Set Up and Run OpenAI's CLIP on Amazon SageMaker for Inference

This tutorial will guide you through the process of deploying the OpenAI's Contrastive Language–Image Pretraining (CLIP) model for inference using Amazon SageMaker. The primary goal is to help you understand how to create an endpoint for real-time inference, and use SageMaker's Batch Transform feature for offline inference.

For consistency and to make things more interesting, we'll use a theme of identifying and classifying images of different types of animals throughout this tutorial.

First, log in to your AWS account and go to the SageMaker console. In your desired region, create a new SageMaker notebook instance (e.g., 'clip-notebook'). Once the instance is ready, open Jupyter and create a new Python 3 notebook.

In this step, we'll use boto3 to check if our model was successfully uploaded to our S3 bucket. First, let's import the library and initialize our S3 client:

Once the model is uploaded to S3, you can create a SageMaker model. To do this, you need a Docker container that contains the necessary libraries and dependencies to run CLIP. If you don't have this Docker image yet, you would need to create one. For the purpose of this tutorial, let's assume you have a Docker image named 'clip-docker-image' in your Elastic Container Registry (ECR).

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