studio-in-vpc

Amazon SageMaker Studio in VPC

This CDK Python project is for Amazon SageMaker Studio with VPC only network access type.

As a result, you won't be able to run a Studio notebook unless your VPC has an interface endpoint to the SageMaker API and runtime, or a NAT gateway with internet access, and your security groups allow outbound connections. The above diagram shows a configuration for using VPC-only mode.

The cdk.json file tells the CDK Toolkit how to execute your app.

This project is set up like a standard Python project. The initialization process also creates a virtualenv within this project, stored under the .venv directory. To create the virtualenv it assumes that there is a python3 (or python for Windows) executable in your path with access to the venv package. If for any reason the automatic creation of the virtualenv fails, you can create the virtualenv manually.

To manually create a virtualenv on MacOS and Linux:

$ python3 -m venv .venv

After the init process completes and the virtualenv is created, you can use the following step to activate your virtualenv.

$ source .venv/bin/activate

If you are a Windows platform, you would activate the virtualenv like this:

% .venv\Scripts\activate.bat

Once the virtualenv is activated, you can install the required dependencies.

(.venv) $ pip install -r requirements.txt

At this point you can now synthesize the CloudFormation template for this code.

(.venv) $ export CDK_DEFAULT_ACCOUNT=$(aws sts get-caller-identity --query Account --output text)
(.venv) $ export CDK_DEFAULT_REGION=$(aws configure get region)
(.venv) $ cdk synth -c vpc_name='your-existing-vpc-name' \
              -c sagmaker_jupyterlab_arn='default-JupterLab-image-arn'

Use cdk deploy command to create the stack shown above.

(.venv) $ cdk deploy -c vpc_name='your-existing-vpc-name' \
              -c sagmaker_jupyterlab_arn='default-JupterLab-image-arn'

For example, if we try to set JupyterLab3 to the default JupyterLab in us-east-1 region, we can deploy like this:

(.venv) $ cdk deploy -c vpc_name=default \
              -c sagmaker_jupyterlab_arn='arn:aws:sagemaker:us-east-1:081325390199:image/jupyter-server-3'

Otherwise, you can pass context varialbes by cdk.contex.json file. Here is an example:

(.venv) $ cat cdk.context.json
{
  "vpc_name": "default",
  "sagmaker_jupyterlab_arn": "arn:aws:sagemaker:us-east-1:081325390199:image/jupyter-server-3"
}

For more information about the available JupyterLab versions for each Region, see Amazon SageMaker - Setting a default JupyterLab version

To add additional dependencies, for example other CDK libraries, just add them to your setup.py file and rerun the pip install -r requirements.txt command.

Clean Up

Delete the CloudFormation stack by running the below command.

(.venv) $ cdk destroy --force

Useful commands

• cdk ls list all stacks in the app
• cdk synth emits the synthesized CloudFormation template
• cdk deploy deploy this stack to your default AWS account/region
• cdk diff compare deployed stack with current state
• cdk docs open CDK documentation

Learn more

• Securing Amazon SageMaker Studio connectivity using a private VPC (2020-10-22)
• Connect SageMaker Studio Notebooks in a VPC to External Resources
• Amazon SageMaker - Setting a default JupyterLab version
• SageMaker Studio Permissions Required to Use Projects
• Automate Amazon SageMaker Studio setup using AWS CDK (2021-06-16)
  • aws-samples/aws-cdk-sagemaker-studio
• Set up Amazon SageMaker Studio with Jupyter Lab 3 using the AWS CDK (2023-01-23)
  • aws-cdk-native-sagemaker-studio
• Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks

Enjoy!