mixtral_LMI-8bit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ca7ad78e-0e1b-44cc-bf0c-da66ebadbe47",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Deploy Mixtral with LMI\n",
    "## Hugging Face Accelerate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b71390-2c33-4ae9-a994-a06ac16da36f",
   "metadata": {},
   "source": [
    "### 8bit quantization\n",
    "See serving.properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dda41385-c9cd-4146-a5bb-15ddc4f5e759",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"mixtral-8bit\"\n",
    "model_filename = \"djl-hf-\" + model_name + \".tar.gz\"\n",
    "s3_prefix = \"djl-hf-\" + model_name\n",
    "instance_type = \"ml.g5.12xlarge\" #\"ml.g4dn.12xlarge\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1666a2a1-6559-4673-a84f-a8f81b748a9b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting sagemaker\n",
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      "Downloading sagemaker-2.200.1-py2.py3-none-any.whl (1.4 MB)\n",
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      "\u001b[?25hInstalling collected packages: urllib3, sagemaker\n",
      "  Attempting uninstall: urllib3\n",
      "    Found existing installation: urllib3 2.1.0\n",
      "    Uninstalling urllib3-2.1.0:\n",
      "      Successfully uninstalled urllib3-2.1.0\n",
      "  Attempting uninstall: sagemaker\n",
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      "    Uninstalling sagemaker-2.199.0:\n",
      "      Successfully uninstalled sagemaker-2.199.0\n",
      "\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",
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      "\u001b[0mSuccessfully installed sagemaker-2.200.1 urllib3-1.26.18\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"
     ]
    }
   ],
   "source": [
    "!pip install -U sagemaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94930898-bfc2-48c5-85ee-9572be253d7e",
   "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"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import sagemaker\n",
    "from sagemaker.model import Model\n",
    "from sagemaker import image_uris\n",
    "from sagemaker import serializers, deserializers\n",
    "\n",
    "role = sagemaker.get_execution_role()\n",
    "sess = sagemaker.session.Session()\n",
    "bucket = sess.default_bucket()\n",
    "region = sess._region_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "efc63ec8-0657-4419-bced-357851ef06d5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-us-west-2-261416220256/djl-hf-mixtral-8bit/djl-hf-mixtral-8bit.tar.gz'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# upload model code archive to S3\n",
    "!rm {model_filename} 2> /dev/null\n",
    "!tar -czf {model_filename} {s3_prefix}\n",
    "s3_artifact = sess.upload_data(model_filename, bucket, s3_prefix)\n",
    "s3_artifact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df735176-635d-4fd3-8f7e-0b8a76960325",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_model(_model_name, model_s3_url):\n",
    "    # Get the DJL DeepSpeed image uri\n",
    "    image_uri = image_uris.retrieve(\n",
    "        framework=\"djl-deepspeed\",\n",
    "        region=region,\n",
    "        version=\"0.25.0\"\n",
    "    )\n",
    "    model = Model(\n",
    "        image_uri=image_uri,\n",
    "        model_data=model_s3_url,\n",
    "        role=role,\n",
    "        name=_model_name,\n",
    "        sagemaker_session=sess,\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1096878-3848-420c-8d69-2c2c2e7a4938",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def deploy_model(model, _endpoint_name):\n",
    "    model.deploy(\n",
    "        initial_instance_count=1,\n",
    "        instance_type=instance_type,\n",
    "        endpoint_name=_endpoint_name,\n",
    "        container_startup_health_check_timeout=1800\n",
    "        #endpoint_logging=True\n",
    "    )\n",
    "    predictor = sagemaker.Predictor(\n",
    "        endpoint_name=_endpoint_name,\n",
    "        sagemaker_session=sess,\n",
    "        serializer=serializers.JSONSerializer(),\n",
    "        deserializer=deserializers.JSONDeserializer()\n",
    "    )\n",
    "    return predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff073761-1581-4096-8fad-a88af74a7829",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mixtral-8bit-20231218-081639'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endpoint_name = model_name + \"-\" + time.strftime(\"%Y%m%d-%H%M%S\")\n",
    "endpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d9640cf-9462-4291-82eb-a22557c172ed",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = create_model(endpoint_name, s3_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "907e68f1-156c-4e98-afb1-e5c84ce967b3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------!"
     ]
    }
   ],
   "source": [
    "predictor = deploy_model(model, endpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f493f31-fd13-4b06-a71f-c374d1fb1a76",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def test_model(prompt):\n",
    "    print(predictor.predict(\n",
    "        data={ \n",
    "            \"inputs\" : prompt,\n",
    "            \"parameters\": { \"max_length\": 100 }\n",
    "        }\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1b5a2679-2231-4451-b2f0-28c209f10528",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'generated_text': 'What is SageMaker LMI?\\n\\nSageMaker LMI is a tool that allows you to quickly and easily create, train, and deploy machine learning models on Amazon SageMaker. It provides a simple, intuitive interface for data scientists and developers to build, test, and deploy models without having to worry about the underlying infrastructure.\\n\\nSageMaker LMI supports a wide range of machine learning algorithms, including deep learning, computer vision, natural language'}]\n"
     ]
    }
   ],
   "source": [
    "test_model(\"What is SageMaker LMI?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb7df3cb-cb7e-4457-b1b6-e40d82cd4ab6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_model(\"What are the recommended steps to train for an AWS Solutions Architect certification?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fbf3a3d-9bc8-40e6-b6d4-aafe6604ad1b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_model(\"Name the stars in our solar system in order from fartherst to closest to the sun\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd24d00-8535-48bf-bb02-eabf8de69f08",
   "metadata": {},
   "source": [
    "### Cleanup Resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb23b5ae-0a75-485e-8d97-dfca451bd67a",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5aeef065-973a-4961-8073-c2eda4d3ac45",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.delete_model()"
   ]
  }
 ],
 "metadata": {
  "availableInstances": [
   {
    "_defaultOrder": 0,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 4,
    "name": "ml.t3.medium",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 1,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.t3.large",
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   },
   {
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    "category": "General purpose",
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    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.t3.xlarge",
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   {
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    "_isFastLaunch": false,
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    "memoryGiB": 32,
    "name": "ml.t3.2xlarge",
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    "_defaultOrder": 4,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5.large",
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   },
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    "_isFastLaunch": false,
    "category": "General purpose",
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    "_isFastLaunch": false,
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    "name": "ml.m5.2xlarge",
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    "name": "ml.m5.4xlarge",
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    "_isFastLaunch": false,
    "category": "General purpose",
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    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.m5.8xlarge",
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    "_isFastLaunch": false,
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