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README.md

Amazon SageMaker Studio

studio-vpc-internet

This CDK Python project is for Amazon SageMaker Studio.

By default, SageMaker Studio provides a network interface that allows communication with the internet through a VPC managed by SageMaker. Traffic to AWS services like Amazon S3 and CloudWatch goes through an internet gateway, as does traffic that accesses the SageMaker API and SageMaker runtime. Traffic between the domain and your Amazon EFS volume goes through the VPC that you specified when you onboarded to Studio or called the CreateDomain API. The above diagram shows the default configuration.

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 --all

Use cdk deploy command to create the stack shown above.

(.venv) $ cdk deploy --all

If you want to set JupyterLab3 to the default JupyterLab, you can do like this:

(.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 sagmaker_jupyterlab_arn='(optional) default-JupterLab-image-arn' --all

Use cdk deploy command to create the stack shown above with JupyterLab3 as the default JupyerLab.

(.venv) $ cdk deploy -c sagmaker_jupyterlab_arn='(optional) default-JupterLab-image-arn' --all

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 sagmaker_jupyterlab_arn='arn:aws:sagemaker:us-east-1:081325390199:image/jupyter-server-3' --all

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

ℹ️ -c sagmaker_jupyterlab_arn option is not required when synthizing or deploying CDK stacks if you do not want to set JupyterLab3 to the default JupyterLab.

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 --all

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

Enjoy!