Preparing the pre-trained model artifacts

In Chapter 6, SageMaker Training and Debugging Solutions, we created a new folder named CH06, along with a new Notebook using the Data Science image inside the created folder. In this section, we will create a new folder (named CH07), along with a new Notebook inside the created folder. Instead of the Data Science image, we will use the PyTorch 1.10 Python 3.8 CPU Optimized image as the image used in the Notebook since we will download the model artifacts of a pre-trained PyTorch model using the Hugging Face transformers library. Once the Notebook is ready, we will use the Hugging Face transformers library to download a pre-trained model that can be used for sentiment analysis. Finally, we will zip the model artifacts into a model.tar.gz file and upload it to an S3 bucket.

Note

Make sure that you have completed the hands-on solutions in the Getting started with SageMaker and SageMaker Studio section of Chapter 1, Introduction to ML Engineering...