sagemaker/pr_2709/en/tutorials/sagemaker-sdk/setup-sagemaker-sdk.md

Set up the SageMaker SDK

Welcome to the SageMaker SDK tutorials. Spend a few minutes on this page once, and every tutorial after it will work out of the box.

AWS account and SDK

You need an AWS account. If you do not have one yet, the AWS setup guide walks you through it.

Then install the SageMaker Python SDK v3:

pip install "sagemaker>=3.0.0"

These docs and examples use the SageMaker Python SDK v3, which introduces a new framework-agnostic API built around ModelBuilder (inference) and ModelTrainer (training), replacing the v2 HuggingFaceModel and HuggingFace classes.

Where to run

The tutorials work the same wherever you prefer to run them:

• SageMaker Studio
• A SageMaker notebook instance
• Your local environment, as long as your AWS credentials are configured

Execution role

SageMaker runs training jobs and endpoints under an IAM execution role with access to S3. How you provide the role depends on where you run.

SageMaker Studio or a notebook instance — nothing to set up, get_execution_role() finds the role for you:

from sagemaker.core.helper.session_helper import Session, get_execution_role

sess = Session()
role = get_execution_role()

Keep in mind that this only works inside SageMaker: get_execution_role() fails with a region error anywhere else.

Your local environment — look up the role ARN once and pass it yourself:

import boto3
from sagemaker.core.helper.session_helper import Session

iam_client = boto3.client("iam")
role = iam_client.get_role(RoleName="role-name-of-your-iam-role-with-right-permissions")["Role"]["Arn"]
sess = Session()

What's next

You are all set. Continue with Train models or Deploy models — or head back to the Quickstart if you have not run it yet.