Add models to a private hub - Amazon SageMaker AI
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Add models to a private hub

After creating a private hub, you can then add allow-listed models. For the full list of available JumpStart models, see the Built-in Algorithms with pre-trained Model Table in the SageMaker Python SDK reference.

  1. You can filter through the available models programmatically using the hub.list_sagemaker_public_hub_models() method. You can optionally filter by categories such as framework ("framework == pytorch"), tasks such as image classification ("task == ic"), and more. For more information about filters, see notebook_utils.py. The filter parameter in the hub.list_sagemaker_public_hub_models() method is optional. 

    filter_value = "framework == meta"
response = hub.list_sagemaker_public_hub_models(filter=filter_value)
models = response["hub_content_summaries"]
while response["next_token"]:
    response = hub.list_sagemaker_public_hub_models(filter=filter_value, next_token=response["next_token"])
    models.extend(response["hub_content_summaries"])

print(models)

  2. You can then add the filtered models by specifying the model ARN in the hub.create_model_reference() method.

    for model in models:
    print(f"Adding {model.get('hub_content_name')} to Hub")
    hub.create_model_reference(model_arn=model.get("hub_content_arn"), model_name=model.get("hub_content_name"))