Test Notebook(s)

Contains collection of jupyter notebooks to test various scenarios

Prerequisites

Sagemaker Domain will be deployed within a VPC/Subnet. Ensure that the Security Group Ingress/Egress rules allow required connectivity to the AWS/OnPrem/Target Network resources

VPC Endpoints to be in place such as: * "com.amazonaws..kms" * "com.amazonaws..athena" * "com.amazonaws..bedrock" * "com.amazonaws..glue" * "com.amazonaws..sagemaker.featurestore-runtime" * "com.amazonaws..sagemaker.runtime" * "com.amazonaws..sagemaker.api" * "aws.sagemaker..notebook" * "com.amazonaws..sts" * "com.amazonaws..logs"

General Instructions

Data Scientist/AI engineer who has access to the SSO Group should be able to launch the Sagemaker Studio or Application directly from the SSO Login page. * Once logged in, go to AWS Access Portal * Select 'Applicaitons' Tab * Hover over the applications to see which domain do they belong to (in case you see multiple applicaitons) * Refer to Domain >> ID. Request your Administrator to provide this id. (Example: d-bdmy11uf4r3o) * It will launch your Sagemaker Studio in a new tab * Click on JupyterLab and then click Create Jupyter Lab Space * Provide an appropriate name and click "Create Space" * Wait for Space to be created and then Click "Start" * Once the status is Running , you will see a button 'Open'. Click it * It will upload your Jupyter Lab personal environment * On your Left hand side, you will see 'Upload File' Option. Click on it and browse to your local repo, /ai/datascience-team/notebooks folder. You will see a test.ipynb file. * Select the file and Click 'Open' * Execute Notebook Cells (Shift+Enter) and if it runs fine all the way, your deployment is successful

1. Notebook: basic_test.ipynb

This is a notebook to test the following: 1. Running within Sagemaker Studio, using the execution role 2. No Internet connectivity - should be able to use pre-installed modules 3. Should be able to read/write to S3-Bucket