Gen AI on AWS - Environment Setup
Lets understand how we can set up an AWS account and configure our environment for Generative AI.
Setting up an AWS Account
For using AWS for Generative AI, we first need to create and set up an AWS account. In this section, we will explain step-by-step how you can set up your AWS account −
Step 1: Sign Up for AWS
First, navigate to the AWS website and click "Create an AWS Account". Next, enter your email, create a strong password, and choose a unique AWS account name.
Step 2: Complete Account Setup
To complete account setup, first enter your contact details, including your phone number and address. Next, you need to select the type of account. It depends on your needs and can be either personal or professional.
For billing, you need to provide a valid credit card.
Step 3: Verify Your Identity
AWS will send a verification code via SMS or voice call to confirm your phone number. You need to enter this code to proceed.
Step 4: Choose Support Plan
AWS has several support plans including Basic (free), Developer, Business, and Enterprise. You can choose any one as per your need. Your account is set up now.
Step 5: Log into the AWS Management Console
Now you can log into the AWS Management Console from where you can launch services like EC2 and SageMaker for Generative AI.
Configuring Your AWS Environment
Once you have an AWS account, the next step is to configure your environment for development and deployment of Generative AI models.
We have given here the step-by-step procedure of how you can configure your AWS environment −
Step 1: Set Up IAM Users and Roles
First, create an IAM (Identity and Access Management) user for yourself instead of using the root account for day-to-day operations.
Assign necessary permissions by creating policies that provide access to services like EC2, AWS SageMaker, and Amazon S3.
Finally, enable Multi-Factor Authentication (MFA) for IAM users. It enhances security.
Step 2: Select AWS Services for Generative AI
AWS provides various services like Amazon SageMaker, AWS Lambda, Amazon EC2, and Amazon S3 that you can use for Gen AI tasks.
Step 3: Launch EC2 Instances for Training
For training purposes, we need to launch EC2 Instances. EC2 provides scalable computing resources for training large models.
To start with, you can launch a GPU-enabled EC2 instance (such as p3.2xlarge or g4dn.xlarge). You can also use Spot Instances for cost savings.
Next, use the Deep Learning AMI that comes pre-installed with frameworks like TensorFlow, PyTorch, and MXNet.
Step 4: Configure Networking and Security
To run your instances securely, first set up a VPC (Virtual Private Cloud) and then configure Security Groups to restrict access to your instances.
Step 5: Install Essential Libraries and Frameworks
If you are not using the Deep Learning AMI, install libraries like PyTorch, TensorFlow, or Hugging Face on your EC2 instance or SageMaker notebook.
For example, you can install PyTorch using the following command −
pip install torch torchvision
Step 6: Setup S3 Buckets for Data Storage
Once done with installation of necessary libraries, you need to create an S3 bucket to store your training data, model checkpoints, and logs.
Step 7: Connect and Configure AWS CLI
Next, install the AWS CLI on your local machine to interact with AWS services programmatically.
Once installed, configure AWS CLI with your access key ID and secret access key.
Use the following command −
aws configure
Step 8: Monitor and Optimize Resources
You can use Amazon CloudWatch to monitor the performance of your EC2 instances, keeping track of CPU, memory, and GPU utilization.
For cost control, you can also set up budgets and alarms through AWS Billing and Cost Explorer to track your spending on AI resources.