sdk/python/endpoints/online/managed/online-endpoints-multimodel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a multimodel deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we serve two models from the same endpoint and same deployment. \n",
    "\n",
    "Both model files are registered as a single model asset on Azure and loaded simultaneously in the scoring script. The scoring script parses each request for a \"model\" field and routes the payload accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Configure parameters, assets, and clients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Set workspace details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subscription_id = \"<SUBSCRIPTION_ID>\"\n",
    "resource_group = \"<RESOURCE_GROUP>\"\n",
    "workspace_name = \"<AML_WORKSPACE_NAME>\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Set endpoint details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "endpoint_name = f\"multimod-{random.randint(0,10000)}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Set asset paths\n",
    "Define the directories containing the two model files as well as a directory which contains the scoring script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "base_path = \"../../../../../cli/endpoints/online/custom-container/minimal/multimodel\"\n",
    "conda_file_path = (\n",
    "    \"../../../assets/environment/conda-yamls/online-endpoints-multimodel.yml\"\n",
    ")\n",
    "models_path = os.path.join(base_path, \"models\")\n",
    "code_path = os.path.join(base_path, \"code\")\n",
    "test_data_path = os.path.join(base_path, \"test-data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Examine the models folder\n",
    "The models folder contains two models which will be loaded simultaneously by the scoring script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.listdir(models_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Examine the scoring script\n",
    "\n",
    "The scoring script loads both models into a dictionary keyed on their name in the `init` function. In the run function, each request is parsed for a `model` key in the JSON to choose the model. The `data` payload is then passed to the appropriate model.\n",
    "\n",
    "```python \n",
    "import joblib\n",
    "import os\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import json\n",
    "\n",
    "models = None\n",
    "\n",
    "\n",
    "def init():\n",
    "    global models\n",
    "    model_dir = Path(os.getenv(\"AZUREML_MODEL_DIR\")) / \"models\"\n",
    "    models = {m[:-4]: joblib.load(model_dir / m) for m in os.listdir(model_dir)}\n",
    "\n",
    "\n",
    "def run(data):\n",
    "    data = json.loads(data)\n",
    "    model = models[data[\"model\"]]\n",
    "    payload = pd.DataFrame(data[\"data\"])\n",
    "    try:\n",
    "        ret = model.predict(payload)\n",
    "        return pd.DataFrame(ret).to_json()\n",
    "    except KeyError:\n",
    "        raise KeyError(\"No such model\")\n",
    "\n",
    "``` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 Examine the Conda file\n",
    "The dockerfile is located at `sdk/python/assets/environment/conda-yamls/online-endpoints-multimodel.yml`\n",
    "\n",
    "```yaml\n",
    "name: multimodel\n",
    "channels:\n",
    "  - defaults\n",
    "dependencies: \n",
    "  - python=3.8\n",
    "  - pip\n",
    "  - pip: \n",
    "    - pandas \n",
    "    - numpy\n",
    "    - scikit-learn\n",
    "    - joblib\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.7 Create an MLClient instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.ai.ml import MLClient\n",
    "from azure.ai.ml.entities import (\n",
    "    ManagedOnlineEndpoint,\n",
    "    ManagedOnlineDeployment,\n",
    "    Model,\n",
    "    CodeConfiguration,\n",
    "    Environment,\n",
    "    BuildContext,\n",
    "    ProbeSettings,\n",
    ")\n",
    "from azure.identity import DefaultAzureCredential\n",
    "\n",
    "credential = DefaultAzureCredential()\n",
    "ml_client = MLClient(\n",
    "    credential,\n",
    "    subscription_id=subscription_id,\n",
    "    resource_group_name=resource_group,\n",
    "    workspace_name=workspace_name,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "credential = DefaultAzureCredential()\n",
    "ml_client = MLClient(\n",
    "    credential,\n",
    "    subscription_id=subscription_id,\n",
    "    resource_group_name=resource_group,\n",
    "    workspace_name=workspace_name,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Create an endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define and create the endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint = ManagedOnlineEndpoint(name=endpoint_name)\n",
    "poller = ml_client.online_endpoints.begin_create_or_update(endpoint)\n",
    "poller.wait()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Confirm that creation was successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.ai.ml.exceptions import DeploymentException\n",
    "\n",
    "status = poller.status()\n",
    "if status != \"Succeeded\":\n",
    "    raise DeploymentException(status)\n",
    "else:\n",
    "    print(\"Endpoint creation succeeded\")\n",
    "    endpoint = poller.result()\n",
    "    print(endpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Create the deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Create the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "environment = Environment(\n",
    "    name=\"minimal-multimodel-conda\",\n",
    "    image=\"mcr.microsoft.com/azureml/minimal-ubuntu20.04-py38-cpu-inference\",\n",
    "    conda_file=conda_file_path,\n",
    ")\n",
    "environment = ml_client.environments.create_or_update(environment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Define the deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deployment = ManagedOnlineDeployment(\n",
    "    name=\"custom-container-multimodel\",\n",
    "    endpoint_name=endpoint_name,\n",
    "    model=Model(name=\"minimal-multimodel\", path=models_path),\n",
    "    code_configuration=CodeConfiguration(\n",
    "        code=code_path, scoring_script=\"minimal-multimodel-score.py\"\n",
    "    ),\n",
    "    environment=f\"azureml:{environment.name}:{environment.version}\",\n",
    "    instance_type=\"Standard_DS3_v2\",\n",
    "    instance_count=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Create the deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "poller = ml_client.online_deployments.begin_create_or_update(deployment)\n",
    "poller.wait()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Confirm that creation was successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "status = poller.status()\n",
    "if status != \"Succeeded\":\n",
    "    raise DeploymentException(status)\n",
    "else:\n",
    "    print(\"Deployment creation succeeded\")\n",
    "    deployment = poller.result()\n",
    "    print(deployment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Set traffic to 100% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint.traffic = {\"custom-container-multimodel\": 100}\n",
    "poller = ml_client.begin_create_or_update(endpoint)\n",
    "poller.wait()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Test the endpoint\n",
    "The `model` JSON field in both JSON payloads indicates which model to score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Test the diabetes model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "res = ml_client.online_endpoints.invoke(\n",
    "    endpoint_name, request_file=os.path.join(test_data_path, \"diabetes-test-data.json\")\n",
    ")\n",
    "print(json.loads(res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Test the iris model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = ml_client.online_endpoints.invoke(\n",
    "    endpoint_name, request_file=os.path.join(test_data_path, \"iris-test-data.json\")\n",
    ")\n",
    "print(json.loads(res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Delete assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Delete the endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "poller = ml_client.online_endpoints.begin_delete(name=endpoint_name)"
   ]
  }
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