add test service

docs/how-to-deploy-to-aci/how-to-deploy-to-aci.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+import azureml.core
+print('SDK version' + azureml.core.VERSION)
+
+# PREREQ: load workspace info
+# import azureml.core
+# <load_workspace>
+from azureml.core import Workspace
+ws = Workspace.from_config()
+# </load_workspace>
+
+scorepy_content = "import json\nimport numpy as np\nimport os\nimport pickle\nfrom sklearn.externals import joblib\nfrom sklearn.linear_model import LogisticRegression\n\nfrom azureml.core.model import Model\n\ndef init():\n    global model\n    # retreive the path to the model file using the model name\n    model_path = Model.get_model_path('sklearn_mnist')\n    model = joblib.load(model_path)\n\ndef run(raw_data):\n    data = np.array(json.loads(raw_data)['data'])\n    # make prediction\n    y_hat = model.predict(data)\n    return json.dumps(y_hat.tolist())"
+print(scorepy_content)
+with open("score.py","w") as f:
+    f.write(scorepy_content) 
+
+
+# PREREQ: create environment file
+from azureml.core.conda_dependencies import CondaDependencies 
+
+myenv = CondaDependencies()
+myenv.add_conda_package("scikit-learn")
+
+with open("myenv.yml","w") as f:
+    f.write(myenv.serialize_to_string())
+
+# <config-image>
+from azureml.core.image import ContainerImage
+
+image_config = ContainerImage.image_configuration(execution_script = "score.py",
+                                                  runtime = "python",
+                                                  conda_file = "myenv.yml",
+                                                  description = "Image with mnist model",
+                                                  tags = {"data": "mnist", "type": "classification"}
+                                                 )
+# </config-image>
+
+# <config-aci>
+from azureml.core.webservice import AciWebservice
+
+aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, 
+                                               memory_gb = 1, 
+                                               tags = {"data": "mnist", "type": "classification"}, 
+                                               description = 'Handwriting recognition')
+# </config-aci>
+
+# <register-model>
+from azureml.core.model import Model
+
+model_name = "sklearn_mnist"
+model = Model.register(model_path = "sklearn_mnist_model.pkl",
+                        model_name = model_name,
+                        tags = {"data": "mnist", "type": "classification"},
+                        description = "Mnist handwriting recognition",
+                        workspace = ws)
+# </register-model>
+
+# <retrieve-model>
+from azureml.core.model import Model
+
+model_name = "sklearn_mnist"
+model=Model(ws, model_name)
+# </retrieve-model>
+
+
+# ## DEPLOY FROM REGISTERED MODEL
+
+# <option2-deploy>
+from azureml.core.webservice import Webservice
+
+service_name = 'aci-mnist-2'
+service = Webservice.deploy_from_model(deployment_config = aciconfig,
+                                       image_config = image_config,
+                                       models = [model], # this is the registered model object
+                                       name = service_name,
+                                       workspace = ws)
+service.wait_for_deployment(show_output = True)
+print(service.state)
+# </option2-deploy>
+
+service.delete()
+
+# ## DEPLOY FROM IMAGE
+
+
+# <option3-create-image>
+from azureml.core.image import ContainerImage
+
+image = ContainerImage.create(name = "myimage1",
+                              models = [model], # this is the registered model object
+                              image_config = image_config,
+                              workspace = ws)
+
+image.wait_for_creation(show_output = True)
+# </option3-create-image>
+
+# <option3-deploy>
+from azureml.core.webservice import Webservice
+
+service_name = 'aci-mnist-13'
+service = Webservice.deploy_from_image(deployment_config = aciconfig,
+                                            image = image,
+                                            name = service_name,
+                                            workspace = ws)
+service.wait_for_deployment(show_output = True)
+print(service.state)
+# </option3-deploy>
+
+service.delete()
+
+
+# ## DEPLOY FROM MODEL FILE
+# First change score.py!
+
+
+
+scorepy_content = "import json\nimport numpy as np\nimport os\nimport pickle\nfrom sklearn.externals import joblib\nfrom sklearn.linear_model import LogisticRegression\n\nfrom azureml.core.model import Model\n\ndef init():\n    global model\n    # retreive the path to the model file using the model name\n    model_path = Model.get_model_path('sklearn_mnist_model.pkl')\n    model = joblib.load(model_path)\n\ndef run(raw_data):\n    data = np.array(json.loads(raw_data)['data'])\n    # make prediction\n    y_hat = model.predict(data)\n    return json.dumps(y_hat.tolist())"
+with open("score.py","w") as f:
+    f.write(scorepy_content) 
+
+
+
+# <option1-deploy>
+from azureml.core.webservice import Webservice
+
+service_name = 'aci-mnist-1'
+service = Webservice.deploy(deployment_config = aciconfig,
+                                image_config = image_config,
+                                model_paths = ['sklearn_mnist_model.pkl'],
+                                name = service_name,
+                                workspace = ws)
+
+service.wait_for_deployment(show_output = True)
+print(service.state)
+# </option1-deploy>
+
+# <test-service>
+# Load Data
+import os
+import urllib
+
+os.makedirs('./data', exist_ok = True)
+
+urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/test-images.gz')
+
+from utils import load_data
+X_test = load_data('./data/test-images.gz', False) / 255.0
+
+from sklearn import datasets
+import numpy as np
+import json
+
+# find 5 random samples from test set
+n = 5
+sample_indices = np.random.permutation(X_test.shape[0])[0:n]
+
+test_samples = json.dumps({"data": X_test[sample_indices].tolist()})
+test_samples = bytes(test_samples, encoding = 'utf8')
+
+# predict using the deployed model
+prediction = service.run(input_data = test_samples)
+print(prediction)
+# </test-service>
+
+service.delete()
+
+
+
+
+