{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0428530b-8f42-4ab7-83ce-c3bdfa96a51b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install sagemaker>=2.143.0 -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f94c4e8-de95-4657-97e0-dc213415bee5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker, boto3, json\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.pytorch.model import PyTorchModel\n",
    "from sagemaker.huggingface import HuggingFace\n",
    "\n",
    "role = get_execution_role()\n",
    "region = boto3.Session().region_name\n",
    "sess = sagemaker.Session()\n",
    "bucket = sess.default_bucket()\n",
    "\n",
    "sagemaker.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a08d3c5f-2537-433b-a7c9-e2da3480a5f4",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "%cd scripts\n",
    "!tar -czvf ../package.tar.gz *\n",
    "%cd -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11d0ae99-8cfe-43f5-8677-863e91bce7a0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_path = sess.upload_data('package.tar.gz', bucket=bucket, key_prefix=f\"Cerebras\")\n",
    "model_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a20f1592-7ff0-45a2-bbb7-3f9528a11a98",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.async_inference import AsyncInferenceConfig\n",
    "from sagemaker.serializers import JSONSerializer\n",
    "\n",
    "endpoint_name = \"Cerebras\"\n",
    "\n",
    "huggingface_model = PyTorchModel(\n",
    "    model_data=model_path,\n",
    "    framework_version=\"1.13\",\n",
    "    py_version='py39',\n",
    "    role=role,\n",
    "    name=endpoint_name,\n",
    "    env={\n",
    "        \"base_model\": \"cerebras/Cerebras-GPT-2.7B\",\n",
    "        \"peft\": \"False\",\n",
    "        \"SAGEMAKER_MODEL_SERVER_TIMEOUT\": \"3600\"\n",
    "    }\n",
    ")\n",
    "\n",
    "# deploy model to SageMaker Inference\n",
    "predictor = huggingface_model.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type='ml.g5.2xlarge',\n",
    "    endpoint_name=endpoint_name,\n",
    "    serializer=JSONSerializer(),\n",
    "    async_inference_config=AsyncInferenceConfig()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6eb4904-4b77-4cd0-bbe9-4d4685bc018e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.predictor import Predictor\n",
    "from sagemaker.predictor_async import AsyncPredictor\n",
    "from sagemaker.serializers import JSONSerializer\n",
    "from sagemaker.deserializers import NumpyDeserializer\n",
    "\n",
    "predictor_client = AsyncPredictor(\n",
    "    predictor=Predictor(\n",
    "        endpoint_name=endpoint_name,\n",
    "        sagemaker_session=sess,\n",
    "        serializer=JSONSerializer(),\n",
    "        deserializer=NumpyDeserializer()\n",
    "    ),\n",
    "    name=endpoint_name\n",
    ")\n",
    "data = {\n",
    "    \"input\": \"雨が降る時は\",\n",
    "    \"max_new_tokens\": 512,\n",
    "    # \"temperature\": 0.5\n",
    "}\n",
    "response = predictor_client.predict(\n",
    "    data=data\n",
    ")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cd9be37-f988-4394-b920-61d1cfeaf066",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "predictor.delete_model()\n",
    "predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dca3e9ef-793e-4d30-8900-6396432d49d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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