AWS SageMaker Example

This example takes the example model provided by AWS to show how to deploy your own machine learning algorithm into a SageMaker container using Terraform.

Wrap model in Docker container and upload to ECR

Get the SageMaker example model from AWS:

git clone https://github.com/awslabs/amazon-sagemaker-examples.git
cd amazon-sagemaker-examples/advanced_functionality/scikit_bring_your_own/container/

Export credentials for your account:

export AWS_ACCESS_KEY_ID=<your-access-key-id>
export AWS_SECRET_ACCESS_KEY=<your-secret-access-key>

Create docker container and push it to ECR:

./build_and_push.sh foo

Deploy model and run test prediction call

In the directory where this README is located, run the following:

terraform init
terraform apply

Go back to amazon-sagemaker-examples/advanced_functionality/scikit_bring_your_own/container/ and make a test call to the deployed model:

aws runtime.sagemaker invoke-endpoint --endpoint-name terraform-sagemaker-example \
    --body "`cat ./local_test/payload.csv`" --content-type "text/csv" "output.dat"

Have a look the predicted values:

cat output.dat

Destroy the deployed model:

terraform destroy