         sagemaker-core/sagemaker-core-llama-3-8B.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Amazon SageMaker Using AWS Large Model Inference (LMI) Deep Learning Container\n",
    "\n",
    "**Recommended Kernel(s):** You can run this notebook with any Amazon SageMaker Studio kernel. We recommend using the Data Science 3.0 kernel.\n",
    "\n",
    "This notebook demonstrates how to deploy a [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) HuggingFace model to a SageMaker Endpoint for text generation. In this example, the SageMaker-managed [LMI (Large Model Inference)](https://docs.djl.ai/docs/serving/serving/docs/large_model_inference.html) Docker image will serve as the inference image. LMI images feature a [DJL serving](https://github.com/deepjavalibrary/djl-serving) stack powered by the [Deep Java Library](https://djl.ai/). \n",
    "\n",
    "## 1. Dependency Installation\n",
    "### 1.1. Python Dependencies & Imports\n",
    "This notebook requires the following Python dependencies:\n",
    "* AWS [`sagemaker_core`]()\n",
    "\n",
    "Let's install or upgrade these dependencies using the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install pip --upgrade --quiet\n",
    "%pip install sagemaker-core huggingface_hub --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import sagemaker\n",
    "\n",
    "from sagemaker_core.helper.session_helper import get_execution_role, Session\n",
    "import pathlib\n",
    "import huggingface_hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "PROJECT_ROOT_DIR = pathlib.Path.cwd()\n",
    "\n",
    "SM_SESSION = Session()\n",
    "\n",
    "REGION = SM_SESSION._region_name\n",
    "\n",
    "SM_DEFAULT_EXECUTION_ROLE_ARN = get_execution_role()\n",
    "\n",
    "INSTANCE_TYPE = \"ml.g5.12xlarge\"\n",
    "\n",
    "# See https://github.com/aws/deep-learning-containers/blob/master/available_images.md#large-model-inference-containers for lastest DLC.\n",
    "container_image_uri = (\n",
    "    f\"763104351884.dkr.ecr.{REGION}.amazonaws.com/djl-inference:0.29.0-tensorrtllm0.11.0-cu124\"\n",
    ")\n",
    "\n",
    "default_bucket = sagemaker.Session().default_bucket()\n",
    "default_bucket_prefix = sagemaker.Session().default_bucket_prefix\n",
    "\n",
    "MODEL = \"meta-llama/Meta-Llama-3-8B\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download custom draft model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2. Deploy Speculative Decoding-Enabled Endpoint\n",
    "### 2.1. Endpoint Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will configure what models to use and server startup parameters and via environment variables.   \n",
    "\n",
    "* `OPTION_MODEL_ID` points to the HF Model ID or base S3 prefix of the model artifacts\n",
    "You can read about the other config paramters in LMI [documentation](https://github.com/deepjavalibrary/djl-serving/tree/master/serving/docs/lmi)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "environment = {\n",
    "    \"HF_MODEL_ID\": MODEL,\n",
    "    \"OPTION_GPU_MEMORY_UTILIZATION\": \"0.85\",\n",
    "    \"HF_TOKEN\": \"YourHFToken\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker_core.shapes import ContainerDefinition, ProductionVariant\n",
    "from sagemaker_core.resources import Model, EndpointConfig, Endpoint\n",
    "from time import gmtime, strftime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "container_defintion = ContainerDefinition(image=container_image_uri, environment=environment)\n",
    "container_defintion.environment = environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_name = f'llama-3-8B-hugging-face-{strftime(\"%H-%M-%S\", gmtime())}'\n",
    "\n",
    "model = Model.create(\n",
    "    model_name=model_name,\n",
    "    primary_container=container_defintion,\n",
    "    execution_role_arn=SM_DEFAULT_EXECUTION_ROLE_ARN,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [],
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "Start-up of LLM inference containers can last longer than smaller models, mainly due to longer model downloading and loading times. Timeout values need to be increased accordingly from their default values. Each endpoint deployment takes a few minutes. We also set `routing_strategy` which would benefit us if we were to back our endpoint with multiple instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker_core.shapes import ProductionVariantRoutingConfig\n",
    "\n",
    "routing_config = ProductionVariantRoutingConfig(routing_strategy=\"LEAST_OUTSTANDING_REQUESTS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "endpoint_config = EndpointConfig.create(\n",
    "    endpoint_config_name=model_name,\n",
    "    production_variants=[\n",
    "        ProductionVariant(\n",
    "            variant_name=model_name,\n",
    "            initial_instance_count=1,\n",
    "            instance_type=INSTANCE_TYPE,\n",
    "            model_name=model,\n",
    "            container_startup_health_check_timeout_in_seconds=3600,\n",
    "            model_data_download_timeout_in_seconds=3600,\n",
    "            routing_config=routing_config,\n",
    "        )\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "endpoint = Endpoint.create(\n",
    "    endpoint_name=model_name,\n",
    "    endpoint_config_name=endpoint_config,  # Pass `EndpointConfig` object created above\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "This cells will block until the endpoint is deployed, which is necessary for the following steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "endpoint.wait_for_status(\"InService\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Endpoint invocation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Let's invoke our endpoint and get a sample response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "response = endpoint.invoke(\n",
    "    body=json.dumps(\n",
    "        {\n",
    "            \"inputs\": [\"What is the capital of France?\"],\n",
    "            \"max_new_tokens\": 512,\n",
    "            \"temperature\": 0.0,\n",
    "        }\n",
    "    ),\n",
    "    content_type=\"application/json\",\n",
    ")\n",
    "response[\"Body\"].read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Clean Up Endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Delete any sagemaker core resource objects created in this notebook\n",
    "def delete_all_sagemaker_resources():\n",
    "    all_objects = list(locals().values()) + list(globals().values())\n",
    "    deletable_objects = [\n",
    "        obj\n",
    "        for obj in all_objects\n",
    "        if hasattr(obj, \"delete\") and obj.__class__.__module__ == \"sagemaker_core.main.resources\"\n",
    "    ]\n",
    "\n",
    "    for obj in deletable_objects:\n",
    "        obj.delete()\n",
    "\n",
    "\n",
    "delete_all_sagemaker_resources()"
   ]
  }
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