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update samples from Release-57 as a part of SDK release

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amlrelsa-ms
2020-07-06 22:05:24 +00:00
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commit 6c629f1eda
67 changed files with 1470 additions and 1338 deletions
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78
how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
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@@ -80,9 +80,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register input and output datasets\n",
"## Create trained model\n",
"\n",
"For this example, we have provided a small model (`sklearn_regression_model.pkl` in the notebook's directory) that was trained on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). Here, you will register the data used to create this model in your workspace."
"For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
]
},
{
@@ -91,9 +91,42 @@
"metadata": {},
"outputs": [],
"source": [
"import joblib\n",
"\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.linear_model import Ridge\n",
"\n",
"\n",
"dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
"\n",
"model = Ridge().fit(dataset_x, dataset_y)\n",
"\n",
"joblib.dump(model, 'sklearn_regression_model.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register input and output datasets\n",
"\n",
"Here, you will register the data used to create the model in your workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"from azureml.core import Dataset\n",
"\n",
"\n",
"np.savetxt('features.csv', dataset_x, delimiter=',')\n",
"np.savetxt('labels.csv', dataset_y, delimiter=',')\n",
"\n",
"datastore = ws.get_default_datastore()\n",
"datastore.upload_files(files=['./features.csv', './labels.csv'],\n",
" target_path='sklearn_regression/',\n",
@@ -125,6 +158,8 @@
},
"outputs": [],
"source": [
"import sklearn\n",
"\n",
"from azureml.core import Model\n",
"from azureml.core.resource_configuration import ResourceConfiguration\n",
"\n",
@@ -133,7 +168,7 @@
" model_name='my-sklearn-model', # Name of the registered model in your workspace.\n",
" model_path='./sklearn_regression_model.pkl', # Local file to upload and register as a model.\n",
" model_framework=Model.Framework.SCIKITLEARN, # Framework used to create the model.\n",
" model_framework_version='0.19.1', # Version of scikit-learn used to create the model.\n",
" model_framework_version=sklearn.__version__, # Version of scikit-learn used to create the model.\n",
" sample_input_dataset=input_dataset,\n",
" sample_output_dataset=output_dataset,\n",
" resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),\n",
@@ -174,19 +209,9 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Webservice\n",
"from azureml.exceptions import WebserviceException\n",
"\n",
"\n",
"service_name = 'my-sklearn-service'\n",
"\n",
"# Remove any existing service under the same name.\n",
"try:\n",
" Webservice(ws, service_name).delete()\n",
"except WebserviceException:\n",
" pass\n",
"\n",
"service = Model.deploy(ws, service_name, [model])\n",
"service = Model.deploy(ws, service_name, [model], overwrite=True)\n",
"service.wait_for_deployment(show_output=True)"
]
},
@@ -207,10 +232,7 @@
"\n",
"\n",
"input_payload = json.dumps({\n",
" 'data': [\n",
" [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n",
" -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
" ],\n",
" 'data': dataset_x[0:2].tolist(),\n",
" 'method': 'predict' # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n",
"})\n",
"\n",
@@ -262,7 +284,7 @@
" 'inference-schema[numpy-support]',\n",
" 'joblib',\n",
" 'numpy',\n",
" 'scikit-learn'\n",
" 'scikit-learn=={}'.format(sklearn.__version__)\n",
"])"
]
},
@@ -303,20 +325,12 @@
},
"outputs": [],
"source": [
"from azureml.core import Webservice\n",
"from azureml.core.model import InferenceConfig\n",
"from azureml.core.webservice import AciWebservice\n",
"from azureml.exceptions import WebserviceException\n",
"\n",
"\n",
"service_name = 'my-custom-env-service'\n",
"\n",
"# Remove any existing service under the same name.\n",
"try:\n",
" Webservice(ws, service_name).delete()\n",
"except WebserviceException:\n",
" pass\n",
"\n",
"inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
"aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
"\n",
@@ -324,7 +338,8 @@
" name=service_name,\n",
" models=[model],\n",
" inference_config=inference_config,\n",
" deployment_config=aci_config)\n",
" deployment_config=aci_config,\n",
" overwrite=True)\n",
"service.wait_for_deployment(show_output=True)"
]
},
@@ -342,10 +357,7 @@
"outputs": [],
"source": [
"input_payload = json.dumps({\n",
" 'data': [\n",
" [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n",
" -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
" ]\n",
" 'data': dataset_x[0:2].tolist()\n",
"})\n",
"\n",
"output = service.run(input_payload)\n",
@@ -471,7 +483,7 @@
" 'inference-schema[numpy-support]',\n",
" 'joblib',\n",
" 'numpy',\n",
" 'scikit-learn'\n",
" 'scikit-learn=={}'.format(sklearn.__version__)\n",
"])\n",
"inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
"# if cpu and memory_in_gb parameters are not provided\n",