deploy_sbert_batch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ece7719-b982-4d20-ac68-571e3650dda2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sagemaker in /opt/conda/lib/python3.10/site-packages (2.194.0)\n",
      "Requirement already satisfied: attrs<24,>=23.1.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (23.1.0)\n",
      "Requirement already satisfied: boto3<2.0,>=1.26.131 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.26.132)\n",
      "Requirement already satisfied: cloudpickle==2.2.1 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (2.2.1)\n",
      "Requirement already satisfied: google-pasta in /opt/conda/lib/python3.10/site-packages (from sagemaker) (0.2.0)\n",
      "Requirement already satisfied: numpy<2.0,>=1.9.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.23.5)\n",
      "Requirement already satisfied: protobuf<5.0,>=3.12 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (3.20.3)\n",
      "Requirement already satisfied: smdebug-rulesconfig==1.0.1 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.0.1)\n",
      "Requirement already satisfied: importlib-metadata<7.0,>=1.4.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (4.13.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (23.1)\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from sagemaker) (2.0.1)\n",
      "Requirement already satisfied: pathos in /opt/conda/lib/python3.10/site-packages (from sagemaker) (0.3.0)\n",
      "Requirement already satisfied: schema in /opt/conda/lib/python3.10/site-packages (from sagemaker) (0.7.5)\n",
      "Requirement already satisfied: PyYAML~=6.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (6.0.1)\n",
      "Requirement already satisfied: jsonschema in /opt/conda/lib/python3.10/site-packages (from sagemaker) (4.17.3)\n",
      "Requirement already satisfied: platformdirs in /opt/conda/lib/python3.10/site-packages (from sagemaker) (3.5.0)\n",
      "Requirement already satisfied: tblib==1.7.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker) (1.7.0)\n",
      "Requirement already satisfied: botocore<1.30.0,>=1.29.132 in /opt/conda/lib/python3.10/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (1.29.132)\n",
      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.10/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (1.0.1)\n",
      "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /opt/conda/lib/python3.10/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (0.6.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.10/site-packages (from importlib-metadata<7.0,>=1.4.0->sagemaker) (3.15.0)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from google-pasta->sagemaker) (1.16.0)\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/lib/python3.10/site-packages (from jsonschema->sagemaker) (0.19.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas->sagemaker) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->sagemaker) (2023.3)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas->sagemaker) (2023.3)\n",
      "Requirement already satisfied: ppft>=1.7.6.6 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (1.7.6.6)\n",
      "Requirement already satisfied: dill>=0.3.6 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (0.3.6)\n",
      "Requirement already satisfied: pox>=0.3.2 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (0.3.2)\n",
      "Requirement already satisfied: multiprocess>=0.70.14 in /opt/conda/lib/python3.10/site-packages (from pathos->sagemaker) (0.70.14)\n",
      "Requirement already satisfied: contextlib2>=0.5.5 in /opt/conda/lib/python3.10/site-packages (from schema->sagemaker) (21.6.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /opt/conda/lib/python3.10/site-packages (from botocore<1.30.0,>=1.29.132->boto3<2.0,>=1.26.131->sagemaker) (1.26.14)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install sagemaker --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3eab115c-f412-4a81-9fd7-9aa212abbd92",
   "metadata": {
    "tags": []
   },
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2.194.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sagemaker\n",
    "import boto3\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "sagemaker.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b9f2e28-5ef0-424f-807c-6bc39856b5de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Get:1 http://archive.ubuntu.com/ubuntu focal InRelease [265 kB]\n",
      "Get:2 http://archive.ubuntu.com/ubuntu focal-updates InRelease [114 kB]        \n",
      "Get:3 http://archive.ubuntu.com/ubuntu focal-backports InRelease [108 kB]\n",
      "Get:4 http://security.ubuntu.com/ubuntu focal-security InRelease [114 kB]\n",
      "Get:5 http://archive.ubuntu.com/ubuntu focal/universe amd64 Packages [11.3 MB]\n",
      "Get:6 http://archive.ubuntu.com/ubuntu focal/restricted amd64 Packages [33.4 kB]\n",
      "Get:7 http://archive.ubuntu.com/ubuntu focal/main amd64 Packages [1275 kB]\n",
      "Get:8 http://archive.ubuntu.com/ubuntu focal/multiverse amd64 Packages [177 kB]\n",
      "Get:9 http://archive.ubuntu.com/ubuntu focal-updates/restricted amd64 Packages [3092 kB]\n",
      "Get:10 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 Packages [3633 kB]\n",
      "Get:11 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 Packages [1421 kB]\n",
      "Get:12 http://archive.ubuntu.com/ubuntu focal-updates/multiverse amd64 Packages [32.0 kB]\n",
      "Get:13 http://archive.ubuntu.com/ubuntu focal-backports/universe amd64 Packages [28.6 kB]\n",
      "Get:14 http://archive.ubuntu.com/ubuntu focal-backports/main amd64 Packages [55.2 kB]\n",
      "Get:15 http://security.ubuntu.com/ubuntu focal-security/universe amd64 Packages [1117 kB]\n",
      "Get:16 http://security.ubuntu.com/ubuntu focal-security/main amd64 Packages [3145 kB]\n",
      "Get:17 http://security.ubuntu.com/ubuntu focal-security/restricted amd64 Packages [2942 kB]\n",
      "Get:18 http://security.ubuntu.com/ubuntu focal-security/multiverse amd64 Packages [29.3 kB]\n",
      "Fetched 28.9 MB in 2s (13.7 MB/s)                      \n",
      "Reading package lists... Done\n"
     ]
    }
   ],
   "source": [
    "!apt-get update -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e325b32-e912-45a8-9b39-990ade218380",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "curl: /opt/conda/lib/libcurl.so.4: no version information available (required by curl)\n",
      "Detected operating system as Ubuntu/focal.\n",
      "Checking for curl...\n",
      "Detected curl...\n",
      "Checking for gpg...\n",
      "Detected gpg...\n",
      "Detected apt version as 2.0.9\n",
      "Running apt-get update... done.\n",
      "Installing apt-transport-https... done.\n",
      "Installing /etc/apt/sources.list.d/github_git-lfs.list...curl: /opt/conda/lib/libcurl.so.4: no version information available (required by curl)\n",
      "done.\n",
      "Importing packagecloud gpg key... curl: /opt/conda/lib/libcurl.so.4: no version information available (required by curl)\n",
      "Packagecloud gpg key imported to /etc/apt/keyrings/github_git-lfs-archive-keyring.gpg\n",
      "done.\n",
      "Running apt-get update... done.\n",
      "\n",
      "The repository is setup! You can now install packages.\n"
     ]
    }
   ],
   "source": [
    "!curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ffe56a27-59e0-41fe-87f0-226cc7d7779c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "git is already the newest version (1:2.25.1-1ubuntu3.11).\n",
      "The following NEW packages will be installed:\n",
      "  git-lfs\n",
      "0 upgraded, 1 newly installed, 0 to remove and 103 not upgraded.\n",
      "Need to get 3316 kB of archives.\n",
      "After this operation, 11.1 MB of additional disk space will be used.\n",
      "Get:1 http://archive.ubuntu.com/ubuntu focal/universe amd64 git-lfs amd64 2.9.2-1 [3316 kB]\n",
      "Fetched 3316 kB in 0s (24.0 MB/s)\n",
      "debconf: delaying package configuration, since apt-utils is not installed\n",
      "Selecting previously unselected package git-lfs.\n",
      "(Reading database ... 40697 files and directories currently installed.)\n",
      "Preparing to unpack .../git-lfs_2.9.2-1_amd64.deb ...\n",
      "Unpacking git-lfs (2.9.2-1) ...\n",
      "Setting up git-lfs (2.9.2-1) ...\n"
     ]
    }
   ],
   "source": [
    "!apt-get install git-lfs git -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31175fd7-b573-4b12-aacd-a4d73b68ee30",
   "metadata": {
    "tags": []
   },
   "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",
      "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 role arn: arn:aws:iam::628152409662:role/service-role/AmazonSageMaker-ExecutionRole-20231009T120215\n",
      "sagemaker bucket: sagemaker-us-east-1-628152409662\n",
      "sagemaker session region: us-east-1\n"
     ]
    }
   ],
   "source": [
    "sess = sagemaker.Session()\n",
    "# sagemaker session bucket -> used for uploading data, models and logs\n",
    "# sagemaker will automatically create this bucket if it not exists\n",
    "sagemaker_session_bucket=None\n",
    "if sagemaker_session_bucket is None and sess is not None:\n",
    "    # set to default bucket if a bucket name is not given\n",
    "    sagemaker_session_bucket = sess.default_bucket()\n",
    "\n",
    "try:\n",
    "    role = sagemaker.get_execution_role()\n",
    "except ValueError:\n",
    "    iam = boto3.client('iam')\n",
    "    role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']\n",
    "\n",
    "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n",
    "\n",
    "print(f\"sagemaker role arn: {role}\")\n",
    "print(f\"sagemaker bucket: {sess.default_bucket()}\")\n",
    "print(f\"sagemaker session region: {sess.boto_region_name}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dff2929e-0263-4f5a-acf8-057d20e9c9c4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!mkdir code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7616ccd-b79f-4969-8682-fdfd03b453d8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting code/inference.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile code/inference.py\n",
    "\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# Helper: Mean Pooling - Take attention mask into account for correct averaging\n",
    "def mean_pooling(model_output, attention_mask):\n",
    "    token_embeddings = model_output[0] #First element of model_output contains all token embeddings\n",
    "    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n",
    "    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n",
    "\n",
    "\n",
    "def model_fn(model_dir):\n",
    "  # Load model from HuggingFace Hub\n",
    "  tokenizer = AutoTokenizer.from_pretrained(model_dir)\n",
    "  model = AutoModel.from_pretrained(model_dir)\n",
    "  return model, tokenizer\n",
    "\n",
    "def predict_fn(data, model_and_tokenizer):\n",
    "    # destruct model and tokenizer\n",
    "    model, tokenizer = model_and_tokenizer\n",
    "\n",
    "    # Tokenize sentences\n",
    "    sentences = data.pop(\"inputs\", data)\n",
    "    encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')\n",
    "\n",
    "    # Compute token embeddings\n",
    "    with torch.no_grad():\n",
    "        model_output = model(**encoded_input)\n",
    "\n",
    "    # Perform pooling\n",
    "    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])\n",
    "\n",
    "    # Normalize embeddings\n",
    "    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)\n",
    "\n",
    "    # # return dictonary, which will be json serializable\n",
    "    # return {\"vectors\": sentence_embeddings[0].tolist()}\n",
    "\n",
    "    # return for handling batch request\n",
    "    return {\"vectors\": sentence_embeddings.tolist()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45ebacc8-70d9-4d7c-86d2-4d2b894f3c45",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-us-east-1-628152409662/custom_inference/vietnamese-sbert/model.tar.gz'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repository = \"keepitreal/vietnamese-sbert\"\n",
    "model_id=repository.split(\"/\")[-1]\n",
    "s3_location=f\"s3://{sess.default_bucket()}/custom_inference/{model_id}/model.tar.gz\"\n",
    "s3_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c54aeb5-dbc4-4481-9303-68bc8b2f3da4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: Failed to call git rev-parse --git-dir: exit status 128 \n",
      "Git LFS initialized.\n"
     ]
    }
   ],
   "source": [
    "!git lfs install"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10d48f03-27a1-4a06-abfa-ab37b875de39",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'vietnamese-sbert'...\n",
      "remote: Enumerating objects: 30, done.\u001b[K\n",
      "remote: Total 30 (delta 0), reused 0 (delta 0), pack-reused 30\u001b[K\n",
      "Unpacking objects: 100% (30/30), 853.10 KiB | 768.00 KiB/s, done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://huggingface.co/$repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dc6df8f5-0900-49ed-ac9b-5cc8e151a1a7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!cp -r code/ $model_id/code/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5b8da1ee-8c47-4985-bd2c-02a496a60cab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/root/sbert-batch/vietnamese-sbert\n",
      "1_Pooling/\n",
      "1_Pooling/config.json\n",
      "README.md\n",
      "added_tokens.json\n",
      "bpe.codes\n",
      "code/\n",
      "code/.ipynb_checkpoints/\n",
      "code/.ipynb_checkpoints/inference-checkpoint.py\n",
      "code/inference.py\n",
      "config.json\n",
      "config_sentence_transformers.json\n",
      "eval/\n",
      "eval/similarity_evaluation_results.csv\n",
      "modules.json\n",
      "pytorch_model.bin\n",
      "sentence_bert_config.json\n",
      "special_tokens_map.json\n",
      "tokenizer_config.json\n",
      "vocab.txt\n"
     ]
    }
   ],
   "source": [
    "%cd $model_id\n",
    "!tar zcvf model.tar.gz *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c53a9577-e0ec-4c30-b0df-9cbd19f7d450",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upload: ./model.tar.gz to s3://sagemaker-us-east-1-628152409662/custom_inference/vietnamese-sbert/model.tar.gz\n"
     ]
    }
   ],
   "source": [
    "!aws s3 cp model.tar.gz $s3_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26043079-e01c-4d9c-bb3f-b7cfd5177a87",
   "metadata": {
    "tags": []
   },
   "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": [
    "\n",
    "\n",
    "from sagemaker.huggingface.model import HuggingFaceModel\n",
    "\n",
    "\n",
    "# create Hugging Face Model Class\n",
    "huggingface_model = HuggingFaceModel(\n",
    "   model_data=s3_location,       # path to your model and script\n",
    "   role=role,                    # iam role with permissions to create an Endpoint\n",
    "   transformers_version=\"4.12\",  # transformers version used\n",
    "   pytorch_version=\"1.9\",        # pytorch version used\n",
    "   py_version='py38',            # python version used\n",
    ")\n",
    "\n",
    "# deploy the endpoint endpoint\n",
    "predictor = huggingface_model.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type=\"ml.g4dn.xlarge\"\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ca22172-f34a-45bf-8dc0-50985cba5183",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "  \"inputs\": \"Cô giáo đang dạy học\",\n",
    "}\n",
    "\n",
    "res = predictor.predict(data=data)\n",
    "print(res)\n"
   ]
  }
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