x-beagle-DeployEndpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a83e7018",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sagemaker, subprocess, boto3\n",
    "from datetime import datetime\n",
    "from sagemaker import s3\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.pytorch import PyTorchModel\n",
    "from sagemaker.deserializers import JSONDeserializer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1872fad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting code/requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile code/requirements.txt\n",
    "nvgpu\n",
    "opencv-python\n",
    "torchvision\n",
    "seaborn\n",
    "ultralytics\n",
    "omegaconf==2.3.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3f63b246",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting code/inference.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile code/inference.py\n",
    "import numpy as np\n",
    "import torch, os, json, io, cv2, time\n",
    "from ultralytics import YOLO\n",
    "\n",
    "def model_fn(model_dir):\n",
    "    print(\"Executing model_fn from inference.py ...\")\n",
    "    env = os.environ\n",
    "    model = YOLO(os.path.join(model_dir, env['YOLOV8_MODEL']))\n",
    "    return model\n",
    "\n",
    "def input_fn(request_body, request_content_type):\n",
    "    print(\"Executing input_fn from inference.py ...\")\n",
    "    if request_content_type:\n",
    "        jpg_original = np.load(io.BytesIO(request_body), allow_pickle=True)\n",
    "        jpg_as_np = np.frombuffer(jpg_original, dtype=np.uint8)\n",
    "        img = cv2.imdecode(jpg_as_np, flags=-1)\n",
    "    else:\n",
    "        raise Exception(\"Unsupported content type: \" + request_content_type)\n",
    "    return img\n",
    "    \n",
    "def predict_fn(input_data, model):\n",
    "    print(\"Executing predict_fn from inference.py ...\")\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "    model.to(device)\n",
    "    with torch.no_grad():\n",
    "        result = model(input_data)\n",
    "    return result\n",
    "        \n",
    "def output_fn(prediction_output, content_type):\n",
    "    print(\"Executing output_fn from inference.py ...\")\n",
    "    infer = {}\n",
    "    for result in prediction_output:\n",
    "        if 'boxes' in result._keys and result.boxes is not None:\n",
    "            infer['boxes'] = result.boxes.numpy().data.tolist()\n",
    "        if 'masks' in result._keys and result.masks is not None:\n",
    "            infer['masks'] = result.masks.numpy().data.tolist()\n",
    "        if 'keypoints' in result._keys and result.keypoints is not None:\n",
    "            infer['keypoints'] = result.keypoints.numpy().data.tolist()\n",
    "        if 'probs' in result._keys and result.probs is not None:\n",
    "            infer['probs'] = result.probs.numpy().data.tolist()\n",
    "    return json.dumps(infer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cf1b2eca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ultralytics in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (8.0.196)\n",
      "Requirement already satisfied: matplotlib>=3.3.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (3.8.3)\n",
      "Requirement already satisfied: numpy>=1.22.2 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (1.26.4)\n",
      "Requirement already satisfied: opencv-python>=4.6.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (4.9.0.80)\n",
      "Requirement already satisfied: pillow>=7.1.2 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (10.2.0)\n",
      "Requirement already satisfied: pyyaml>=5.3.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (6.0.1)\n",
      "Requirement already satisfied: requests>=2.23.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (2.31.0)\n",
      "Requirement already satisfied: scipy>=1.4.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (1.12.0)\n",
      "Requirement already satisfied: torch>=1.8.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (2.1.0)\n",
      "Requirement already satisfied: torchvision>=0.9.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (0.16.0)\n",
      "Requirement already satisfied: tqdm>=4.64.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (4.66.2)\n",
      "Requirement already satisfied: pandas>=1.1.4 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (1.5.3)\n",
      "Requirement already satisfied: seaborn>=0.11.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (0.13.2)\n",
      "Requirement already satisfied: psutil in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (5.9.8)\n",
      "Requirement already satisfied: py-cpuinfo in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (9.0.0)\n",
      "Requirement already satisfied: thop>=0.1.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from ultralytics) (0.1.1.post2209072238)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from matplotlib>=3.3.0->ultralytics) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from matplotlib>=3.3.0->ultralytics) (4.50.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from matplotlib>=3.3.0->ultralytics) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from matplotlib>=3.3.0->ultralytics) (21.3)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from matplotlib>=3.3.0->ultralytics) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from pandas>=1.1.4->ultralytics) (2024.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from requests>=2.23.0->ultralytics) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from requests>=2.23.0->ultralytics) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from requests>=2.23.0->ultralytics) (2.2.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from requests>=2.23.0->ultralytics) (2024.2.2)\n",
      "Requirement already satisfied: filelock in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (3.13.3)\n",
      "Requirement already satisfied: typing-extensions in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (4.10.0)\n",
      "Requirement already satisfied: sympy in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (1.12)\n",
      "Requirement already satisfied: networkx in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (3.2.1)\n",
      "Requirement already satisfied: jinja2 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (3.1.3)\n",
      "Requirement already satisfied: fsspec in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (2024.3.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n",
      "Requirement already satisfied: mpmath>=0.19 in /home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt to 'yolov8m.pt'...\n",
      "100%|██████████| 49.7M/49.7M [00:00<00:00, 399MB/s]\n"
     ]
    }
   ],
   "source": [
    "!pip3 install ultralytics\n",
    "from ultralytics import YOLO\n",
    "\n",
    "## Choose a model:\n",
    "model_name = 'yolov8m.pt'\n",
    "\n",
    "YOLO(model_name)\n",
    "os.system(f'mv {model_name} code/.')\n",
    "\n",
    "bashCommand = \"tar -cpzf  model.tar.gz code/\"\n",
    "process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)\n",
    "output, error = process.communicate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6caba3c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bucket: x-beagle\n"
     ]
    }
   ],
   "source": [
    "bucket = 'x-beagle'\n",
    "print(f'Bucket: {bucket}')\n",
    "\n",
    "sess = sagemaker.Session(default_bucket=bucket)\n",
    "\n",
    "prefix = \"yolov8/final-endpoint\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c879a119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model.tar.gz uploaded to S3 bucket x-beagle\n"
     ]
    }
   ],
   "source": [
    "import boto3\n",
    "\n",
    "# S3 버킷 이름\n",
    "bucket_name = 'x-beagle'\n",
    "# 업로드할 파일 이름\n",
    "file_name = 'model.tar.gz'\n",
    "# 업로드할 로컬 경로 및 파일 이름\n",
    "local_file_path = '/home/ec2-user/SageMaker/model.tar.gz'   \n",
    "\n",
    "# S3 클라이언트 생성\n",
    "s3_client = boto3.client('s3')\n",
    "\n",
    "# model.tar.gz 파일을 S3 버킷에 업로드\n",
    "s3_client.upload_file(local_file_path, bucket_name, file_name)\n",
    "\n",
    "print(f'{file_name} uploaded to S3 bucket {bucket_name}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a559681f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.pytorch import PyTorchModel\n",
    "from sagemaker.deserializers import JSONDeserializer\n",
    "\n",
    "# S3 버킷 이름과 업로드된 모델의 위치\n",
    "bucket_name = 'x-beagle'\n",
    "model_artifact = 'model.tar.gz'\n",
    "\n",
    "# SageMaker 실행 역할 가져오기\n",
    "role = get_execution_role()\n",
    "\n",
    "# SageMaker 세션 및 클라이언트 생성\n",
    "import sagemaker\n",
    "sagemaker_session = sagemaker.Session()\n",
    "sagemaker_client = boto3.client('sagemaker')\n",
    "\n",
    "# 모델 데이터의 S3 경로\n",
    "model_data = f's3://{bucket_name}/{model_artifact}'\n",
    "\n",
    "# PyTorch 모델 생성\n",
    "model = PyTorchModel(entry_point='inference.py',\n",
    "                     model_data=model_data, \n",
    "                     framework_version='1.12', \n",
    "                     py_version='py38',\n",
    "                     role=role,\n",
    "                     env={'TS_MAX_RESPONSE_SIZE':'20000000', 'YOLOV8_MODEL': model_name},\n",
    "                     sagemaker_session=sagemaker_session,\n",
    "                     name='xbeagle-model-pytorch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "47cf2334",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------!Endpoint Name: x-beagle-yolov8-2024-05-24-13-22-32-582715\n"
     ]
    }
   ],
   "source": [
    "# 엔드포인트 이름 생성\n",
    "ENDPOINT_NAME = 'x-beagle-yolov8-' + str(datetime.utcnow().strftime('%Y-%m-%d-%H-%M-%S-%f'))\n",
    "\n",
    "# 엔드포인트 배포\n",
    "predictor = model.deploy(initial_instance_count=1, \n",
    "                         instance_type='ml.m5.4xlarge',\n",
    "                         deserializer=JSONDeserializer(),\n",
    "                         endpoint_name=ENDPOINT_NAME)\n",
    "\n",
    "print(f'Endpoint Name: {ENDPOINT_NAME}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ba65c03f",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor_reuse=sagemaker.predictor.Predictor(\n",
    "    endpoint_name=\"xbeagle-yolov8-YYYY-MM-DD-HH-MM-SS-SSS\",\n",
    "    sagemaker_session=sagemaker.Session(),\n",
    "    serializer=sagemaker.serializers.CSVSerializer()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f5e8ebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'ENDPOINT_NAME' (str)\n",
      "Endpoint Name: xbeagle-yolov8-2024-05-24-05-31-12-296466\n",
      "---------!"
     ]
    }
   ],
   "source": [
    "INSTANCE_TYPE = 'ml.g4dn.4xlarge'\n",
    "ENDPOINT_NAME = 'xbeagle-yolov8-' + str(datetime.utcnow().strftime('%Y-%m-%d-%H-%M-%S-%f'))\n",
    "%store ENDPOINT_NAME\n",
    "print(f'Endpoint Name: {ENDPOINT_NAME}')\n",
    "\n",
    "predictor = model.deploy(initial_instance_count=1, \n",
    "                         instance_type=INSTANCE_TYPE,\n",
    "                         deserializer=JSONDeserializer(),\n",
    "                         endpoint_name=ENDPOINT_NAME)"
   ]
  }
 ],
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