create-ep03-mistral2.ipynb

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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## Mistral v2 endpoint (This notebook is for a reference. We don't need to create it again.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4dc49792-7344-45df-a116-3706ebf5a0c8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "autovizwidget 0.21.0 requires pandas<2.0.0,>=0.20.1, but you have pandas 2.1.3 which is incompatible.\n",
      "awscli 1.29.80 requires botocore==1.31.80, but you have botocore 1.32.3 which is incompatible.\n",
      "hdijupyterutils 0.21.0 requires pandas<2.0.0,>=0.17.1, but you have pandas 2.1.3 which is incompatible.\n",
      "sparkmagic 0.21.0 requires pandas<2.0.0,>=0.17.1, but you have pandas 2.1.3 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "autovizwidget 0.21.0 requires pandas<2.0.0,>=0.20.1, but you have pandas 2.1.3 which is incompatible.\n",
      "hdijupyterutils 0.21.0 requires pandas<2.0.0,>=0.17.1, but you have pandas 2.1.3 which is incompatible.\n",
      "sparkmagic 0.21.0 requires pandas<2.0.0,>=0.17.1, but you have pandas 2.1.3 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install sagemaker --quiet --upgrade --force-reinstall\n",
    "!pip install ipywidgets==7.0.0 --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd0ffa9b-be15-40ca-a736-2c908a01be1e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_id, model_version = \"huggingface-llm-mistral-7b\", \"*\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "51626949-0be1-498f-9b6e-91c54f809a19",
   "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: /home/ec2-user/.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: /home/ec2-user/.config/sagemaker/config.yaml\n",
      "\u001b[1maws_region:\u001b[0m us-west-2\n",
      "\u001b[1maws_role:\u001b[0m arn:aws:iam::571667364805:role/service-role/AmazonSageMaker-ExecutionRole-20231103T080028\n",
      "\u001b[1moutput_bucket:\u001b[0m sagemaker-us-west-2-571667364805\n"
     ]
    }
   ],
   "source": [
    "import boto3\n",
    "import sagemaker\n",
    "import json\n",
    "import re \n",
    "\n",
    "# Get current region, role, and default bucket\n",
    "aws_region = boto3.Session().region_name\n",
    "aws_role = sagemaker.session.Session().get_caller_identity_arn()\n",
    "output_bucket = sagemaker.Session().default_bucket()\n",
    "\n",
    "# This will be useful for printing\n",
    "newline, bold, unbold = \"\\n\", \"\\033[1m\", \"\\033[0m\"\n",
    "\n",
    "print(f\"{bold}aws_region:{unbold} {aws_region}\")\n",
    "print(f\"{bold}aws_role:{unbold} {aws_role}\")\n",
    "print(f\"{bold}output_bucket:{unbold} {output_bucket}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c8f7bc0-7e2a-4af3-8f6e-1835d06bdf34",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "template = {\n",
    "    \"prompt\": \"Below is an instruction that describes a task, paired with an input that provides further context. \"\n",
    "    \"Write a response that appropriately completes the request.\\n\\n\"\n",
    "    \"### Instruction:\\n{instruction}\\n\\n### Input:\\n{context}\\n\\n\",\n",
    "    \"completion\": \" {response}\",\n",
    "}\n",
    "with open(\"template.json\", \"w\") as f:\n",
    "    json.dump(template, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ceb9d2c-2977-4f13-84c6-24cef36a6258",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------!"
     ]
    }
   ],
   "source": [
    "from sagemaker.jumpstart.model import JumpStartModel\n",
    "\n",
    "model = JumpStartModel(model_id=model_id, model_version=model_version)\n",
    "predictor = model.deploy()"
   ]
  }
 ],
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