04b_Approve_and_Deploy_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Approve and Deploy the Model\n",
    "\n",
    "The pipeline that was executed created a Model Package version within the specified Model Package Group. Of particular note, the registration of the model/creation of the Model Package was done so with approval status as `PendingManualApproval`.\n",
    "\n",
    "As part of SageMaker Pipelines, data scientists can register the model with approved/pending manual approval as part of the CI/CD workflow.\n",
    "\n",
    "We can also approve the model using the SageMaker Studio UI or programmatically as shown below.\n",
    "\n",
    "![Pipeline](img/generative_ai_pipeline_rlhf_plus.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name='1'></a>\n",
    "## Set up Kernel and Required Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "First, check that the correct kernel is chosen.\n",
    "\n",
    "<img src=\"img/kernel_set_up.png\" width=\"300\"/>\n",
    "\n",
    "You can click on that to see and check the details of the image, kernel, and instance type.\n",
    "\n",
    "<img src=\"img/w3_kernel_and_instance_type.png\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import psutil\n",
    "\n",
    "notebook_memory = psutil.virtual_memory()\n",
    "print(notebook_memory)\n",
    "\n",
    "if notebook_memory.total < 32 * 1000 * 1000 * 1000:\n",
    "    print('*******************************************')    \n",
    "    print('YOU ARE NOT USING THE CORRECT INSTANCE TYPE')\n",
    "    print('PLEASE CHANGE INSTANCE TYPE TO  m5.2xlarge ')\n",
    "    print('*******************************************')\n",
    "else:\n",
    "    correct_instance_type=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from botocore.exceptions import ClientError\n",
    "\n",
    "import os\n",
    "import sagemaker\n",
    "import logging\n",
    "import boto3\n",
    "import sagemaker\n",
    "\n",
    "sess = sagemaker.Session()\n",
    "bucket = sess.default_bucket()\n",
    "region = boto3.Session().region_name\n",
    "\n",
    "import botocore.config\n",
    "\n",
    "config = botocore.config.Config(\n",
    "    user_agent_extra='dsoaws/2.0'\n",
    ")\n",
    "\n",
    "sm = boto3.Session().client(service_name=\"sagemaker\", \n",
    "                            region_name=region,\n",
    "                            config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%store -r role"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List Pipeline Execution Steps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%store -r pipeline_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(pipeline_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "import time\n",
    "from pprint import pprint\n",
    "\n",
    "executions_response = sm.list_pipeline_executions(PipelineName=pipeline_name)[\"PipelineExecutionSummaries\"]\n",
    "pipeline_execution_status = executions_response[0][\"PipelineExecutionStatus\"]\n",
    "print(pipeline_execution_status)\n",
    "\n",
    "while pipeline_execution_status == \"Executing\":\n",
    "    try:\n",
    "        executions_response = sm.list_pipeline_executions(PipelineName=pipeline_name)[\"PipelineExecutionSummaries\"]\n",
    "        pipeline_execution_status = executions_response[0][\"PipelineExecutionStatus\"]\n",
    "    except Exception as e:\n",
    "        print(\"Please wait...\")\n",
    "        time.sleep(30)\n",
    "\n",
    "pprint(executions_response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pipeline_execution_status = executions_response[0][\"PipelineExecutionStatus\"]\n",
    "print(pipeline_execution_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pipeline_execution_arn = executions_response[0][\"PipelineExecutionArn\"]\n",
    "print(pipeline_execution_arn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "steps = sm.list_pipeline_execution_steps(PipelineExecutionArn=pipeline_execution_arn)\n",
    "\n",
    "pprint(steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# View Registered Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for execution_step in steps[\"PipelineExecutionSteps\"]:\n",
    "    if execution_step[\"StepName\"] == \"Summarization-RegisterModel\":\n",
    "        model_package_arn = execution_step[\"Metadata\"][\"RegisterModel\"][\"Arn\"]\n",
    "        break\n",
    "print(model_package_arn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Approve the Model for Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_package_update_response = sm.update_model_package(\n",
    "    ModelPackageArn=model_package_arn,\n",
    "    ModelApprovalStatus=\"Approved\",  # Other options are Rejected and PendingManualApproval\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# View Created Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for execution_step in steps[\"PipelineExecutionSteps\"]:\n",
    "    if execution_step[\"StepName\"] == \"CreateModel\":\n",
    "        model_arn = execution_step[\"Metadata\"][\"Model\"][\"Arn\"]\n",
    "        break\n",
    "print(model_arn)\n",
    "\n",
    "pipeline_model_name = model_arn.split(\"/\")[-1]\n",
    "print(pipeline_model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Model Endpoint from Model Registry\n",
    "More details here:  https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-deploy.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "timestamp = int(time.time())\n",
    "\n",
    "model_from_registry_name = \"model-from-registry-{}\".format(timestamp)\n",
    "print(\"Model from registry name : {}\".format(model_from_registry_name))\n",
    "\n",
    "model_registry_package_container = {\n",
    "    \"ModelPackageName\": model_package_arn,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "create_model_from_registry_response = sm.create_model(\n",
    "    ModelName=model_from_registry_name, ExecutionRoleArn=role, PrimaryContainer=model_registry_package_container\n",
    ")\n",
    "pprint(create_model_from_registry_response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_from_registry_arn = create_model_from_registry_response[\"ModelArn\"]\n",
    "model_from_registry_arn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: In this workshop, we are intentionally deploying our model to only 1 instance. The general recommendation is to deploy to 2 or more instances for automatic placement across two AZs for high availability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "endpoint_config_name = \"model-from-registry-epc-{}\".format(timestamp)\n",
    "print(endpoint_config_name)\n",
    "\n",
    "create_endpoint_config_response = sm.create_endpoint_config(\n",
    "    EndpointConfigName=endpoint_config_name,\n",
    "    ProductionVariants=[\n",
    "        {\n",
    "            \"InstanceType\": \"ml.m5.4xlarge\",\n",
    "            \"InitialVariantWeight\": 1,\n",
    "            \"InitialInstanceCount\": 1,\n",
    "            \"ModelName\": pipeline_model_name,\n",
    "            \"VariantName\": \"AllTraffic\",\n",
    "        }\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%store -d pipeline_endpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Pick up the existing `pipeline_endpoint_name` if it was already created\n",
    "%store -r pipeline_endpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestamp = int(time.time())\n",
    "pipeline_endpoint_name = \"model-from-registry-ep-{}\".format(timestamp)\n",
    "print(\"Created Pipeline EndpointName={}\".format(pipeline_endpoint_name))\n",
    "\n",
    "create_endpoint_response = sm.create_endpoint(\n",
    "    EndpointName=pipeline_endpoint_name, EndpointConfigName=endpoint_config_name\n",
    ")\n",
    "print(create_endpoint_response[\"EndpointArn\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store pipeline_endpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "\n",
    "display(\n",
    "    HTML(\n",
    "        '<b>Review <a target=\"blank\" href=\"https://console.aws.amazon.com/sagemaker/home?region={}#/endpoints/{}\">SageMaker HTTPS Endpoint</a></b>'.format(\n",
    "            region, pipeline_endpoint_name\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# _Wait Until the Endpoint is Deployed_\n",
    "_Note:  This will take a few minutes.  Please be patient._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "waiter = sm.get_waiter(\"endpoint_in_service\")\n",
    "waiter.wait(EndpointName=pipeline_endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Release Resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%html\n",
    "\n",
    "<p><b>Shutting down your kernel for this notebook to release resources.</b></p>\n",
    "<button class=\"sm-command-button\" data-commandlinker-command=\"kernelmenu:shutdown\" style=\"display:none;\">Shutdown Kernel</button>\n",
    "        \n",
    "<script>\n",
    "try {\n",
    "    els = document.getElementsByClassName(\"sm-command-button\");\n",
    "    els[0].click();\n",
    "}\n",
    "catch(err) {\n",
    "    // NoOp\n",
    "}    \n",
    "</script>"
   ]
  }
 ],
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