05_finetune/11_test_inference_flan_t5_sagemaker_endpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n"
     ]
    }
   ],
   "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='gaia/1.0'\n",
    ")\n",
    "\n",
    "sm = boto3.Session().client(service_name=\"sagemaker\", \n",
    "                            region_name=region,\n",
    "                            config=config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieve model endpoint\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r pipeline_endpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    pipeline_endpoint_name\n",
    "except NameError:\n",
    "    print(\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")\n",
    "    print(\"[ERROR] Please run previous notebooks before you continue.\")\n",
    "    print(\"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model-from-registry-ep-1698266943\n"
     ]
    }
   ],
   "source": [
    "print(pipeline_endpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_934/2920223535.py:1: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
      "  from IPython.core.display import display, HTML\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<b>Review <a target=\"blank\" href=\"https://console.aws.amazon.com/sagemaker/home?region=us-west-2#/endpoints/model-from-registry-ep-1698266943\">SageMaker HTTPS Endpoint</a></b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 28.7 ms, sys: 5.11 ms, total: 33.8 ms\n",
      "Wall time: 1min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "waiter = sm.get_waiter(\"endpoint_in_service\")\n",
    "waiter.wait(EndpointName=pipeline_endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Wait Until the Endpoint ^^ Above ^^ is Deployed_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zero Shot Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tom's novel has won the Nobel Prize.\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "from sagemaker import Predictor\n",
    "\n",
    "zero_shot_prompt = \"\"\"Summarize the following conversation.\n",
    "\n",
    "#Person1#: Tom, I've got good news for you.\n",
    "#Person2#: What is it?\n",
    "#Person1#: Haven't you heard that your novel has won The Nobel Prize?\n",
    "#Person2#: Really? I can't believe it. It's like a dream come true. I never expected that I would win The Nobel Prize!\n",
    "#Person1#: You did a good job. I'm extremely proud of you.\n",
    "#Person2#: Thanks for the compliment.\n",
    "#Person1#: You certainly deserve it. Let's celebrate!\n",
    "\n",
    "Summary:\"\"\"\n",
    "predictor = Predictor(\n",
    "    endpoint_name=pipeline_endpoint_name,\n",
    "    sagemaker_session=sess,\n",
    ")\n",
    "response = predictor.predict(zero_shot_prompt,\n",
    "        {\n",
    "            \"ContentType\": \"application/x-text\",\n",
    "            \"Accept\": \"application/json\",\n",
    "        },\n",
    ")\n",
    "response_json = json.loads(response.decode('utf-8'))\n",
    "print(response_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean Up: Tear Down Endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# sm.delete_endpoint(\n",
    "#     EndpointName=pipeline_endpoint_name\n",
    "# )"
   ]
  }
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