Use models in Model Garden
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Discover, test, tune, and deploy models by using Model Garden in the Google Cloud console. You can also deploy Model Garden models by using the Google Cloud CLI.

Send test prompts

In the Google Cloud console, go to the Model Garden page.

Go to Model Garden
Find a supported model that you want to test and click View details.
Click Open prompt design.

You're taken to the Prompt design page.
In Prompt, enter the prompt that you want to test.
Optional: Configure the model parameters.
Click Submit.

Tune a model

In the Google Cloud console, go to the Model Garden page.

Go to Model Garden
In Search models, enter BERT or T5-FLAN, then click the magnifying glass to search.
Click View details on the T5-FLAN or the BERT model card.
Click Open fine-tuning pipeline.

You're taken to the Vertex AI pipelines page.
To start tuning, click Create run.

Tune in a notebook

The model cards for most open source foundation models and fine-tunable models support tuning in a notebook.

In the Google Cloud console, go to the Model Garden page.

Go to Model Garden
Find a supported model that you want to tune and go to its model card.
Click Open notebook.

Deploy an open model

You can deploy a model by using its model card in the Google Cloud console or programmatically.

For more information about setting up the Google Gen AI SDK or Google Cloud CLI, see the Google Gen AI SDK overview or Install the Google Cloud CLI.

Vertex AI SDK for Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Vertex AI SDK for Python API reference documentation.

List the models that you can deploy and record the model ID to deploy. You can optionally list the supported Hugging Face models in Model Garden and even filter them by model names. The output doesn't include any tuned models.


import vertexai
from vertexai.preview import model_garden

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")

# List deployable models, optionally list Hugging Face models only or filter by model name.
deployable_models = model_garden.list_deployable_models(list_hf_models=False, model_filter="gemma")
print(deployable_models)
# Example response:
# ['google/gemma2@gemma-2-27b','google/gemma2@gemma-2-27b-it', ...]

View the deployment specifications for a model by using the model ID from the previous step. You can view the machine type, accelerator type, and container image URI that Model Garden has verified for a particular model.


import vertexai
from vertexai.preview import model_garden

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
# model = "google/gemma3@gemma-3-1b-it"
vertexai.init(project=PROJECT_ID, location="us-central1")

# For Hugging Face modelsm the format is the Hugging Face model name, as in
# "meta-llama/Llama-3.3-70B-Instruct".
# Go to https://console.cloud.google.com/vertex-ai/model-garden to find all deployable
# model names.

model = model_garden.OpenModel(model)
deploy_options = model.list_deploy_options()
print(deploy_options)
# Example response:
# [
#   dedicated_resources {
#     machine_spec {
#       machine_type: "g2-standard-12"
#       accelerator_type: NVIDIA_L4
#       accelerator_count: 1
#     }
#   }
#   container_spec {
#     ...
#   }
#   ...
# ]

Deploy a model to an endpoint. Model Garden uses the default deployment configuration unless you specify additional argument and values.


import vertexai
from vertexai.preview import model_garden

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")

open_model = model_garden.OpenModel("google/gemma3@gemma-3-12b-it")
endpoint = open_model.deploy(
    machine_type="g2-standard-48",
    accelerator_type="NVIDIA_L4",
    accelerator_count=4,
    accept_eula=True,
)

# Optional. Run predictions on the deployed endoint.
# endpoint.predict(instances=[{"prompt": "What is Generative AI?"}])

gcloud

Before you begin, specify a quota project to run the following commands. The commands you run are counted against the quotas for that project. For more information, see Set the quota project.

List the models that you can deploy by running the gcloud beta ai model-garden models list command. This command lists all model IDs and which ones you can self deploy.

gcloud beta ai model-garden models list

In the output, find the model ID to deploy. The following example shows an abbreviated output.

MODEL_ID                                                                SUPPORTS_DEPLOYMENT
google/gemma2@gemma-2-27b                                               Yes
google/gemma2@gemma-2-27b-it                                            Yes
google/gemma2@gemma-2-2b                                                Yes
google/gemma2@gemma-2-2b-it                                             Yes
google/gemma2@gemma-2-9b                                                Yes
google/gemma2@gemma-2-9b-it                                             Yes
google/gemma@gemma-1.1-2b-it                                            Yes
google/gemma@gemma-1.1-2b-it-gg-hf                                      Yes
google/gemma@gemma-1.1-7b-it                                            Yes
google/gemma@gemma-1.1-7b-it-gg-hf                                      Yes
google/gemma@gemma-2b                                                   Yes
google/gemma@gemma-2b-gg-hf                                             Yes
google/gemma@gemma-2b-it                                                Yes
google/gemma@gemma-2b-it-gg-hf                                          Yes
google/gemma@gemma-7b                                                   Yes
google/gemma@gemma-7b-gg-hf                                             Yes
google/gemma@gemma-7b-it                                                Yes
google/gemma@gemma-7b-it-gg-hf                                          Yes

The output doesn't include any tuned models or Hugging Face models. To view which Hugging Face models are supported, add the --list-supported-hugging-face-models flag.

To view the deployment specifications for a model, run the gcloud beta ai model-garden models list-deployment-config command. You can view the machine type, accelorator type, and container image URI that Model Garden supports for a particular model.
```
gcloud beta ai model-garden models list-deployment-config \
    --model=MODEL_ID
```
Replace MODEL_ID with the model ID from the previous list command, such as google/gemma@gemma-2b or stabilityai/stable-diffusion-xl-base-1.0.
Deploy a model to an endpoint by running the gcloud beta ai model-garden models deploy command. Model Garden generates a display name for your endpoint and uses the default deployment configuration unless you specify additional argument and values.

To run the command asynchronously, include the --asynchronous flag.
```
gcloud beta ai model-garden models deploy \
    --model=MODEL_ID \
    [--machine-type=MACHINE_TYPE] \
    [--accelerator-type=ACCELERATOR_TYPE] \
    [--endpoint-display-name=ENDPOINT_NAME] \
    [--hugging-face-access-token=HF_ACCESS_TOKEN] \
    [--reservation-affinity reservation-affinity-type=any-reservation] \
    [--reservation-affinity reservation-affinity-type=specific-reservation, key="compute.googleapis.com/reservation-name", values=RESERVATION_RESOURCE_NAME] \
    [--asynchronous]
```
Replace the following placeholders:
- MODEL_ID: The model ID from the previous list command. For Hugging Face models, use the Hugging Face model URL format, such as stabilityai/stable-diffusion-xl-base-1.0.
- MACHINE_TYPE: Defines the set of resources to deploy for your model, such as g2-standard-4.
- ACCELERATOR_TYPE: Specifies accelerators to add to your deployment to help improve performance when working with intensive workloads, such as NVIDIA_L4.
- ENDPOINT_NAME: A name for the deployed Vertex AI endpoint.
- HF_ACCESS_TOKEN: For Hugging Face models, if the model is gated, provide an access token.
- RESERVATION_RESOURCE_NAME: To use a specific Compute Engine reservation, specify the name of your reservation. If you specify a specific reservation, you can't specify any-reservation.
The output includes the deployment configuration that Model Garden used, the endpoint ID, and the deployment operation ID, which you can use to check the deployment status.
```
Using the default deployment configuration:
 Machine type: g2-standard-12
 Accelerator type: NVIDIA_L4
 Accelerator count: 1

The project has enough quota. The current usage of quota for accelerator type NVIDIA_L4 in region us-central1 is 0 out of 28.

Deploying the model to the endpoint. To check the deployment status, you can try one of the following methods:
1) Look for endpoint `ENDPOINT_DISPLAY_NAME` at the [Vertex AI] -> [Online prediction] tab in Cloud Console
2) Use `gcloud ai operations describe OPERATION_ID --region=LOCATION` to find the status of the deployment long-running operation
```
To see details about your deployment, run the gcloud beta ai endpoints list --list-model-garden-endpoints-only command:
```
gcloud beta ai endpoints list --list-model-garden-endpoints-only \
    --region=LOCATION_ID
```
Replace LOCATION_ID with the region where you deployed the model.

The output includes all endpoints that were created from Model Garden and includes information such as the endpoint ID, endpoint name, and whether the endpoint is associated with a deployed model. To find your deployment, look for the endpoint name that was returned from the previous command.

REST

List all deployable models and then get the ID of the model to deploy. You can then deploy the model with its default configuration and endpoint. Or, you can choose to customize your deployment, such as setting a specific machine type or using a dedicated endpoint.

1. List models that you can deploy

Before using any of the request data, make the following replacements:

PROJECT_ID: Your project ID.
QUERY_PARAMETERS: To list Model Garden models, add the following query parameters listAllVersions=True&filter=is_deployable(true). To list Hugging Face models, set the filter to alt=json&is_hf_wildcard(true)+AND+labels.VERIFIED_DEPLOYMENT_CONFIG%3DVERIFIED_DEPLOYMENT_SUCCEED&listAllVersions=True.

HTTP method and URL:

GET https://aiplatform.googleapis.com/v1beta1/publishers/*/models?QUERY_PARAMETERS

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Execute the following command:

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: PROJECT_ID" \
     "https://aiplatform.googleapis.com/v1beta1/publishers/*/models?QUERY_PARAMETERS"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "PROJECT_ID" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://aiplatform.googleapis.com/v1beta1/publishers/*/models?QUERY_PARAMETERS" | Select-Object -Expand Content

You receive a JSON response similar to the following.

{
  "publisherModels": [
    {
      "name": "publishers/google/models/gemma3",
      "versionId": "gemma-3-1b-it",
      "openSourceCategory": "GOOGLE_OWNED_OSS_WITH_GOOGLE_CHECKPOINT",
      "supportedActions": {
        "openNotebook": {
          "references": {
            "us-central1": {
              "uri": "https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gradio_streaming_chat_completions.ipynb"
            }
          },
          "resourceTitle": "Notebook",
          "resourceUseCase": "Chat Completion Playground",
          "resourceDescription": "Chat with deployed Gemma 2 endpoints via Gradio UI."
        },
        "deploy": {
          "modelDisplayName": "gemma-3-1b-it",
          "containerSpec": {
            "imageUri": "us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20250312_0916_RC01",
            "args": [
              "python",
              "-m",
              "vllm.entrypoints.api_server",
              "--host=0.0.0.0",
              "--port=8080",
              "--model=gs://vertex-model-garden-restricted-us/gemma3/gemma-3-1b-it",
              "--tensor-parallel-size=1",
              "--swap-space=16",
              "--gpu-memory-utilization=0.95",
              "--disable-log-stats"
            ],
            "env": [
              {
                "name": "MODEL_ID",
                "value": "google/gemma-3-1b-it"
              },
              {
                "name": "DEPLOY_SOURCE",
                "value": "UI_NATIVE_MODEL"
              }
            ],
            "ports": [
              {
                "containerPort": 8080
              }
            ],
            "predictRoute": "/generate",
            "healthRoute": "/ping"
          },
          "dedicatedResources": {
            "machineSpec": {
              "machineType": "g2-standard-12",
              "acceleratorType": "NVIDIA_L4",
              "acceleratorCount": 1
            }
          },
          "publicArtifactUri": "gs://vertex-model-garden-restricted-us/gemma3/gemma3.tar.gz",
          "deployTaskName": "vLLM 128K context",
          "deployMetadata": {
            "sampleRequest": "{\n    \"instances\": [\n        {\n          \"@requestFormat\": \"chatCompletions\",\n          \"messages\": [\n              {\n                  \"role\": \"user\",\n                  \"content\": \"What is machine learning?\"\n              }\n          ],\n          \"max_tokens\": 100\n        }\n    ]\n}\n"
          }
        },
        ...

2. Deploy a model

Deploy a model from Model Garden or a model from Hugging Face. You can also customize the deployment by specifying additional JSON fields.

Deploy a model with its default configuration.

Before using any of the request data, make the following replacements:

LOCATION: A region where the model is deployed.
PROJECT_ID: Your project ID.
MODEL_ID: The ID of the model to deploy, which you can get from listing all the deployable models. The ID uses the following format: publishers/PUBLISHER_NAME/models/ MODEL_NAME@MODEL_VERSION.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION:deploy

Request JSON body:

{
  "publisher_model_name": "MODEL_ID",
  "model_config": {
    "accept_eula": "true"
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

cat > request.json << 'EOF'
{
  "publisher_model_name": "MODEL_ID",
  "model_config": {
    "accept_eula": "true"
  }
}
EOF

Then execute the following command to send your REST request:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION:deploy"

PowerShell

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

@'
{
  "publisher_model_name": "MODEL_ID",
  "model_config": {
    "accept_eula": "true"
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION:deploy" | Select-Object -Expand Content

You receive a JSON response similar to the following.

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1beta1.DeployOperationMetadata",
    "genericMetadata": {
      "createTime": "2025-03-13T21:44:44.538780Z",
      "updateTime": "2025-03-13T21:44:44.538780Z"
    },
    "publisherModel": "publishers/google/models/gemma3@gemma-3-1b-it",
    "destination": "projects/PROJECT_ID/locations/LOCATION",
    "projectNumber": "PROJECT_ID"
  }
}

Deploy a Hugging Face model

Before using any of the request data, make the following replacements:

LOCATION: A region where the model is deployed.
PROJECT_ID: Your project ID.
MODEL_ID: The Hugging Face model ID model to deploy, which you can get from listing all the deployable models. The ID uses the following format: PUBLISHER_NAME/MODEL_NAME.
ACCESS_TOKEN: If the model is gated, provide an access token.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION:deploy

Request JSON body:

{
  "hugging_face_model_id": "MODEL_ID",
  "hugging_face_access_token": "ACCESS_TOKEN",
  "model_config": {
    "accept_eula": "true"
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

cat > request.json << 'EOF'
{
  "hugging_face_model_id": "MODEL_ID",
  "hugging_face_access_token": "ACCESS_TOKEN",
  "model_config": {
    "accept_eula": "true"
  }
}
EOF

Then execute the following command to send your REST request:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION:deploy"

PowerShell

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

@'
{
  "hugging_face_model_id": "MODEL_ID",
  "hugging_face_access_token": "ACCESS_TOKEN",
  "model_config": {
    "accept_eula": "true"
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION:deploy" | Select-Object -Expand Content

You receive a JSON response similar to the following.

{
  "name": "projects/PROJECT_ID/locations/us-central1LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1beta1.DeployOperationMetadata",
    "genericMetadata": {
      "createTime": "2025-03-13T21:44:44.538780Z",
      "updateTime": "2025-03-13T21:44:44.538780Z"
    },
    "publisherModel": "publishers/PUBLISHER_NAME/model/MODEL_NAME",
    "destination": "projects/PROJECT_ID/locations/LOCATION",
    "projectNumber": "PROJECT_ID"
  }
}

Deploy a model with customizations

Before using any of the request data, make the following replacements:

LOCATION: A region where the model is deployed.
PROJECT_ID: Your project ID.
MODEL_ID: The ID of the model to deploy, which you can get from listing all the deployable models. The ID uses the following format: publishers/PUBLISHER_NAME/models/ MODEL_NAME@MODEL_VERSION, such as google/gemma@gemma-2b or stabilityai/stable-diffusion-xl-base-1.0.
MACHINE_TYPE: Defines the set of resources to deploy for your model, such as g2-standard-4.
ACCELERATOR_TYPE: Specifies accelerators to add to your deployment to help improve performance when working with intensive workloads, such as NVIDIA_L4
ACCELERATOR_COUNT: The number of accelerators to use in your deployment.
reservation_affinity_type: To use an existing Compute Engine reservation for your deployment, specify any reservation or a specific one. If you specify this value, don't specify spot.
spot: Whether to use spot VMs for your deployment.
IMAGE_URI: The location of the container image to use, such as us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20241016_0916_RC00_maas
CONTAINER_ARGS: Arguments to pass to the container during the deployment.
CONTAINER_PORT: A port number for your container.
fast_tryout_enabled: When testing a model, you can choose to use a faster deployment. This option is available only for the highly-used models with certain machine types. If enabled, you cannot specify model or deployment configurations.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION:deploy

Request JSON body:

{
  "publisher_model_name": "MODEL_ID",
  "deploy_config": {
    "dedicated_resources": {
      "machine_spec": {
        "machine_type": "MACHINE_TYPE",
        "accelerator_type": "ACCELERATOR_TYPE",
        "accelerator_count": ACCELERATOR_COUNT,
        "reservation_affinity": {
          "reservation_affinity_type": "ANY_RESERVATION"
        }
      },
      "spot": "false"
    }
  },
  "model_config": {
    "accept_eula": "true",
    "container_spec": {
      "image_uri": "IMAGE_URI",
      "args": [CONTAINER_ARGS ],
      "ports": [
        {
          "container_port": CONTAINER_PORT
        }
      ]
    }
  },
  "deploy_config": {
    "fast_tryout_enabled": false
  },
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

cat > request.json << 'EOF'
{
  "publisher_model_name": "MODEL_ID",
  "deploy_config": {
    "dedicated_resources": {
      "machine_spec": {
        "machine_type": "MACHINE_TYPE",
        "accelerator_type": "ACCELERATOR_TYPE",
        "accelerator_count": ACCELERATOR_COUNT,
        "reservation_affinity": {
          "reservation_affinity_type": "ANY_RESERVATION"
        }
      },
      "spot": "false"
    }
  },
  "model_config": {
    "accept_eula": "true",
    "container_spec": {
      "image_uri": "IMAGE_URI",
      "args": [CONTAINER_ARGS ],
      "ports": [
        {
          "container_port": CONTAINER_PORT
        }
      ]
    }
  },
  "deploy_config": {
    "fast_tryout_enabled": false
  },
}
EOF

Then execute the following command to send your REST request:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION:deploy"

PowerShell

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

@'
{
  "publisher_model_name": "MODEL_ID",
  "deploy_config": {
    "dedicated_resources": {
      "machine_spec": {
        "machine_type": "MACHINE_TYPE",
        "accelerator_type": "ACCELERATOR_TYPE",
        "accelerator_count": ACCELERATOR_COUNT,
        "reservation_affinity": {
          "reservation_affinity_type": "ANY_RESERVATION"
        }
      },
      "spot": "false"
    }
  },
  "model_config": {
    "accept_eula": "true",
    "container_spec": {
      "image_uri": "IMAGE_URI",
      "args": [CONTAINER_ARGS ],
      "ports": [
        {
          "container_port": CONTAINER_PORT
        }
      ]
    }
  },
  "deploy_config": {
    "fast_tryout_enabled": false
  },
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION:deploy" | Select-Object -Expand Content

You receive a JSON response similar to the following.

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1beta1.DeployOperationMetadata",
    "genericMetadata": {
      "createTime": "2025-03-13T21:44:44.538780Z",
      "updateTime": "2025-03-13T21:44:44.538780Z"
    },
    "publisherModel": "publishers/google/models/gemma3@gemma-3-1b-it",
    "destination": "projects/PROJECT_ID/locations/LOCATION",
    "projectNumber": "PROJECT_ID"
  }
}

Console

In the Google Cloud console, go to the Model Garden page.

Go to Model Garden
Find a supported model that you want to deploy, and click its model card.
Click Deploy to open the Deploy model pane.
In the Deploy model pane, specify details for your deployment.
1. Use or modify the generated model and endpoint names.
2. Select a location to create your model endpoint in.
3. Select a machine type to use for each node of your deployment.
4. To use a Compute Engine reservation, under the Deployment settings section, select Advanced.
  
  For the Reservation type field, select a reservation type. The reservation must match your specified machine specs.
  - Automatically use created reservation: Vertex AI automatically selects an allowed reservation with matching properties. If there's no capacity in the automatically selected reservation, Vertex AI uses the general Google Cloud resource pool.
  - Select specific reservations: Vertex AI uses a specific reservation. If there's no capacity for your selected reservation, an error is thrown.
  - Don't use (default): Vertex AI uses the general Google Cloud resource pool. This value has the same effect as not specifying a reservation.
Click Deploy.

Deploy a partner model and make prediction requests

Before you begin, you must have an agreement with the partner. This agreement includes agreeing to any partner specific terms and licensing requirements and pricing. For more information or to initiate contact with a partner, see the partner documentation on their Model Garden model card and click Contact sales.

You must deploy on the partner's required machine types, as described in the "Recommended hardware configuration" section on their Model Garden model card. When deployed, the model serving resources are located in a secure Google-managed project.

Vertex AI SDK for Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Vertex AI SDK for Python API reference documentation.

In your code, replace the following placeholders:

LOCATION: The region where you plan to deploy the model and endpoint.
PROJECT_ID: Your project ID.
DISPLAY_NAME: A descriptive name for the associated resource.
PUBLISHER_NAME: The name of partner that provides the model to upload or deploy.
PUBLISHER_MODEL_NAME: The name of the model to upload.
MACHINE_TYPE: Defines the set of resources to deploy for your model, such as g2-standard-4. You must match one of the confirgurations provided by the partner.
ACCELERATOR_TYPE: Specifies accelerators to add to your deployment to help improve performance when working with intensive workloads, such as NVIDIA_L4. You must match one of the confirgurations provided by the partner.
ACCELERATOR_COUNT: The number of accelerators to use. You must match one of the confirgurations provided by the partner.
REQUEST_PAYLOAD: The fields and values to include in your prediction request. View the partner's Model Garden model card to see the available fields.

from google.cloud import aiplatform

aiplatform.init(project=PROJECT_ID, location=LOCATION)

# Upload a model
model = aiplatform.Model.upload(
    display_name="DISPLAY_NAME_MODEL",
    model_garden_source_model_name = f"publishers/PUBLISHER_NAME/models/PUBLISHER_MODEL_NAME",
)

# Create endpoint
my_endpoint = aiplatform.Endpoint.create(display_name="DISPLAY_NAME_ENDPOINT")

# Deploy model
MACHINE_TYPE = "MACHINE_TYPE"  # @param {type: "string"}
ACCELERATOR_TYPE = "ACCELERATOR_TYPE" # @param {type: "string"}
ACCELERATOR_COUNT = ACCELERATOR_COUNT # @param {type: "number"}

model.deploy(
    endpoint=my_endpoint,
    deployed_model_display_name="DISPLAY_NAME_DEPLOYED_MODEL",
    traffic_split={"0": 100},
    machine_type=MACHINE_TYPE,
    accelerator_type=ACCELERATOR_TYPE,
    accelerator_count=ACCELERATOR_COUNT,
    min_replica_count=1,
    max_replica_count=1,
)

# Unary call for predictions
PAYLOAD = {
    REQUEST_PAYLOAD
}

request = json.dumps(PAYLOAD)

response = my_endpoint.raw_predict(
    body = request,
    headers = {'Content-Type':'application/json'}
)

print(response)

# Streaming call for predictions
PAYLOAD = {
    REQUEST_PAYLOAD
}

request = json.dumps(PAYLOAD)

for stream_response in my_endpoint.stream_raw_predict(
    body = request,
    headers = {'Content-Type':'application/json'}
):
    print(stream_response)

REST

In the sample curl commands, replace the following placeholders:

LOCATION: The region where you plan to deploy the model and endpoint.
PROJECT_ID: Your project ID.
DISPLAY_NAME: A descriptive name for the associated resource.
PUBLISHER_NAME: The name of partner that provides the model to upload or deploy.
PUBLISHER_MODEL_NAME: The name of the model to upload.
ENDPOINT_ID: The ID of the endpoint.
MACHINE_TYPE: Defines the set of resources to deploy for your model, such as g2-standard-4. You must match one of the confirgurations provided by the partner.
ACCELERATOR_TYPE: Specifies accelerators to add to your deployment to help improve performance when working with intensive workloads, such as NVIDIA_L4. You must match one of the confirgurations provided by the partner.
ACCELERATOR_COUNT: The number of accelerators to use. You must match one of the confirgurations provided by the partner.
REQUEST_PAYLOAD: The fields and values to include in your prediction request. View the partner's Model Garden model card to see the available fields.

Upload a model to add it to your Model Registry.

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapi.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/models:upload \
-d '{
  "model": {
    "displayName": "DISPLAY_NAME_MODEL",
    "baseModelSource": {
      "modelGardenSource": {
        "publicModelName": f"publishers/PUBLISHER_NAME/models/PUBLISHER_MODEL_NAME",
      }
    }
  }
}'

Create an endpoint.

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapi.com/v1/projects/PROJECT_ID/locations/LOCATION/endpoints \
-d '{
  "displayName": "DISPLAY_NAME_ENDPOINT"
}'

Deploy the uploaded model to the endpoint.

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapi.com/v1/projects/PROJECT_ID/locations/LOCATION/endpoints/ENDPOINT_ID:deployModel \
-d '{
  "deployedModel": {
    "model": f"projects/PROJECT_ID/locations/LOCATION/models/MODEL_ID",
    "displayName": "DISPLAY_NAME_DEPLOYED_MODEL",
    "dedicatedResources": {
     "machineSpec": {
        "machineType": "MACHINE_TYPE",
        "acceleratorType": "ACCELERATOR_TYPE",
        "acceleratorCount":"ACCELERATOR_COUNT",
     },
     "minReplicaCount": 1,
     "maxReplicaCount": 1
    },
  },
  "trafficSplit": {
    "0": 100
  }
}'

After the model is deployed, you can make an unary or streaming call for predictions. View the partner's Model Garden model card to see which API methods are supported.

Sample unary call:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapi.com/v1/projects/PROJECT_ID/locations/LOCATION/endpoints/ENDPOINT_ID:rawPredict \
-d 'REQUEST_PAYLOAD'

Sample streaming call:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapi.com/v1/projects/PROJECT_ID/locations/LOCATION/endpoints/ENDPOINT_ID:streamRawPredict \
-d 'REQUEST_PAYLOAD'

Console

In the Google Cloud console, go to the Model Garden page.

Go to Model Garden
To find a specific model, enter its name in the Model Garden search box.
To view all the models that you can self-deploy, in the Model collections section of the filter pane, select Self-deploy partner models. The resulting list includes all the self-deployable partner models.
Click the name of the model to deploy, which opens its model card.
Click Deploy options.
In the Deploy on Vertex AI pane, configure your deployment such as the location and machine type.
Click Deploy.

After the deployment is complete, you can request predictions by using the SDK or API. Additional instructions are available in the "Documentation" section on the model card.

Undeploy models and delete resources

To stop a deployed model from using resources in your project, undeploy your model from its endpoint. You must undeploy a model before you can delete the endpoint and the model.

Undeploy models

Undeploy a model from its endpoint.

Vertex AI SDK for Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Vertex AI SDK for Python API reference documentation.

In your code, replace:

PROJECT_ID with your project ID
LOCATION with your region, for example, "us-central1"
ENDPOINT_ID with your endpoint ID

from google.cloud import aiplatform

aiplatform.init(project=PROJECT_ID, location=LOCATION)

# To find out which endpoints are available, un-comment the line below:
# endpoints = aiplatform.Endpoint.list()

endpoint = aiplatform.Endpoint(ENDPOINT_ID)
endpoint.undeploy_all()

gcloud

In these commands, replace:

PROJECT_ID with your project name
LOCATION_ID with the region where you deployed the model and endpoint
ENDPOINT_ID with the endpoint ID
MODEL_ID with the model ID from the list model command
DEPLOYED_MODEL_ID with the deployed model ID

Find the endpoint ID that is associated with your deployment by running the gcloud ai endpoints list command.
```
gcloud ai endpoints list \
    --project=PROJECT_ID \
    --region=LOCATION_ID
```

Find the model ID by running the gcloud ai models list command.

gcloud ai models list \
    --project=PROJECT_ID \
    --region=LOCATION_ID

Use the model ID from the previous command to get the deployed model ID by running the gcloud ai models describe command.

gcloud ai models describe MODEL_ID \
    --project=PROJECT_ID \
    --region=LOCATION_ID

The abbreviated output looks like the following example. In the output, the ID is called deployedModelId.

Using endpoint [https://us-central1-aiplatform.googleapis.com/]
artifactUri: [URI removed]
baseModelSource:
  modelGardenSource:
    publicModelName: publishers/google/models/gemma2
...
deployedModels:
- deployedModelId: '1234567891234567891'
  endpoint: projects/12345678912/locations/us-central1/endpoints/12345678912345
displayName: gemma2-2b-it-12345678912345
etag: [ETag removed]
modelSourceInfo:
  sourceType: MODEL_GARDEN
name: projects/123456789123/locations/us-central1/models/gemma2-2b-it-12345678912345
...

Run the gcloud ai endpoints undeploy-model command to undeploy the model from the endpoint by using the endpoint ID and the deployed model ID from the previous commands.
```
gcloud ai endpoints undeploy-model ENDPOINT_ID \
    --project=PROJECT_ID \
    --region=LOCATION_ID \
    --deployed-model-id=DEPLOYED_MODEL_ID
```
This command produces no output.

Console

In the Google Cloud console, go to the Endpoints tab on the Online prediction page.

Go to Endpoints
In the Region drop-down list, choose the region where your endpoint is located.
Click the endpoint name to open the details page.
On the row for the model, click Actions, and then select Undeploy model from endpoint.
In the Undeploy model from endpoint dialog, click Undeploy.

Delete endpoints

Delete the Vertex AI endpoint that was associated with your model deployment.

Vertex AI SDK for Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Vertex AI SDK for Python API reference documentation.

In your code, replace:

PROJECT_ID with your project ID
LOCATION with your region, for example, "us-central1"
ENDPOINT_ID with your endpoint ID

from google.cloud import aiplatform

aiplatform.init(project=PROJECT_ID, location=LOCATION)

# To find out which endpoints are available, un-comment the line below:
# endpoints = aiplatform.Endpoint.list()

endpoint = aiplatform.Endpoint(ENDPOINT_ID)
endpoint.delete()

gcloud

In these commands, replace:

PROJECT_ID with your project name
LOCATION_ID with the region where you deployed the model and endpoint
ENDPOINT_ID with the endpoint ID

Get the endpoint ID to delete by running the gcloud ai endpoints list command. This command lists the endpoint IDs for all endpoints in your project.
```
gcloud ai endpoints list \
    --project=PROJECT_ID \
    --region=LOCATION_ID
```
Run the gcloud ai endpoints delete command to delete the endpoint.
```
gcloud ai endpoints delete ENDPOINT_ID \
    --project=PROJECT_ID \
    --region=LOCATION_ID
```
When prompted, type y to confirm. This command produces no output.

Console

In the Google Cloud console, go to the Endpoints tab on the Online prediction page.

Go to Endpoints
In the Region drop-down list, choose the region your endpoint is located.
At the end of the endpoint's row, click Actions, and then select Delete endpoint.
In the confirmation prompt, click Confirm.

Delete models

Delete the model resource that was associated with your model deployment.

Vertex AI SDK for Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Vertex AI SDK for Python API reference documentation.

In your code, replace:

PROJECT_ID with your project ID
LOCATION with your region, for example, "us-central1"
MODEL_ID with your model ID

from google.cloud import aiplatform

aiplatform.init(project=PROJECT_ID, location=LOCATION)

# To find out which models are available in Model Registry, un-comment the line below:
# models = aiplatform.Model.list()

model = aiplatform.Model(MODEL_ID)
model.delete()

gcloud

In these commands, replace:

PROJECT_ID with your project name
LOCATION_ID with the region where you deployed the model and endpoint
MODEL_ID with the model ID from the list model command

Find the model ID to delete by running the gcloud ai models list command.

gcloud ai models list \
    --project=PROJECT_ID \
    --region=LOCATION_ID

Run the gcloud ai models delete command to delete the model by providing the model ID and the model's location.
```
gcloud ai models delete MODEL_ID \
    --project=PROJECT_ID \
    --region=LOCATION_ID
```

Console

Go to the Model Registry page from the Vertex AI section in the Google Cloud console.

Go to the Model Registry page
In the Region drop-down list, choose the region where you deployed your model.
On the row for your model, click Actions and then select Delete model.

When you delete the model, all associated model versions and evaluations are deleted from your Google Cloud project.
In the confirmation prompt, click Delete.

View code samples

Most of the model cards for task-specific solutions models contain code samples that you can copy and test.

In the Google Cloud console, go to the Model Garden page.

Go to Model Garden
Find a supported model that you want to view code samples for and click the Documentation tab.
The page scrolls to the documentation section with sample code embedded in place.

Create a vision app

The model cards for applicable computer vision models support creating a vision application.

In the Google Cloud console, go to the Model Garden page.

Go to Model Garden
Find a vision model in the Task specific solutions section that you want to use to create a vision application and click View details.
Click Build app.

You're taken to Vertex AI Vision.
In Application name, enter a name for your application and click Continue.
Select a billing plan and click Create.

You're taken to Vertex AI Vision Studio where you can continue creating your computer vision application.

Use models in Model Garden Stay organized with collections Save and categorize content based on your preferences.

Send test prompts

Tune a model

Tune in a notebook

Deploy an open model

Vertex AI SDK for Python

gcloud

REST

1. List models that you can deploy

curl

PowerShell

2. Deploy a model

Deploy a model with its default configuration.

curl

PowerShell

Deploy a Hugging Face model

curl

PowerShell

Deploy a model with customizations

curl

PowerShell

Console

Deploy a partner model and make prediction requests

Vertex AI SDK for Python

REST

Console

Undeploy models and delete resources

Undeploy models

Vertex AI SDK for Python

gcloud

Console

Delete endpoints

Vertex AI SDK for Python

gcloud

Console

Delete models

Vertex AI SDK for Python

gcloud

Console

View code samples

Create a vision app

Use models in Model Garden
Stay organized with collections Save and categorize content based on your preferences.