dockerfile updated

dockerfile

@@ -31,9 +31,6 @@ FROM python:3.10-slim
 # # Run the application
 # CMD ["python", "app.py"]
 
-# Use NVIDIA CUDA base image
-# FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
-
 # Set environment variables
 ENV PYTHONUNBUFFERED=TRUE
 ENV PYTHONDONTWRITEBYTECODE=TRUE

src/deploy_sagemaker.py

@@ -7,9 +7,40 @@ import os
 from datetime import datetime
 
 # Set up logging
-logging.basicConfig(level=logging.INFO)
+logging.basicConfig(
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    level=logging.INFO
+)
 logger = logging.getLogger(__name__)
 
+def create_model_archive(model_path):
+    """
+    Create a model archive if needed
+    
+    Args:
+        model_path (str): Path to model files
+        
+    Returns:
+        str: S3 URI of the model archive
+    """
+    try:
+        # Initialize S3 client
+        s3 = boto3.client('s3')
+        bucket = 'customer-support-gpt'
+        model_key = 'models/model.tar.gz'
+        
+        # Check if model archive exists in S3
+        try:
+            s3.head_object(Bucket=bucket, Key=model_key)
+            logger.info("Model archive already exists in S3")
+        except:
+            logger.info("Model archive not found in S3, will be created during deployment")
+            
+        return f's3://{bucket}/{model_key}'
+    except Exception as e:
+        logger.error(f"Error creating model archive: {str(e)}")
+        raise
+
 def deploy_app(acc_id, region_name, role_arn, ecr_repo_name, endpoint_name="customer-support-chatbot"):
     """
     Deploys a Gradio app as a SageMaker endpoint using an ECR image.
@@ -19,40 +50,99 @@ def deploy_app(acc_id, region_name, role_arn, ecr_repo_name, endpoint_name="cust
         region_name (str): AWS region name
         role_arn (str): IAM role ARN for SageMaker
         ecr_repo_name (str): ECR repository name
-        endpoint_name (str): SageMaker endpoint name (default: "customer-support-chatbot")
+        endpoint_name (str): SageMaker endpoint name
     """
-    # Initialize SageMaker session
-    sagemaker_session = sagemaker.Session()
-    
-    # Define the image URI in ECR
-    ecr_image = f"{acc_id}.dkr.ecr.{region_name}.amazonaws.com/{ecr_repo_name}:latest"
+    try:
+        logger.info("Starting SageMaker deployment process...")
+        
+        # Initialize SageMaker session
+        sagemaker_session = sagemaker.Session()
+        
+        # Define the image URI in ECR
+        ecr_image = f"{acc_id}.dkr.ecr.{region_name}.amazonaws.com/{ecr_repo_name}:latest"
+        logger.info(f"Using ECR image: {ecr_image}")
+        
+        # Get model archive S3 URI
+        model_data = create_model_archive("models/customer_support_gpt")
+        
+        # Define model configuration
+        model_environment = {
+            "MODEL_PATH": "/opt/ml/model",
+            "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/code",
+            "SAGEMAKER_PROGRAM": "inference.py"
+        }
+        
+        # Create model
+        logger.info("Creating SageMaker model...")
+        model = Model(
+            image_uri=ecr_image,
+            model_data=model_data,
+            role=role_arn,
+            sagemaker_session=sagemaker_session,
+            env=model_environment,
+            enable_network_isolation=False
+        )
+        
+        # Define deployment configuration
+        deployment_config = {
+            "initial_instance_count": 1,
+            "instance_type": "ml.t3.large",
+            "endpoint_name": endpoint_name,
+            "update_endpoint": True if _endpoint_exists(sagemaker_session, endpoint_name) else False
+        }
+        
+        # Deploy model
+        logger.info(f"Deploying model to endpoint: {endpoint_name}")
+        logger.info(f"Deployment configuration: {deployment_config}")
+        
+        predictor = model.deploy(**deployment_config)
+        
+        logger.info(f"Successfully deployed to endpoint: {endpoint_name}")
+        return predictor
+        
+    except Exception as e:
+        logger.error(f"Deployment failed: {str(e)}")
+        raise
 
-    # Define model
-    model = Model(
-        image_uri=ecr_image,
-        role=role_arn,
-        sagemaker_session=sagemaker_session,
-        entry_point="serve",
-    )
+def _endpoint_exists(sagemaker_session, endpoint_name):
+    """Check if SageMaker endpoint already exists"""
+    client = sagemaker_session.boto_session.client('sagemaker')
+    try:
+        client.describe_endpoint(EndpointName=endpoint_name)
+        return True
+    except client.exceptions.ClientError:
+        return False
 
-    # Deploy model as a SageMaker endpoint
-    logger.info(f"Starting deployment of Gradio app to SageMaker endpoint {endpoint_name}...")
-    predictor = model.deploy(
-        initial_instance_count=1,
-        instance_type="ml.t3.large", #"ml.g4dn.xlarge",
-        endpoint_name=endpoint_name
-    )
-    logger.info(f"Gradio app deployed successfully to endpoint: {endpoint_name}")
-
-if __name__ == "__main__":
-    # Parse arguments from CLI
+def main():
     parser = argparse.ArgumentParser(description="Deploy Gradio app to SageMaker")
-    parser.add_argument("--account_id", type=str, required=True, help="AWS Account ID")
-    parser.add_argument("--region", type=str, required=True, help="AWS Region")
-    parser.add_argument("--role_arn", type=str, required=True, help="IAM Role ARN for SageMaker")
-    parser.add_argument("--ecr_repo_name", type=str, required=True, help="ECR Repository name")
-    parser.add_argument("--endpoint_name", type=str, default="customer-support-chatbot", help="SageMaker Endpoint Name")
+    parser.add_argument("--account_id", type=str, required=True,
+                      help="AWS Account ID")
+    parser.add_argument("--region", type=str, required=True,
+                      help="AWS Region")
+    parser.add_argument("--role_arn", type=str, required=True,
+                      help="IAM Role ARN for SageMaker")
+    parser.add_argument("--ecr_repo_name", type=str, required=True,
+                      help="ECR Repository name")
+    parser.add_argument("--endpoint_name", type=str,
+                      default="customer-support-chatbot",
+                      help="SageMaker Endpoint Name")
+    
     args = parser.parse_args()
+    
+    try:
+        logger.info("Starting deployment process...")
+        deploy_app(
+            args.account_id,
+            args.region,
+            args.role_arn,
+            args.ecr_repo_name,
+            args.endpoint_name
+        )
+        logger.info("Deployment completed successfully!")
+        
+    except Exception as e:
+        logger.error(f"Deployment failed: {str(e)}")
+        raise
 
-    # Deploy the Gradio app to SageMaker
-    deploy_app(args.account_id, args.region, args.role_arn, args.ecr_repo_name, args.endpoint_name)
+if __name__ == "__main__":
+    main()