Access curated model hubs in Amazon SageMaker JumpStart - Amazon SageMaker
Access your private model hub in StudioAccess your private model hub using the SageMaker Python SDK
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Access curated model hubs in Amazon SageMaker JumpStart

You can access a private model hub either through Studio or through the SageMaker Python SDK.

Access your private model hub in Studio

Important

As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the updated Studio experience. For information about using the Studio Classic application, see Amazon SageMaker Studio Classic.

In Amazon SageMaker Studio, open the JumpStart landing page either through the Home page or the Home menu on the left-side panel. This opens the SageMaker JumpStart landing page where you can explore model hubs and search for models.

  • From the Home page, choose JumpStart in the Prebuilt and automated solutions pane.

  • From the Home menu in the left panel, navigate to the JumpStart node.

For more information on getting started with Amazon SageMaker Studio, see Amazon SageMaker Studio.

From the SageMaker JumpStart landing page in Studio, you can explore any private model hubs that include allow-listed models for your organization. If you only have access to one model hub, then the SageMaker JumpStart landing page takes you directly into that hub. If you have access to multiple hubs, you are taken to the Hubs page.

For more information on fine-tuning, deploying, and evaluating models that you have access to in Studio, see Use foundation models in Studio.

Access your private model hub using the SageMaker Python SDK

You can access your private model hub using the SageMaker Python SDK. Your access to read, use, or edit your curated hub is provided by your administrator.

Note

If a hub is shared across accounts, then the HUB_NAME must be the hub ARN. If a hub is not shared across accounts, then the HUB_NAME can be the hub name.

  1. Install the SageMaker Python SDK and import the necessary Python packages.

    # Install the SageMaker Python SDK
!pip3 install sagemaker --force-reinstall --quiet

# Import the necessary Python packages
import boto3
from sagemaker import Session
from sagemaker.jumpstart.hub.hub import Hub
from sagemaker.jumpstart.model import JumpStartModel
from sagemaker.jumpstart.estimator import JumpStartEstimator

  2. Initalize a SageMaker session and connect to your private hub using the hub name and Region.

    # If a hub is shared across accounts, then the HUB_NAME must be the hub ARN
HUB_NAME="Example-Hub-ARN" 
REGION="us-west-2" 

# Initialize a SageMaker session
sm_client = boto3.client('sagemaker') 
sm_runtime_client = boto3.client('sagemaker-runtime') 
session = Session(sagemaker_client=sm_client, 
                    sagemaker_runtime_client=sm_runtime_client)

# Initialize the private hub
hub = Hub(hub_name=HUB_NAME, sagemaker_session=session)

  3. After connecting to a private hub, you can list all available models in that hub using the following commands:

    response = hub.list_models()
models = response["hub_content_summaries"]
while response["next_token"]:
    response = hub.list_models(next_token=response["next_token"])
    models.extend(response["hub_content_summaries"])
    
print(models)

  4. You can get more information about a specific model using the model name with the following command:

    response = hub.describe_model(model_name="example-model")
print(response)

For more information on fine-tuning and deploying models that you have access to using the SageMaker Python SDK, see Use foundation models with the SageMaker Python SDK.