default configuration (can also provide parameters to customize).\n", " # Use the default configuration (can also provide parameters to customize).\n",
" prov_config = AksCompute.provisioning_configuration()\n", " prov_config = AksCompute.provisioning_configuration()\n",
@@ -302,7 +303,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"%%time\n", "%%time\n",
"if creating_compute:\n", "if creating_compute and aks_target.provisioning_state != \"Succeeded\":\n",
" aks_target.wait_for_completion(show_output=True)" " aks_target.wait_for_completion(show_output=True)"
] ]
}, },
@@ -382,7 +383,7 @@
" aks_service.wait_for_deployment(show_output=True)\n", " aks_service.wait_for_deployment(show_output=True)\n",
" print(aks_service.state)\n", " print(aks_service.state)\n",
"else:\n", "else:\n",
" raise ValueError(\"AKS provisioning failed. Error: \", aks_service.error)" " raise ValueError(\"AKS cluster provisioning failed. Error: \", aks_target.provisioning_errors)"
] ]
}, },
{ {
@@ -460,7 +461,9 @@
"%%time\n", "%%time\n",
"aks_service.delete()\n", "aks_service.delete()\n",
"aci_service.delete()\n", "aci_service.delete()\n",
"model.delete()" "model.delete()\n",
"if creating_compute:\n",
" aks_target.delete()"
] ]
} }
], ],