Gen AI on AWS - SageMaker

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SageMaker is a fully managed machine learning (ML) service which is especially designed to simplify the process of building, training, and deploying machine learning models. It also includes Generative AI (Gen AI) models.

Generative AI models like GPT (Generative Pre-trained Transformer) and GANs (Generative Adversarial Networks), require high computational resources to train effectively. AWS SageMaker provides an integrated environment that simplifies the process of data preprocessing to model deployment./p>

How does SageMaker Support Generative AI?

SageMaker provides a set of features that are highly useful in generative AI −

Pre-built Algorithms

SageMaker provides pre-built algorithms for tasks like NLP, image classification, and many more. It saves the time of user in developing custom code for Gen AI models.

Distributed Training

SageMaker supports distributed training which allows you to train large Gen AI models across multiple GPUs or instances.

SageMaker Studio

SageMaker Studio is a development environment where you can prepare data, build models, and experiment with different hyperparameters.

Built-in AutoML

SageMaker includes AutoML features with the help of which you can automatically tune hyperparameters and optimize the performance of your Gen AI model.

Managed Spot Training

AWS SageMaker allows you to use EC2 Spot Instances for training. It can reduce the cost of running resource-intensive Gen AI models.

Training Gen-AI Models with SageMaker

We need high computation power to train a Generative AI model especially when working with large-scale models like GPT or GANs. AWS SageMaker makes it easier by providing both GPU-accelerated instances and distributed training capabilities.

Deploying Gen-AI Models with SageMaker

Once your model is trained, you can deploy it in a scalable and cost-effective manner by using AWS SageMaker.

You can deploy your model using SageMaker Endpoints, which provides automatic scaling based on traffic. This feature ensures that your Gen AI model can handle increased demand.

Python Program for Training and Deploying Gen AI Model with SageMaker

Here we have highlighted a Python example that shows how to use AWS SageMaker to train and deploy a Generative AI model using a pre-built algorithm.

For this example, we will use a basic Hugging Face pre-trained transformer model like GPT 2 for text generation.

Before executing this example, you must have an AWS account, the necessary AWS credentials, and the sagemaker library installed.

Step 1: Install Necessary Libraries

Install the necessary Python packages using the following command −

pip install sagemaker transformers

Step 2: Set Up SageMaker and AWS Configurations

Import the necessary libraries and setting up the AWS SageMaker environment.

import sagemaker
from sagemaker.huggingface import HuggingFace
import boto3

# Create a SageMaker session
sagemaker_session = sagemaker.Session()

# Set your AWS region
region = boto3.Session().region_name

# Define the execution role (replace with your own role ARN)
role = 'arn:aws:iam::YOUR_AWS_ACCOUNT_ID:role/service-role/AmazonSageMaker-ExecutionRole'

# Define the S3 bucket for storing model artifacts and data 
bucket = 'your-s3-bucket-name'

Step 3: Define the Hugging Face Model Parameters

Here, we need to define the model parameters for training the GPT-2 model using SageMaker.

# Specify the Hugging Face model and its version
huggingface_model = HuggingFace(
    entry_point = 'train.py',  		# Your training script
    source_dir = './scripts',  		# Directory containing your script
    instance_type = 'ml.p3.2xlarge',# GPU instance
    instance_count=1,
    role = role,
    transformers_version = '4.6.1', # Hugging Face Transformers version
    pytorch_version = '1.7.1',
    py_version = 'py36',
    hyperparameters = {
        'model_name': 'gpt2',  		# Pre-trained GPT-2 model
        'epochs': 3,
        'train_batch_size': 16
    }
)

Step 4: Prepare Training Data

For this example, we need to store preprocessed data in an Amazom S3 bucket. The data can be in CSV, JSON, or plain text format.

# Define the S3 path to your training data
training_data_s3_path = f's3://{bucket}/train-data/'

# Launch the training job
huggingface_model.fit(training_data_s3_path)

Step 5: Deploy the Trained Model for Inference

After training the model, deploy it to a SageMaker endpoint to make real-time inferences.

# Deploy the model to a SageMaker endpoint
predictor = huggingface_model.deploy(
   initial_instance_count=1,
   instance_type='ml.m5.large'
)

Step 6: Generate Text Using the Deployed Model

Once the model is deployed, you can make predictions by sending prompts to the endpoint for text generation.

# Define a prompt for text generation
prompt = "Once upon a time"

# Use the predictor to generate text
response = predictor.predict({
    'inputs': prompt
})

# Print the generated text
print(response)

Step 7: Clean Up Resources

After you have completed your tasks, it is recommended to delete the deployed endpoint to avoid incurring unnecessary charges.

predictor.delete_endpoint()
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