notebooks/community/model_garden/model_garden_pytorch_clip.ipynb

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  "cells": [
    {
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      "execution_count": null,
      "metadata": {
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      "source": [
        "# Copyright 2023 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": "2bd716bf3e39"
      },
      "source": [
        "# Vertex AI Model Garden - CLIP\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_clip.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_clip.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_pytorch_clip.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "Open in Vertex AI Workbench\n",
        "    </a>\n",
        "    (a Python-3 CPU notebook is recommended)\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8cd12648da4"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This notebook demonstrates deploying the pre-trained [CLIP](https://huggingface.co/openai/clip-vit-base-patch32) model on Vertex AI for online prediction.\n",
        "\n",
        "### Objective\n",
        "\n",
        "- Upload the model to [Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
        "- Deploy the model on [Endpoint](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
        "- Run online predictions for image captioning.\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) and [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": "264c07757582"
      },
      "source": [
        "## Setup environment\n",
        "\n",
        "**NOTE**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d73ffa0c0b83"
      },
      "source": [
        "### Colab only"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2707b02ef5df"
      },
      "outputs": [],
      "source": [
        "if \"google.colab\" in str(get_ipython()):\n",
        "    ! pip3 install --upgrade google-cloud-aiplatform\n",
        "    from google.colab import auth as google_auth\n",
        "\n",
        "    google_auth.authenticate_user()\n",
        "\n",
        "    # Restart the notebook kernel after installs.\n",
        "    import IPython\n",
        "\n",
        "    app = IPython.Application.instance()\n",
        "    app.kernel.do_shutdown(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0f826ff482a2"
      },
      "source": [
        "### Setup Google Cloud project\n",
        "\n",
        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
        "\n",
        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
        "\n",
        "1. [Enable the Vertex AI API and Compute Engine API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component).\n",
        "\n",
        "1. [Create a Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) for storing experiment outputs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8958ebc71868"
      },
      "source": [
        "Fill following variables for experiments environment:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9db30f827a65"
      },
      "outputs": [],
      "source": [
        "# Cloud project id.\n",
        "PROJECT_ID = \"\"  # @param {type:\"string\"}\n",
        "\n",
        "# The region you want to launch jobs in.\n",
        "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
        "\n",
        "# The Cloud Storage bucket for storing experiments output. Fill it without the 'gs://' prefix.\n",
        "GCS_BUCKET = \"\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92f16e22c20b"
      },
      "source": [
        "Initialize Vertex AI API:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1680c257acfb"
      },
      "outputs": [],
      "source": [
        "from google.cloud import aiplatform\n",
        "\n",
        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=GCS_BUCKET)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ca48b699d17"
      },
      "source": [
        "### Define constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "de9882ea89ea"
      },
      "outputs": [],
      "source": [
        "# The pre-built serving docker image. It contains serving scripts and models.\n",
        "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-transformers-serve\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10188266a5cd"
      },
      "source": [
        "### Define common functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cac4478ae098"
      },
      "outputs": [],
      "source": [
        "import base64\n",
        "import os\n",
        "from datetime import datetime\n",
        "from io import BytesIO\n",
        "\n",
        "import requests\n",
        "from google.cloud import aiplatform\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "def create_job_name(prefix):\n",
        "    user = os.environ.get(\"USER\")\n",
        "    now = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
        "    job_name = f\"{prefix}-{user}-{now}\"\n",
        "    return job_name\n",
        "\n",
        "\n",
        "def download_image(url):\n",
        "    response = requests.get(url)\n",
        "    return Image.open(BytesIO(response.content))\n",
        "\n",
        "\n",
        "def image_to_base64(image, format=\"JPEG\"):\n",
        "    buffer = BytesIO()\n",
        "    image.save(buffer, format=format)\n",
        "    image_str = base64.b64encode(buffer.getvalue()).decode(\"utf-8\")\n",
        "    return image_str\n",
        "\n",
        "\n",
        "def base64_to_image(image_str):\n",
        "    image = Image.open(BytesIO(base64.b64decode(image_str)))\n",
        "    return image\n",
        "\n",
        "\n",
        "def image_grid(imgs, rows=2, cols=2):\n",
        "    w, h = imgs[0].size\n",
        "    grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n",
        "    for i, img in enumerate(imgs):\n",
        "        grid.paste(img, box=(i % cols * w, i // cols * h))\n",
        "    return grid\n",
        "\n",
        "\n",
        "def deploy_model(model_id, task):\n",
        "    model_name = \"clip\"\n",
        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
        "    serving_env = {\n",
        "        \"MODEL_ID\": model_id,\n",
        "        \"TASK\": task,\n",
        "        \"DEPLOY_SOURCE\": \"notebook\",\n",
        "    }\n",
        "    # If the model_id is a GCS path, use artifact_uri to pass it to serving docker.\n",
        "    artifact_uri = model_id if model_id.startswith(\"gs://\") else None\n",
        "    model = aiplatform.Model.upload(\n",
        "        display_name=model_name,\n",
        "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
        "        serving_container_ports=[7080],\n",
        "        serving_container_predict_route=\"/predictions/transformers_serving\",\n",
        "        serving_container_health_route=\"/ping\",\n",
        "        serving_container_environment_variables=serving_env,\n",
        "        artifact_uri=artifact_uri,\n",
        "    )\n",
        "    model.deploy(\n",
        "        endpoint=endpoint,\n",
        "        machine_type=\"n1-standard-8\",\n",
        "        accelerator_type=\"NVIDIA_TESLA_T4\",\n",
        "        accelerator_count=1,\n",
        "        deploy_request_timeout=1800,\n",
        "    )\n",
        "    return model, endpoint"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2d72ecdb8c9"
      },
      "source": [
        "## Upload and deploy models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9448c5f545fa"
      },
      "source": [
        "This section uploads the pre-trained model to Model Registry and deploys it on the Endpoint with 1 T4 GPU.\n",
        "\n",
        "The model deployment step will take ~15 minutes to complete."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99f3c6b404b5"
      },
      "source": [
        "### Zero-shot image classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b4b46c28d8b1"
      },
      "outputs": [],
      "source": [
        "model, endpoint = deploy_model(\n",
        "    model_id=\"openai/clip-vit-base-patch32\", task=\"zero-shot-image-classification\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "80b3fd2ace09"
      },
      "source": [
        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6be655247cb1"
      },
      "outputs": [],
      "source": [
        "image1 = download_image(\"http://images.cocodataset.org/val2017/000000039769.jpg\")\n",
        "image2 = download_image(\"http://images.cocodataset.org/val2017/000000000285.jpg\")\n",
        "grid = image_grid([image1, image2], 1, 2)\n",
        "display(grid)\n",
        "\n",
        "instances = [\n",
        "    {\"image\": image_to_base64(image1), \"text\": \"two cats\"},\n",
        "    {\"image\": image_to_base64(image2), \"text\": \"a bear\"},\n",
        "]\n",
        "preds = endpoint.predict(instances=instances).predictions\n",
        "print(preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ccf3714dbe9"
      },
      "outputs": [],
      "source": [
        "# Undeploy model and delete endpoint.\n",
        "endpoint.delete(force=True)\n",
        "\n",
        "# Delete models.\n",
        "model.delete()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddf9e9ec7b58"
      },
      "source": [
        "### Image/text feature embedding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8897bbac6887"
      },
      "outputs": [],
      "source": [
        "model, endpoint = deploy_model(\n",
        "    model_id=\"openai/clip-vit-base-patch32\", task=\"feature-embedding\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9f854c45f8f3"
      },
      "source": [
        "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed after the above model deployment step succeeds and before you run the next step below. Otherwise you might see a ServiceUnavailable: 503 502:Bad Gateway error when you send requests to the endpoint."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2abd54335f36"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Extract feature embedding of images.\n",
        "image = download_image(\"http://images.cocodataset.org/val2017/000000039769.jpg\")\n",
        "display(image)\n",
        "instances = [\n",
        "    {\"image\": image_to_base64(image)},\n",
        "]\n",
        "preds = endpoint.predict(instances=instances).predictions\n",
        "image_features = np.array(preds[0][\"image_features\"])\n",
        "print(image_features.shape)\n",
        "\n",
        "# Extract feature embedding of texts.\n",
        "instances = [\n",
        "    {\"text\": \"two cats\"},\n",
        "    {\"text\": \"hello world\"},\n",
        "]\n",
        "preds = endpoint.predict(instances=instances).predictions\n",
        "text_features = np.array(preds[0][\"text_features\"])\n",
        "print(text_features.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "712eb9d0b336"
      },
      "outputs": [],
      "source": [
        "# Undeploy model and delete endpoint.\n",
        "endpoint.delete(force=True)\n",
        "\n",
        "# Delete models.\n",
        "model.delete()"
      ]
    }
  ],
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    "colab": {
      "name": "model_garden_pytorch_clip.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
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