examples/huggingface/huggingface-large-model-inference.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rEJBSTyZIrIb"
   },
   "source": [
    "# Hugging Face Large Model Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kTCFado4IrIc"
   },
   "source": [
    "This notebook demonstrates how to deploy Hugging Face transformer models using Hugging Face Text Generation Inference (TGI) Deep Learning Container on Amazon SageMaker.\n",
    "\n",
    "TGI is an open source, high performance inference library that can be used to deploy large language models from Hugging Face’s repository in minutes. The library includes advanced functionality like model parallelism and dynamic batching to simplify production inference with large language models like flan-t5-xxl, LLaMa, StableLM, and GPT-NeoX. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install the SageMaker Python SDK\n",
    "\n",
    "First, make sure that the latest version of SageMaker SDK is installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install \"sagemaker>=2.163.0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup account and role\n",
    "\n",
    "Then, we import the SageMaker python SDK and instantiate a `sagemaker_session` which we use to determine the current region and execution role."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri\n",
    "import time\n",
    "\n",
    "sagemaker_session = sagemaker.Session()\n",
    "region = sagemaker_session.boto_region_name\n",
    "role = sagemaker.get_execution_role()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve the LLM Image URI\n",
    "\n",
    "We use the helper function `get_huggingface_llm_image_uri()` to generate the appropriate image URI for the Hugging Face Large Language Model (LLM) inference.\n",
    "\n",
    "The function takes a required parameter `backend` and several optional parameters. The `backend` specifies the type of backend to use for the model, the values can be \"lmi\" and \"huggingface\". The \"lmi\" stands for SageMaker LMI inference backend, and \"huggingface\" refers to using Hugging Face TGI inference backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_uri = get_huggingface_llm_image_uri(\n",
    "  backend=\"huggingface\", # or lmi\n",
    "  region=region\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the Hugging Face Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we configure the `model` object by specifying a unique name, the `image_uri` for the managed TGI container, and the execution role for the endpoint. Additionally, we specify a number of environment variables including the `HF_MODEL_ID` which corresponds to the model from the HuggingFace Hub that will be deployed, and the `HF_TASK` which configures the inference task to be performed by the model.\n",
    "\n",
    "You should also define `SM_NUM_GPUS`, which specifies the tensor parallelism degree of the model. Tensor parallelism can be used to split the model across multiple GPUs, which is necessary when working with LLMs that are too big for a single GPU. Here, you should set `SM_NUM_GPUS` to the number of available GPUs on your selected instance type.  For example, in this tutorial, we set `SM_NUM_GPUS` to 4 because our selected instance type `ml.g4dn.12xlarge` has 4 available GPUs.\n",
    "\n",
    "Additionally, we could reduce the memory footprint of the model by setting the `HF_MODEL_QUANTIZE` environment variable to true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"gpt-neox-20b-\" + time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.gmtime())\n",
    "\n",
    "hub = {\n",
    "    'HF_MODEL_ID':'EleutherAI/gpt-neox-20b',\n",
    "    'HF_TASK':'text-generation',\n",
    "    'SM_NUM_GPUS':'4',\n",
    "    'HF_MODEL_QUANTIZE':'bitsandbytes'\n",
    "}\n",
    "\n",
    "model = HuggingFaceModel(\n",
    "    name=model_name,\n",
    "    env=hub,\n",
    "    role=role,\n",
    "    image_uri=image_uri\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a SageMaker Endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we deploy the model by invoking the `deploy()` function. Here we use an `ml.g4dn.12xlarge` instance which come with 4 NVIDIA A10 GPUs. By setting the `SM_NUM_GPUS` environment variable to 4 in the last code block, we indicate that this model should be sharded across all 4 GPU devices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = model.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type=\"ml.g4dn.12xlarge\",\n",
    "    endpoint_name=model_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the endpoint is up and running, we can evaluate the model using the `predict()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data = {\n",
    "  \"inputs\": \"The diamondback terrapin was the first reptile to\",\n",
    "  \"parameters\": {\n",
    "    \"do_sample\": True,\n",
    "    \"max_new_tokens\": 100,\n",
    "    \"temperature\": 0.7,\n",
    "    \"watermark\": True\n",
    "  }\n",
    "}\n",
    "\n",
    "predictor.predict(input_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you've finished using the endpoint, it's important to delete it to avoid incurring unnecessary costs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.delete_model()\n",
    "predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this tutorial, we used a TGI container to deploy GPT-NeoX using 4 GPUs on a SageMaker `ml.g4dn.12xlarge` instance. With Hugging Face's Text Generation Inference and SageMaker Hosting, you can easily host large language models like GPT-NeoX, flan-t5-xxl, and LLaMa."
   ]
  }
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