The deployment process differs significantly when using a private S3 bucket versus the Hugging Face Hub, primarily in terms of model accessibility, security, and latency.
Deployment Using the Hugging Face Hub
Deploying models directly from the Hugging Face Hub involves accessing models over the internet. Hereâs how it typically works:
1. Model Selection: You select the desired model from the Hugging Face Hub, which hosts a wide range of pre-trained models.
2. Deployment Configuration: You configure your deployment environment, such as choosing the appropriate instance type and setting up the necessary infrastructure (e.g., Azure Machine Learning or Amazon SageMaker).
3. Model Download: During deployment, the model is downloaded from the Hugging Face Hub to your deployment environment. This step may introduce latency and requires internet connectivity.
4. Security Considerations: Since models are accessed over the internet, there may be security concerns related to data privacy and compliance.
Deployment Using a Private S3 Bucket
Deploying models from a private S3 bucket offers enhanced security and control over the deployment process:
1. Model Preparation: You first download the model weights from the Hugging Face Hub and upload them to your private S3 bucket. This step allows for vulnerability scans and ensures that the model is stored securely within your AWS account.
2. Deployment Configuration: You configure your deployment environment similar to using the Hugging Face Hub, but you specify the S3 bucket path as the model source.
3. Model Retrieval: During deployment, the model is retrieved from your S3 bucket, reducing latency since the model is stored closer to your deployment environment (e.g., Amazon SageMaker).
4. Security and Compliance: This approach enhances security by keeping model weights within your AWS account, allowing for better control over access and compliance with organizational policies.
In summary, using a private S3 bucket provides more control over model storage and deployment, enhances security, and reduces latency compared to deploying directly from the Hugging Face Hub. However, it requires additional steps to upload models to the S3 bucket before deployment[3].
Citations:
[1] https://support.atlassian.com/bitbucket-cloud/docs/deploy-to-aws-with-s3/
[2] https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-from-huggingface?view=azureml-api-2
[3] https://aws.amazon.com/blogs/machine-learning/optimize-hosting-deepseek-r1-distilled-models-with-hugging-face-tgi-on-amazon-sagemaker-ai/
[4] https://docs.travis-ci.com/user/deployment/s3/
[5] https://www.run.ai/blog/how-to-deploy-hugging-face-models-with-run-ai
[6] https://huggingface.co/blog/introducing-private-hub
[7] https://www.edstem.com/blog/deploying-running-app-s3-cloudfront/
[8] https://huggingface.co/blog/alvarobartt/deploy-from-hub-to-vertex-ai
[9] https://stackoverflow.com/questions/79138919/deploying-llm-from-s3-on-amazon-sagemaker