# Access curated model hubs in Amazon SageMaker JumpStart
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You can access a private model hub either through Studio or through the SageMaker Python SDK.

## Access your private model hub in Studio
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**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](studio.md).

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](studio-updated.md).

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](jumpstart-foundation-models-use-studio-updated.md).

## Access your private model hub using the SageMaker Python SDK
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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
   ```

1. Initalize a SageMaker AI 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)
   ```

1. 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)
   ```

1. 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](jumpstart-foundation-models-use-python-sdk.md).