Sagemaker Iams creator for Brev

• Creates a user with minimal priviledge to
  • create training jobs
  • create models
  • deploy models as a sagemaker endpoint
  • invoke model endpoints
• Creates a role with minimal priviledge for training jobs to
  • retrieve a dataset from a s3 bucket with "sagemaker" in its name
  • output model artifacts to an s3 bucket with "sagemaker" in its name

To use this,

1. Clone the repo and follow the setup instrustions for CDK.
2. Strengthen the IAM policies as you see fit in /policies/sagemaker (will work as is)
3. Run cdk synth && cdk deploy
4. The previous command will ouput in the console
   • brev-iam-maker.accesskeyid => aws_access_key_id
   • brev-iam-maker.secretaccesskey => aws_secret_access_key
   • brev-iam-maker.iamrole => training role name

AWS CDK Setup

Read more about aws cdk here

This project is set up like a standard Python project. The initialization process also creates a virtualenv within this project, stored under the .env 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.

To create a virtualenv on MacOS and Linux:

$ python3 -m venv .env

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

$ source .env/bin/activate

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

% .env\Scripts\activate.bat

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

$ pip install -r requirements.txt

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

$ cdk synth

Next deploy the CloudFormation and show the resulting outputs.

$ cdk deploy

Useful CDK 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

Enjoy!