AWS SageMaker Setup Guide

This guide walks you through setting up a SageMaker notebook instance using credentials and configurations from a shared CSV file.

Prerequisites

• CSV file containing:
  • AWS login URL (e.g., https://your-account.signin.aws.amazon.com/console)
  • User ID and password
  • (Optional) Default region or other configurations

Step 1: Log in to AWS

1. Open the shared CSV file and locate:
   • AWS Login URL (e.g., https://[account-id].signin.aws.amazon.com/console)
   • User ID and Password
2. Navigate to the URL in your browser and log in with the credentials.

Step 2: Open SageMaker

1. After logging in, search for “SageMaker” in the AWS Services search bar.
2. Click Amazon SageMaker to open the dashboard.

Step 3: Create a Notebook Instance

1. In the left sidebar, go to Notebook > Notebook instances.
2. Click Create notebook instance and configure:
   • Name: your preferred name.
   • Instance type: ml.t3.medium (or ml.g4dn.xlarge for GPU support).
   • Volume size: Set to 10GB (required for larger datasets).
3. Under Git repositories, click Add a repo:
   • Repository source: GitHub.
   • URL: https://github.com/symplecticgeometry/equivariant-neural-networks-and-equivarification.git.
   • Check Clone recursively (if the repo has submodules).

Step 4: Launch JupyterLab

• Wait for the instance status to change to “InService” (2–5 minutes).
• Click Open JupyterLab.

Step 5: Verify Python Environment

Create a new Jupyter Notebook (**Tensorflow ** kernel).