notebooks/community/model_garden/model_garden_pytorch_mistral_deployment.ipynb

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        "# Copyright 2024 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99c1c3fc2ca5"
      },
      "source": [
        "# Vertex AI Model Garden - Mistral Model (Deployment)\n",
        "\n",
        "<table><tbody><tr>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_pytorch_mistral_deployment.ipynb\">\n",
        "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_mistral_deployment.ipynb\">\n",
        "      <img alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"><br> View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "</tr></tbody></table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3de7470326a2"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This notebook demonstrates deploying prebuilt Mistral models in Vertex AI.\n",
        "\n",
        "### Objective\n",
        "\n",
        "- Deploy prebuilt [Mistral models](https://huggingface.co/mistralai) with [vLLM](https://github.com/vllm-project/vllm) containers\n",
        "    - [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1): pretrained generative text model with 7 billion parameters\n",
        "    - [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1): instruction fine-tuned version of the Mistral-7B-v0.1 generative text model\n",
        "    - [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2): improved instruction fine-tuned version of Mistral-7B-Instruct-v0.1 supporting 32k context length\n",
        "    - [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3): Mistral-7B-v0.2 with extended vocabulary of 32768 and supports function calling\n",
        "    - [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3): instruction fine-tuned version of the Mistral-7B-v0.3 generative text model\n",
        "    - [mistralai/Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407):  pretrained generative text model of 12B parameters \n",
        "    - [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407): instruct fine-tuned version of the Mistral-Nemo-Base-2407\n",
        "\n",
        "### Costs\n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
        "\n",
        "* Vertex AI\n",
        "* Cloud Storage\n",
        "\n",
        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wi-aGs442yve"
      },
      "source": [
        "## Run the notebook"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# @title Setup Google Cloud project\n",
        "\n",
        "# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
        "\n",
        "# @markdown 2. [Optional] [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs. Set the BUCKET_URI for the experiment environment. The specified Cloud Storage bucket (`BUCKET_URI`) should be located in the same region as where the notebook was launched. Note that a multi-region bucket (eg. \"us\") is not considered a match for a single region covered by the multi-region range (eg. \"us-central1\"). If not set, a unique GCS bucket will be created instead.\n",
        "\n",
        "# Import the necessary packages\n",
        "\n",
        "! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
        "\n",
        "# Import the necessary packages.\n",
        "import importlib\n",
        "import os\n",
        "import uuid\n",
        "from datetime import datetime\n",
        "from typing import Tuple\n",
        "\n",
        "from google.cloud import aiplatform\n",
        "\n",
        "common_util = importlib.import_module(\n",
        "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
        ")\n",
        "\n",
        "models, endpoints = {}, {}\n",
        "\n",
        "# Get the default cloud project id.\n",
        "PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
        "\n",
        "# Get the default region for launching jobs.\n",
        "REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
        "\n",
        "# Enable the Vertex AI API and Compute Engine API, if not already.\n",
        "print(\"Enabling Vertex AI API and Compute Engine API.\")\n",
        "! gcloud services enable aiplatform.googleapis.com compute.googleapis.com\n",
        "\n",
        "# Cloud Storage bucket for storing the experiment artifacts.\n",
        "# A unique GCS bucket will be created for the purpose of this notebook. If you\n",
        "# prefer using your own GCS bucket, change the value yourself below.\n",
        "now = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
        "BUCKET_URI = \"gs://\"  # @param {type:\"string\"}\n",
        "BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
        "\n",
        "if BUCKET_URI is None or BUCKET_URI.strip() == \"\" or BUCKET_URI == \"gs://\":\n",
        "    BUCKET_URI = f\"gs://{PROJECT_ID}-tmp-{now}-{str(uuid.uuid4())[:4]}\"\n",
        "    BUCKET_NAME = \"/\".join(BUCKET_URI.split(\"/\")[:3])\n",
        "    ! gsutil mb -l {REGION} {BUCKET_URI}\n",
        "else:\n",
        "    assert BUCKET_URI.startswith(\"gs://\"), \"BUCKET_URI must start with `gs://`.\"\n",
        "    shell_output = ! gsutil ls -Lb {BUCKET_NAME} | grep \"Location constraint:\" | sed \"s/Location constraint://\"\n",
        "    bucket_region = shell_output[0].strip().lower()\n",
        "    if bucket_region != REGION:\n",
        "        raise ValueError(\n",
        "            \"Bucket region %s is different from notebook region %s\"\n",
        "            % (bucket_region, REGION)\n",
        "        )\n",
        "print(f\"Using this GCS Bucket: {BUCKET_URI}\")\n",
        "\n",
        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
        "MODEL_BUCKET = os.path.join(BUCKET_URI, \"mistral\")\n",
        "\n",
        "\n",
        "# Initialize Vertex AI API.\n",
        "print(\"Initializing Vertex AI API.\")\n",
        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)\n",
        "\n",
        "# Gets the default SERVICE_ACCOUNT.\n",
        "shell_output = ! gcloud projects describe $PROJECT_ID\n",
        "project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
        "SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
        "print(\"Using this default Service Account:\", SERVICE_ACCOUNT)\n",
        "\n",
        "\n",
        "# Provision permissions to the SERVICE_ACCOUNT with the GCS bucket\n",
        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.admin $BUCKET_NAME\n",
        "\n",
        "! gcloud config set project $PROJECT_ID"
      ]
    },
    {
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      "source": [
        "# @title Deploy\n",
        "\n",
        "# @markdown This section deploys the prebuilt Mistral model with [vLLM](https://github.com/vllm-project/vllm) on a Vertex endpoint. It takes 15 minutes to 1 hour to finish depending on the model and the accelerator.\n",
        "\n",
        "# @markdown Set the model to deploy.\n",
        "\n",
        "prebuilt_model_id = \"mistralai/Mistral-7B-Instruct-v0.3\"  # @param [\"mistralai/Mistral-7B-v0.1\", \"mistralai/Mistral-7B-Instruct-v0.1\", \"mistralai/Mistral-7B-Instruct-v0.2\", \"mistralai/Mistral-7B-v0.3\", \"mistralai/Mistral-7B-Instruct-v0.3\", \"mistralai/Mistral-Nemo-Base-2407\", \"mistralai/Mistral-Nemo-Instruct-2407\"]\n",
        "model_id = f\"gs://vertex-model-garden-public-us/{prebuilt_model_id}\"\n",
        "\n",
        "# The pre-built serving docker image for vLLM.\n",
        "VLLM_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20240721_0916_RC00\"\n",
        "\n",
        "# Find Vertex AI prediction supported accelerators and regions in\n",
        "# https://cloud.google.com/vertex-ai/docs/predictions/configure-compute.\n",
        "\n",
        "accelerator_type = \"NVIDIA_L4\"  # @param [\"NVIDIA_L4\", \"NVIDIA_TESLA_V100\", \"NVIDIA_TESLA_T4\", \"NVIDIA_TESLA_A100\"]\n",
        "\n",
        "if accelerator_type == \"NVIDIA_L4\":\n",
        "    if \"Mistral-Nemo\" in prebuilt_model_id:\n",
        "        machine_type = \"g2-standard-48\"\n",
        "        accelerator_count = 4\n",
        "    else:\n",
        "        machine_type = \"g2-standard-8\"\n",
        "        accelerator_count = 1\n",
        "elif accelerator_type == \"NVIDIA_TESLA_V100\":\n",
        "    machine_type = \"n1-standard-16\"\n",
        "    accelerator_count = 2\n",
        "elif accelerator_type == \"NVIDIA_TESLA_T4\":\n",
        "    machine_type = \"n1-standard-16\"\n",
        "    accelerator_count = 2\n",
        "elif accelerator_type == \"NVIDIA_TESLA_A100\":\n",
        "    machine_type = \"a2-highgpu-1g\"\n",
        "    accelerator_count = 1\n",
        "\n",
        "common_util.check_quota(\n",
        "    project_id=PROJECT_ID,\n",
        "    region=REGION,\n",
        "    accelerator_type=accelerator_type,\n",
        "    accelerator_count=accelerator_count,\n",
        "    is_for_training=False,\n",
        ")\n",
        "\n",
        "# Larger setting of `max-model-len` can lead to higher requirements on\n",
        "# `gpu-memory-utilization` and GPU configuration. Larger setting of\n",
        "# `gpu-memory-utilization` increases the risk of running out of GPU memory with\n",
        "# long prompts.\n",
        "max_model_len = 4096\n",
        "gpu_memory_utilization = 0.85\n",
        "\n",
        "if \"Mistral-Nemo\" in prebuilt_model_id:\n",
        "    max_model_len = 128000\n",
        "    gpu_memory_utilization = 0.9\n",
        "\n",
        "dtype = \"bfloat16\"\n",
        "if accelerator_type in [\"NVIDIA_TESLA_T4\", \"NVIDIA_TESLA_V100\"]:\n",
        "    dtype = \"float16\"\n",
        "\n",
        "\n",
        "def deploy_model_vllm(\n",
        "    model_name: str,\n",
        "    model_id: str,\n",
        "    service_account: str,\n",
        "    base_model_id: str = None,\n",
        "    machine_type: str = \"g2-standard-8\",\n",
        "    accelerator_type: str = \"NVIDIA_L4\",\n",
        "    accelerator_count: int = 1,\n",
        "    gpu_memory_utilization: float = 0.9,\n",
        "    max_model_len: int = 4096,\n",
        "    dtype: str = \"auto\",\n",
        "    enable_trust_remote_code: bool = False,\n",
        "    enforce_eager: bool = False,\n",
        "    enable_lora: bool = False,\n",
        "    max_loras: int = 1,\n",
        "    max_cpu_loras: int = 8,\n",
        ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
        "    \"\"\"Deploys trained models with vLLM into Vertex AI.\"\"\"\n",
        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
        "\n",
        "    if not base_model_id:\n",
        "        base_model_id = model_id\n",
        "\n",
        "    # See https://docs.vllm.ai/en/latest/models/engine_args.html for a list of possible arguments with descriptions.\n",
        "    vllm_args = [\n",
        "        \"python\",\n",
        "        \"-m\",\n",
        "        \"vllm.entrypoints.api_server\",\n",
        "        \"--host=0.0.0.0\",\n",
        "        \"--port=8080\",\n",
        "        f\"--model={model_id}\",\n",
        "        f\"--tensor-parallel-size={accelerator_count}\",\n",
        "        \"--swap-space=16\",\n",
        "        f\"--gpu-memory-utilization={gpu_memory_utilization}\",\n",
        "        f\"--max-model-len={max_model_len}\",\n",
        "        f\"--dtype={dtype}\",\n",
        "        f\"--max-loras={max_loras}\",\n",
        "        f\"--max-cpu-loras={max_cpu_loras}\",\n",
        "        \"--disable-log-stats\",\n",
        "    ]\n",
        "\n",
        "    if enable_trust_remote_code:\n",
        "        vllm_args.append(\"--trust-remote-code\")\n",
        "\n",
        "    if enforce_eager:\n",
        "        vllm_args.append(\"--enforce-eager\")\n",
        "\n",
        "    if enable_lora:\n",
        "        vllm_args.append(\"--enable-lora\")\n",
        "\n",
        "    env_vars = {\n",
        "        \"MODEL_ID\": base_model_id,\n",
        "        \"DEPLOY_SOURCE\": \"notebook\",\n",
        "    }\n",
        "\n",
        "    # HF_TOKEN is not a compulsory field and may not be defined.\n",
        "    try:\n",
        "        if HF_TOKEN:\n",
        "            env_vars[\"HF_TOKEN\"] = HF_TOKEN\n",
        "    except NameError:\n",
        "        pass\n",
        "\n",
        "    model = aiplatform.Model.upload(\n",
        "        display_name=model_name,\n",
        "        serving_container_image_uri=VLLM_DOCKER_URI,\n",
        "        serving_container_args=vllm_args,\n",
        "        serving_container_ports=[8080],\n",
        "        serving_container_predict_route=\"/generate\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=env_vars,\n",
        "        serving_container_shared_memory_size_mb=(16 * 1024),  # 16 GB\n",
        "        serving_container_deployment_timeout=7200,\n",
        "    )\n",
        "    print(\n",
        "        f\"Deploying {model_name} on {machine_type} with {accelerator_count} {accelerator_type} GPU(s).\"\n",
        "    )\n",
        "    model.deploy(\n",
        "        endpoint=endpoint,\n",
        "        machine_type=machine_type,\n",
        "        accelerator_type=accelerator_type,\n",
        "        accelerator_count=accelerator_count,\n",
        "        deploy_request_timeout=1800,\n",
        "        service_account=service_account,\n",
        "    )\n",
        "    print(\"endpoint_name:\", endpoint.name)\n",
        "\n",
        "    return model, endpoint\n",
        "\n",
        "models[\"vllm_gpu\"], endpoints[\"vllm_gpu\"] = deploy_model_vllm(\n",
        "    model_name=common_util.get_job_name_with_datetime(prefix=\"mistral-serve-vllm\"),\n",
        "    model_id=model_id,\n",
        "    service_account=SERVICE_ACCOUNT,\n",
        "    machine_type=machine_type,\n",
        "    accelerator_type=accelerator_type,\n",
        "    accelerator_count=accelerator_count,\n",
        "    max_model_len=max_model_len,\n",
        "    gpu_memory_utilization=gpu_memory_utilization,\n",
        "    dtype=dtype,\n",
        ")"
      ]
    },
    {
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      "source": [
        "# @title Predict\n",
        "\n",
        "# @markdown Once deployment succeeds, you can send requests to the endpoint with text prompts. Sampling parameters supported by vLLM can be found [here](https://docs.vllm.ai/en/latest/dev/sampling_params.html).\n",
        "\n",
        "# @markdown Example:\n",
        "\n",
        "# @markdown ```\n",
        "# @markdown Human: What is a car?\n",
        "# @markdown Assistant:  A car, or a motor car, is a road-connected human-transportation system used to move people or goods from one place to another. The term also encompasses a wide range of vehicles, including motorboats, trains, and aircrafts. Cars typically have four wheels, a cabin for passengers, and an engine or motor. They have been around since the early 19th century and are now one of the most popular forms of transportation, used for daily commuting, shopping, and other purposes.\n",
        "# @markdown ```\n",
        "# @markdown Additionally, you can moderate the generated text with Vertex AI. See [Moderate text documentation](https://cloud.google.com/natural-language/docs/moderating-text) for more details.\n",
        "\n",
        "# Loads an existing endpoint instance using the endpoint name:\n",
        "# - Using `endpoint_name = endpoint.name` allows us to get the\n",
        "#   endpoint name of the endpoint `endpoint` created in the cell\n",
        "#   above.\n",
        "# - Alternatively, you can set `endpoint_name = \"1234567890123456789\"` to load\n",
        "#   an existing endpoint with the ID 1234567890123456789.\n",
        "# You may uncomment the code below to load an existing endpoint.\n",
        "\n",
        "# endpoint_name = \"\"  # @param {type:\"string\"}\n",
        "# aip_endpoint_name = (\n",
        "#     f\"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{endpoint_name}\"\n",
        "# )\n",
        "# endpoint = aiplatform.Endpoint(aip_endpoint_name)\n",
        "\n",
        "prompt = \"What is a car?\"  # @param {type: \"string\"}\n",
        "# @markdown If you encounter the issue like `ServiceUnavailable: 503 Took too long to respond when processing`, you can reduce the maximum number of output tokens, by lowering `max_tokens`.\n",
        "max_tokens = 50  # @param {type:\"integer\"}\n",
        "temperature = 1.0  # @param {type:\"number\"}\n",
        "top_p = 1.0  # @param {type:\"number\"}\n",
        "top_k = 1  # @param {type:\"integer\"}\n",
        "raw_response = False  # @param {type:\"boolean\"}\n",
        "\n",
        "# Overrides parameters for inferences.\n",
        "instances = [\n",
        "    {\n",
        "        \"prompt\": prompt,\n",
        "        \"max_tokens\": max_tokens,\n",
        "        \"temperature\": temperature,\n",
        "        \"top_p\": top_p,\n",
        "        \"top_k\": top_k,\n",
        "        \"raw_response\": raw_response,\n",
        "    },\n",
        "]\n",
        "response = endpoints[\"vllm_gpu\"].predict(instances=instances)\n",
        "\n",
        "for prediction in response.predictions:\n",
        "    print(prediction)\n",
        "\n",
        "# Reference the following code for using the OpenAI vLLM server.\n",
        "# import json\n",
        "# response = endpoints[\"vllm_gpu\"].raw_predict(\n",
        "#     body=json.dumps({\n",
        "#         \"model\": prebuilt_model_id,\n",
        "#         \"prompt\": \"My favourite condiment is\",\n",
        "#         \"n\": 1,\n",
        "#         \"max_tokens\": 200,\n",
        "#         \"temperature\": 1.0,\n",
        "#         \"top_p\": 1.0,\n",
        "#         \"top_k\": 10,\n",
        "#     }),\n",
        "#     headers={\"Content-Type\": \"application/json\"},\n",
        "# )\n",
        "# print(response.json())"
      ]
    },
    {
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        "cellView": "form",
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      "source": [
        "# @title Clean up resources\n",
        "# @markdown  Delete the experiment models and endpoints to recycle the resources\n",
        "# @markdown  and avoid unnecessary continuous charges that may incur.\n",
        "\n",
        "# Undeploy model and delete endpoint.\n",
        "for endpoint in endpoints.values():\n",
        "    endpoint.delete(force=True)\n",
        "\n",
        "# Delete models.\n",
        "for model in models.values():\n",
        "    model.delete()\n",
        "\n",
        "delete_bucket = False  # @param {type:\"boolean\"}\n",
        "if delete_bucket:\n",
        "    ! gsutil -m rm -r $BUCKET_NAME"
      ]
    }
  ],
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    "colab": {
      "name": "model_garden_pytorch_mistral_deployment.ipynb",
      "toc_visible": true
    },
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      "display_name": "Python 3",
      "name": "python3"
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