Key	Value
Environment
Services	S3, SageMaker, Lambda
Integrations	AWS SDK
Categories	Serverless, S3 website, Lambda function URLs, SageMaker, Machine Learning, JavaScript, Python
Level	Intermediate

This is a sample application that demonstrates how to use SageMaker on LocalStack. A simple web frontend allows users to draw a digit and submit it to a locally running SageMaker endpoint. The endpoint returns a prediction of the digit, which is then displayed in the web frontend. Request handling is performed by a Lambda function, accessible via a function URL, that uses the SageMaker SDK to invoke the endpoint.

Here's a short summary of AWS service features we use:

• S3 website
• Lambda function URLs
• SageMaker endpoint

Here's the web application in action:

Create a virtualenv and install all the development dependencies there:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

If you'd like to perform training locally, you'll need to install the ml dev dependencies as well:

pip install -r ml/requirements.txt

You'll also need npm/node installed to build the web application. Please install according to official guidelines: https://github.com/nvm-sh/nvm

As our inference container, we use the PyTorch inference container from the AWS ECR.

aws ecr get-login-password --region eu-central-1 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.eu-central-1.amazonaws.com
docker pull 763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-inference:1.10.2-cpu-py38-ubuntu20.04-sagemaker

Start LocalStack Pro with your API key:

PERSISTENCE=1 LOCALSTACK_API_KEY=... localstack start

First, we install the dependencies for the Web application in the web directory:

(cd web; npm install)

You can then create the AWS infrastructure on LocalStack by running the deploy/deploy_app.py script (make sure to have the virtual environment activated):

source .venv/bin/activate
python deploy/deploy_app.py

This script will create the SageMaker endpoint with the model, which it first uploads to a bucket. The script will also create a lambda function that will be used to invoke the endpoint. Finally, the script will build the web application and then create a s3 website to host it.

Once deployed, visit http://mnist-website.s3-website.localhost.localstack.cloud:4566

Draw something in the canvas and click on the button that says Predict.

After a few moments the resulting prediction should be displayed in the box to the right.

To switch to a serverless SageMaker endpoint you can also execute the deployment script with the additional -s or --serverless flag:

python deploy/deploy_app.py --serverless

The code of this sample application is published under the Apache 2.0 license (see LICENSE).

We appreciate your interest in contributing to our project and are always looking for new ways to improve the developer experience. We welcome feedback, bug reports, and even feature ideas from the community. Please refer to the contributing file for more details on how to get started.