notebooks/community/model_garden/model_garden_pytorch_clip.ipynb (313 lines of code) (raw):
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"# Copyright 2025 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><tbody><tr>\n",
" <td style=\"text-align: center\">\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/instances\">\n",
" <img alt=\"Workbench logo\" src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" width=\"32px\"><br> Run in Workbench\n",
" </a>\n",
" </td>\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_clip.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_clip.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": "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",
"### File a bug\n",
"\n",
"File a bug on [GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/issues/new) if you encounter any issue with the notebook.\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": "code",
"execution_count": null,
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"# @title Setup Google Cloud project\n",
"\n",
"# Import the necessary packages\n",
"\n",
"# Upgrade Vertex AI SDK.\n",
"! pip3 install --upgrade --quiet 'google-cloud-aiplatform>=1.84.0'\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",
"BUCKET_URI = \"gs://\" # @param {type:\"string\"}\n",
"\n",
"# @markdown 3. **[Optional]** Set region. If not set, the region will be set automatically according to Colab Enterprise environment.\n",
"\n",
"REGION = \"\" # @param {type:\"string\"}\n",
"\n",
"import importlib\n",
"import os\n",
"\n",
"from google.cloud import aiplatform\n",
"\n",
"if os.environ.get(\"VERTEX_PRODUCT\") != \"COLAB_ENTERPRISE\":\n",
" ! pip install --upgrade tensorflow\n",
"! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\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",
"\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",
"if not REGION:\n",
" REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
"\n",
"# Initialize Vertex AI API.\n",
"print(\"Initializing Vertex AI API.\")\n",
"aiplatform.init(project=PROJECT_ID, location=REGION)\n",
"\n",
"! gcloud config set project $PROJECT_ID\n",
"\n",
"aiplatform.init(project=PROJECT_ID, location=REGION)\n",
"\n",
"# 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\"\n",
"\n",
"models, endpoints = {}, {}\n",
"LABEL = \"endpoint\"\n",
"\n",
"import vertexai\n",
"\n",
"vertexai.init(\n",
" project=PROJECT_ID,\n",
" location=REGION,\n",
")\n",
"\n",
"version_id = \"clip-vit-base-patch32\"\n",
"PUBLISHER_MODEL_NAME = f\"publishers/openai/models/clip-vit-base-patch32@{version_id}\"\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",
" model_garden_source_model_name=\"publishers/openai/models/clip-vit-base-patch32\",\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",
" system_labels={\n",
" \"NOTEBOOK_NAME\": \"model_garden_pytorch_clip.ipynb\",\n",
" \"NOTEBOOK_ENVIRONMENT\": common_util.get_deploy_source(),\n",
" },\n",
" )\n",
" return model, endpoint"
]
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{
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"accelerator_type = \"NVIDIA_TESLA_T4\"\n",
"machine_type = \"n1-standard-8\"\n",
"accelerator_count = 1\n",
"\n",
"# @title [Option 1] Deploy with Model Garden SDK\n",
"\n",
"# @markdown Deploy with Gen AI model-centric SDK. This section uploads the prebuilt model to Model Registry and deploys it to a Vertex AI Endpoint. It takes 15 minutes to 1 hour to finish depending on the size of the model. See [use open models with Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/open-models/use-open-models) for documentation on other use cases.\n",
"from vertexai.preview import model_garden\n",
"\n",
"model = model_garden.OpenModel(PUBLISHER_MODEL_NAME)\n",
"endpoints[LABEL] = model.deploy(\n",
" machine_type=machine_type,\n",
" accelerator_type=accelerator_type,\n",
" accelerator_count=accelerator_count,\n",
" accept_eula=True, # Accept the End User License Agreement (EULA) on the model card before deploy. Otherwise, the deployment will be forbidden.\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "vBvfVTWOhdEP"
},
"outputs": [],
"source": [
"# @title [Option 2] Deploy with customized configs\n",
"\n",
"# @markdown This section uploads the pre-trained model to Model Registry and deploys it on the Endpoint with 1 T4 GPU.\n",
"# @markdown The model deployment step will take ~15 minutes to complete.\n",
"\n",
"models[LABEL], endpoints[LABEL] = deploy_model(\n",
" model_id=\"openai/clip-vit-base-patch32\", task=\"zero-shot-image-classification\"\n",
")\n",
"\n",
"image1 = common_util.download_image(\n",
" \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n",
")\n",
"image2 = common_util.download_image(\n",
" \"http://images.cocodataset.org/val2017/000000000285.jpg\"\n",
")\n",
"grid = common_util.image_grid([image1, image2], 1, 2)\n",
"display(grid)\n",
"\n",
"instances = [\n",
" {\"image\": common_util.image_to_base64(image1), \"text\": \"two cats\"},\n",
" {\"image\": common_util.image_to_base64(image2), \"text\": \"a bear\"},\n",
"]\n",
"preds = endpoints[LABEL].predict(instances=instances).predictions\n",
"print(preds)\n",
"\n",
"models[LABEL], endpoints[LABEL] = deploy_model(\n",
" model_id=\"openai/clip-vit-base-patch32\", task=\"feature-embedding\"\n",
")\n",
"\n",
"import numpy as np\n",
"\n",
"# Extract feature embedding of images.\n",
"image = common_util.download_image(\n",
" \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n",
")\n",
"display(image)\n",
"instances = [\n",
" {\"image\": common_util.image_to_base64(image)},\n",
"]\n",
"preds = endpoints[LABEL].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 = endpoints[LABEL].predict(instances=instances).predictions\n",
"text_features = np.array(preds[0][\"text_features\"])\n",
"print(text_features.shape)"
]
},
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"source": [
"# @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()"
]
}
],
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