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

how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb

@@ -86,7 +86,37 @@
       "source": [
         "In this example, we will be using and registering two models. \n",
         "\n",
-        "You wil need to have a `first_model.pkl` file and `second_model.pkl` file in the current directory. The below call registers the files as Models with the names `my_first_model` and `my_second_model` in the workspace."
+        "First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "import sklearn\n",
+        "\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.linear_model import BayesianRidge, Ridge\n",
+        "\n",
+        "x, y = load_diabetes(return_X_y=True)\n",
+        "\n",
+        "first_model = Ridge().fit(x, y)\n",
+        "second_model = BayesianRidge().fit(x, y)\n",
+        "\n",
+        "joblib.dump(first_model, \"first_model.pkl\")\n",
+        "joblib.dump(second_model, \"second_model.pkl\")\n",
+        "\n",
+        "print(\"Trained models using scikit-learn {}.\".format(sklearn.__version__))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have our trained models locally, we will register them as Models with the names `my_first_model` and `my_second_model` in the workspace."
       ]
     },
     {
@@ -102,12 +132,12 @@
         "from azureml.core.model import Model\n",
         "\n",
         "my_model_1 = Model.register(model_path=\"first_model.pkl\",\n",
-        "                       model_name=\"my_first_model\",\n",
-        "                       workspace=ws)\n",
+        "                            model_name=\"my_first_model\",\n",
+        "                            workspace=ws)\n",
         "\n",
         "my_model_2 = Model.register(model_path=\"second_model.pkl\",\n",
-        "                       model_name=\"my_second_model\",\n",
-        "                       workspace=ws)"
+        "                            model_name=\"my_second_model\",\n",
+        "                            workspace=ws)"
       ]
     },
     {
@@ -149,25 +179,24 @@
       "outputs": [],
       "source": [
         "%%writefile score.py\n",
-        "import pickle\n",
+        "import joblib\n",
         "import json\n",
         "import numpy as np\n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.linear_model import Ridge\n",
+        "\n",
         "from azureml.core.model import Model\n",
         "\n",
         "def init():\n",
         "    global model_1, model_2\n",
-        "    # note here \"my_first_model\" is the name of the model registered under the workspace\n",
-        "    # this call should return the path to the model.pkl file on the local disk.\n",
+        "    # Here \"my_first_model\" is the name of the model registered under the workspace.\n",
+        "    # This call will return the path to the .pkl file on the local disk.\n",
         "    model_1_path = Model.get_model_path(model_name='my_first_model')\n",
         "    model_2_path = Model.get_model_path(model_name='my_second_model')\n",
         "    \n",
-        "    # deserialize the model files back into a sklearn model\n",
+        "    # Deserialize the model files back into scikit-learn models.\n",
         "    model_1 = joblib.load(model_1_path)\n",
         "    model_2 = joblib.load(model_2_path)\n",
         "\n",
-        "# note you can pass in multiple rows for scoring\n",
+        "# Note you can pass in multiple rows for scoring.\n",
         "def run(raw_data):\n",
         "    try:\n",
         "        data = json.loads(raw_data)['data']\n",
@@ -177,7 +206,7 @@
         "        result_1 = model_1.predict(data)\n",
         "        result_2 = model_2.predict(data)\n",
         "\n",
-        "        # you can return any data type as long as it is JSON-serializable\n",
+        "        # You can return any JSON-serializable value.\n",
         "        return {\"prediction1\": result_1.tolist(), \"prediction2\": result_2.tolist()}\n",
         "    except Exception as e:\n",
         "        result = str(e)\n",
@@ -208,10 +237,10 @@
       "source": [
         "from azureml.core import Environment\n",
         "\n",
-        "env = Environment.from_conda_specification(name='deploytocloudenv', file_path='myenv.yml')\n",
-        "\n",
-        "# This is optional at this point\n",
-        "# env.register(workspace=ws)"
+        "env = Environment(\"deploytocloudenv\")\n",
+        "env.python.conda_dependencies.add_pip_package(\"joblib\")\n",
+        "env.python.conda_dependencies.add_pip_package(\"numpy\")\n",
+        "env.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
       ]
     },
     {
@@ -281,25 +310,15 @@
       },
       "outputs": [],
       "source": [
-        "from azureml.core.webservice import AciWebservice, Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
+        "from azureml.core.webservice import AciWebservice\n",
+        "\n",
+        "aci_service_name = \"aciservice-multimodel\"\n",
         "\n",
         "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
-        "aci_service_name = 'aciservice-multimodel'\n",
-        "\n",
-        "try:\n",
-        "    # if you want to get existing service below is the command\n",
-        "    # since aci name needs to be unique in subscription deleting existing aci if any\n",
-        "    # we use aci_service_name to create azure aci\n",
-        "    service = Webservice(ws, name=aci_service_name)\n",
-        "    if service:\n",
-        "        service.delete()\n",
-        "except WebserviceException as e:\n",
-        "    print()\n",
-        "\n",
-        "service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config)\n",
         "\n",
+        "service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config, overwrite=True)\n",
         "service.wait_for_deployment(True)\n",
+        "\n",
         "print(service.state)"
       ]
     },
@@ -317,13 +336,11 @@
       "outputs": [],
       "source": [
         "import json\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,2,3,4,5,6,7,8,9,10], \n",
-        "    [10,9,8,7,6,5,4,3,2,1]\n",
-        "]})\n",
         "\n",
-        "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
-        "prediction = service.run(input_data=test_sample_encoded)\n",
+        "test_sample = json.dumps({'data': x[0:2].tolist()})\n",
+        "\n",
+        "prediction = service.run(test_sample)\n",
+        "\n",
         "print(prediction)"
       ]
     },

how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.yml

@@ -2,3 +2,5 @@ name: multi-model-register-and-deploy
 dependencies:
 - pip:
   - azureml-sdk
+  - numpy
+  - scikit-learn

how-to-use-azureml/deployment/deploy-multi-model/myenv.yml

@@ -1,8 +0,0 @@
-name: project_environment
-dependencies:
-  - python=3.6.2
-  - pip:
-    - azureml-defaults
-    - scikit-learn
-    - numpy
-    - inference-schema[numpy-support]