{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fdad1f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.huggingface import HuggingFaceModel\n",
    "import sagemaker\n",
    "\n",
    "role = sagemaker.get_execution_role()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a691203",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hub Model configuration. https://huggingface.co/models\n",
    "hub = {\n",
    "    'HF_MODEL_ID':'distilbert-base-uncased',\n",
    "    'HF_TASK':'text-classification'\n",
    "}\n",
    "\n",
    "# create Hugging Face Model Class\n",
    "huggingface_model = HuggingFaceModel(\n",
    "    transformers_version='4.17.0',\n",
    "    pytorch_version='1.10.2',\n",
    "    py_version='py38',\n",
    "    env=hub,\n",
    "    role=role, \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9624bf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----!"
     ]
    }
   ],
   "source": [
    "# deploy model to SageMaker Inference\n",
    "predictor = huggingface_model.deploy(\n",
    "    initial_instance_count=1, # number of instances\n",
    "    instance_type='ml.m5.12xlarge' # ec2 instance type\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b24b113",
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint_name = predictor.endpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a01dd78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'LABEL_0', 'score': 0.528241753578186}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.predict({\n",
    "    'inputs': \"I am super happy right now.\"\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1270b5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'[{\"label\":\"LABEL_1\",\"score\":0.507586658000946}]'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import boto3\n",
    "import json\n",
    "client = boto3.client('sagemaker-runtime')\n",
    "content_type = \"application/json\"\n",
    "request_body = {'inputs': \"I am super happy right now.\"}\n",
    "data = json.loads(json.dumps(request_body))\n",
    "payload = json.dumps(data)\n",
    "response = client.invoke_endpoint(\n",
    "    EndpointName=predictor.endpoint_name,\n",
    "    ContentType=content_type,\n",
    "    Body=payload)\n",
    "result = response['Body'].read()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "10122976",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install locust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4c405d54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/bin/locust\n"
     ]
    }
   ],
   "source": [
    "!which locust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4aeee5b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash -s \"$endpoint_name\"\n",
    "cd locust\n",
    "./distributed.sh $1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcd93aa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "locust_data = pd.read_csv('locust/results_stats.csv')\n",
    "for index, row in locust_data.head(n=2).iterrows():\n",
    "     print(index, row)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "conda_python3",
   "language": "python",
   "name": "conda_python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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