Example Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker.
Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models.
The SageMaker example notebooks are Jupyter notebooks that demonstrate the usage of Amazon SageMaker.
The Sagemaker Example Community repository are additional notebooks, beyond those critical for showcasing key SageMaker functionality, can be shared and explored by the commmunity.
The quickest setup to run example notebooks includes:
- An AWS account
- Proper IAM User and Role setup
- An Amazon SageMaker Notebook Instance
- An S3 bucket
They can be accessed by cloning this repo inside Jupyter or just uploading/copying the example.
Although most examples utilize key Amazon SageMaker functionality like distributed, managed training or real-time hosted endpoints, these notebooks can be run outside of Amazon SageMaker Notebook Instances with minimal modification (updating IAM role definition and installing the necessary libraries).
- Aguila 7b Hugging Face Large Model Inference - TGI shows how to deploy common large language models such as projecte-aina/aguila-7b, using Hugging Face Text Generation Inference (TGI) Deep Learning Container on Amazon SageMaker
- Aguila 7b fine-tunning with instruction dataset shows how to fine-tune the falcon 7B aguila model projecte-aina/aguila-7b, using an instructional dataset (in this case an example from the InstructCat collection) with a g5 instance from Amazon Sagemaker.
Apache-2.0 license
This work is funded by the Generalitat de Catalunya within the framework of Projecte AINA.
