Getting Started

This guide shows you how to deploy customized Amazon Nova models on SageMaker real-time endpoints, configure inference parameters, and invoke your models for testing.

Prerequisites

The following are prerequisites to deploy Amazon Nova models on SageMaker inference:

Step 1: Configure AWS credentials

Configure your AWS credentials using one of the following methods:

Option 1: AWS CLI (Recommended)

aws configure

Enter your AWS access key, secret key, and default region when prompted.

Option 2: AWS credentials file

Create or edit ~/.aws/credentials:

[default]
aws_access_key_id = YOUR_ACCESS_KEY
aws_secret_access_key = YOUR_SECRET_KEY

Option 3: Environment variables

export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key

Initialize AWS clients

Create a Python script or notebook with the following code to initialize the AWS SDK and verify your credentials:

import boto3

# AWS Configuration - Update these for your environment
REGION = "us-east-1"  # Supported regions: us-east-1, us-west-2
AWS_ACCOUNT_ID = "YOUR_ACCOUNT_ID"  # Replace with your AWS account ID

# Initialize AWS clients using default credential chain
sagemaker = boto3.client('sagemaker', region_name=REGION)
sts = boto3.client('sts')

# Verify credentials
try:
    identity = sts.get_caller_identity()
    print(f"Successfully authenticated to AWS Account: {identity['Account']}")
    
    if identity['Account'] != AWS_ACCOUNT_ID:
        print(f"Warning: Connected to account {identity['Account']}, expected {AWS_ACCOUNT_ID}")

except Exception as e:
    print(f"Failed to authenticate: {e}")
    print("Please verify your credentials are configured correctly.")

If the authentication is successful, you should see output confirming your AWS account ID.

Step 2: Create a SageMaker execution role

A SageMaker execution role is an IAM role that grants SageMaker permissions to access AWS resources on your behalf, such as Amazon S3 buckets for model artifacts and CloudWatch for logging.

Creating the execution role

The following code creates an IAM role with the necessary permissions for deploying Amazon Nova customized models:

import json

# Create SageMaker Execution Role
role_name = f"SageMakerInference-ExecutionRole-{AWS_ACCOUNT_ID}"

trust_policy = {
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {"Service": "sagemaker.amazonaws.com"},
            "Action": "sts:AssumeRole"
        }
    ]
}

iam = boto3.client('iam', region_name=REGION)

# Create the role
role_response = iam.create_role(
    RoleName=role_name,
    AssumeRolePolicyDocument=json.dumps(trust_policy),
    Description='SageMaker execution role with S3 and SageMaker access'
)

# Attach required policies
iam.attach_role_policy(
    RoleName=role_name,
    PolicyArn='arn:aws:iam::aws:policy/AmazonSageMakerFullAccess'
)

iam.attach_role_policy(
    RoleName=role_name,
    PolicyArn='arn:aws:iam::aws:policy/AmazonS3FullAccess'
)

SAGEMAKER_EXECUTION_ROLE_ARN = role_response['Role']['Arn']
print(f"Created SageMaker execution role: {SAGEMAKER_EXECUTION_ROLE_ARN}")

Using an existing execution role (Optional)

If you already have a SageMaker execution role, you can use it instead:

# Replace with your existing role ARN
SAGEMAKER_EXECUTION_ROLE_ARN = "arn:aws:iam::YOUR_ACCOUNT_ID:role/YOUR_EXISTING_ROLE_NAME"

To find existing SageMaker roles in your account:

iam = boto3.client('iam', region_name=REGION)
response = iam.list_roles()
sagemaker_roles = [role for role in response['Roles'] if 'SageMaker' in role['RoleName']]
for role in sagemaker_roles:
    print(f"{role['RoleName']}: {role['Arn']}")

For more information about SageMaker execution roles, see SageMaker Roles.

Step 3: Configure model parameters

Configure the deployment parameters for your Amazon Nova model. These settings control model behavior, resource allocation, and inference characteristics. For a list of supported instance types and supported CONTEXT_LENGTH and MAX_CONCURRENCY values for each, see Supported models and instances. For a complete list of additional container features such as sampling defaults, speculative decoding, and quantization, see Inference Container Features.

Required parameters

Configure your deployment

# AWS Configuration
REGION = "us-east-1"  # Must match region from Step 1

# ECR Account mapping by region
ECR_ACCOUNT_MAP = {
    "us-east-1": "708977205387",
    "us-west-2": "176779409107"
}

# Container Image
IMAGE = f"{ECR_ACCOUNT_MAP[REGION]}.dkr.ecr.{REGION}.amazonaws.com/nova-inference-repo:SM-Inference-latest"
print(f"IMAGE = {IMAGE}")

# Required parameters
CONTEXT_LENGTH = "16000"       # Maximum total context length
MAX_CONCURRENCY = "2"          # Maximum concurrent sequences

# Build environment variables for the container
environment = {
    'CONTEXT_LENGTH': CONTEXT_LENGTH,
    'MAX_CONCURRENCY': MAX_CONCURRENCY,
    # Optional: add container feature environment variables here.
    # See "Inference Container Features" for the full list.
    # Examples:
    # 'DEFAULT_TEMPERATURE': '0.7',
    # 'DEFAULT_MAX_NEW_TOKENS': '512',
    # 'QUANTIZATION_DTYPE': 'fp8',
}

print("Environment configuration:")
for key, value in environment.items():
    print(f"  {key}: {value}")

Configure deployment-specific parameters

Now configure the specific parameters for your Amazon Nova model deployment, including model artifacts location and instance type selection.

Set deployment identifier

# Deployment identifier - use a descriptive name for your use case
JOB_NAME = "my-nova-deployment"

Specify model artifacts location

Provide the Amazon S3 URI where your trained Amazon Nova model artifacts are stored. This should be the output location from your model training or fine-tuning job.

# S3 location of your trained Nova model artifacts
# Replace with your model's S3 URI - must end with /
MODEL_S3_LOCATION = "s3://your-bucket-name/path/to/model/artifacts/"

Select model variant and instance type

# Configure model variant and instance type
TESTCASE = {
    "model": "lite2",              # Options: micro, lite, lite2
    "instance": "ml.p5.48xlarge"   # Refer to "Supported models and instances" section
}

# Generate resource names
INSTANCE_TYPE = TESTCASE["instance"]
MODEL_NAME = JOB_NAME + "-" + TESTCASE["model"] + "-" + INSTANCE_TYPE.replace(".", "-")
ENDPOINT_CONFIG_NAME = MODEL_NAME + "-Config"
ENDPOINT_NAME = MODEL_NAME + "-Endpoint"

print(f"Model Name: {MODEL_NAME}")
print(f"Endpoint Config: {ENDPOINT_CONFIG_NAME}")
print(f"Endpoint Name: {ENDPOINT_NAME}")

Naming conventions

The code automatically generates consistent names for AWS resources:

Step 4: Create SageMaker resources and deploy the endpoint

SageMaker offers two approaches for deploying models to real-time endpoints. Choose the approach that fits your use case:

Option A: Creating with inference components

With inference components, you first create an endpoint, then deploy your model as an inference component on that endpoint. This decouples the model from the endpoint infrastructure, giving you more flexibility.

Create the endpoint configuration

Create an endpoint configuration that defines the infrastructure without specifying a model. The instance type and count are managed at the endpoint level:

# Create Endpoint Configuration for inference components
INFERENCE_COMPONENT_NAME = MODEL_NAME + "-IC"

try:
    config_response = sagemaker.create_endpoint_config(
        EndpointConfigName=ENDPOINT_CONFIG_NAME,
        ProductionVariants=[
            {
                'VariantName': 'primary',
                'InstanceType': INSTANCE_TYPE,
                'InitialInstanceCount': 1,
                'RoutingConfig': {
                    'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'
                }
            }
        ],
        Tags=[
            {
                'Key': 'sagemaker:nova-inference-component',
                'Value': 'true'
            }
        ]
    )
    print("Endpoint configuration created successfully!")
    print(f"Config ARN: {config_response['EndpointConfigArn']}")

except sagemaker.exceptions.ClientError as e:
    print(f"Error creating endpoint configuration: {e}")

Create and deploy the endpoint

import time

try:
    endpoint_response = sagemaker.create_endpoint(
        EndpointName=ENDPOINT_NAME,
        EndpointConfigName=ENDPOINT_CONFIG_NAME
    )
    print("Endpoint creation initiated successfully!")
    print(f"Endpoint ARN: {endpoint_response['EndpointArn']}")
except Exception as e:
    print(f"Error creating endpoint: {e}")

# Wait for endpoint to be InService
print("Waiting for endpoint to be InService...")
print("This typically takes 5-10 minutes...\n")

while True:
    try:
        response = sagemaker.describe_endpoint(EndpointName=ENDPOINT_NAME)
        status = response['EndpointStatus']
        
        if status == 'Creating':
            print(f"⏳ Status: {status} - Provisioning infrastructure...")
        elif status == 'InService':
            print(f"✅ Status: {status}")
            print(f"\nEndpoint '{ENDPOINT_NAME}' is ready.")
            break
        elif status == 'Failed':
            print(f"❌ Status: {status}")
            print(f"Failure Reason: {response.get('FailureReason', 'Unknown')}")
            break
        else:
            print(f"Status: {status}")
    except Exception as e:
        print(f"Error checking endpoint status: {e}")
        break
    
    time.sleep(30)

Create the inference component

Once the endpoint is InService, deploy your Amazon Nova model as an inference component:

try:
    ic_response = sagemaker.create_inference_component(
        InferenceComponentName=INFERENCE_COMPONENT_NAME,
        EndpointName=ENDPOINT_NAME,
        VariantName='primary',
        Specification={
            'Container': {
                'Image': IMAGE,
                'ArtifactUrl': MODEL_S3_LOCATION,
                'Environment': environment
            },
            'ComputeResourceRequirements': {
                'NumberOfCpuCoresRequired': 15,
                'NumberOfAcceleratorDevicesRequired': 4,
                'MinMemoryRequiredInMb': 25000
            }
        },
        RuntimeConfig={
            'CopyCount': 1
        }
    )
    print("Inference component creation initiated!")
    print(f"Inference Component ARN: {ic_response['InferenceComponentArn']}")

except sagemaker.exceptions.ClientError as e:
    print(f"Error creating inference component: {e}")

Key parameters:

Monitor inference component deployment

# Wait for inference component to be InService
print("Waiting for inference component deployment...")
print("This typically takes 10-20 minutes as the model is loaded...\n")

while True:
    try:
        ic_desc = sagemaker.describe_inference_component(
            InferenceComponentName=INFERENCE_COMPONENT_NAME
        )
        ic_status = ic_desc['InferenceComponentStatus']
        
        if ic_status == 'Creating':
            print(f"⏳ Status: {ic_status} - Loading model artifacts...")
        elif ic_status == 'InService':
            print(f"✅ Status: {ic_status}")
            print(f"\nInference component '{INFERENCE_COMPONENT_NAME}' is ready!")
            break
        elif ic_status == 'Failed':
            print(f"❌ Status: {ic_status}")
            print(f"Failure Reason: {ic_desc.get('FailureReason', 'Unknown')}")
            break
        else:
            print(f"Status: {ic_status}")
    except Exception as e:
        print(f"Error checking inference component status: {e}")
        break
    
    time.sleep(30)

Option B: Creating with single model endpoints

With single model endpoints, you create a SageMaker model object, an endpoint configuration, and then deploy the endpoint. This approach packages the model directly into the endpoint configuration.

Create the SageMaker model

The following code creates a SageMaker model that references your Amazon Nova model artifacts:

try:
    model_response = sagemaker.create_model(
        ModelName=MODEL_NAME,
        PrimaryContainer={
            'Image': IMAGE,
            'ModelDataSource': {
                'S3DataSource': {
                    'S3Uri': MODEL_S3_LOCATION,
                    'S3DataType': 'S3Prefix',
                    'CompressionType': 'None'
                }
            },
            'Environment': environment
        },
        ExecutionRoleArn=SAGEMAKER_EXECUTION_ROLE_ARN,
        EnableNetworkIsolation=True
    )
    print("Model created successfully!")
    print(f"Model ARN: {model_response['ModelArn']}")
    
except sagemaker.exceptions.ClientError as e:
    print(f"Error creating model: {e}")

Key parameters:

Create the endpoint configuration

Next, create an endpoint configuration that defines your deployment infrastructure:

# Create Endpoint Configuration
try:
    production_variant = {
        'VariantName': 'primary',
        'ModelName': MODEL_NAME,
        'InitialInstanceCount': 1,
        'InstanceType': INSTANCE_TYPE,
    }
    
    config_response = sagemaker.create_endpoint_config(
        EndpointConfigName=ENDPOINT_CONFIG_NAME,
        ProductionVariants=[production_variant]
    )
    print("Endpoint configuration created successfully!")
    print(f"Config ARN: {config_response['EndpointConfigArn']}")
    
except sagemaker.exceptions.ClientError as e:
    print(f"Error creating endpoint configuration: {e}")

Key parameters:

Deploy the endpoint

import time

try:
    endpoint_response = sagemaker.create_endpoint(
        EndpointName=ENDPOINT_NAME,
        EndpointConfigName=ENDPOINT_CONFIG_NAME
    )
    print("Endpoint creation initiated successfully!")
    print(f"Endpoint ARN: {endpoint_response['EndpointArn']}")
except Exception as e:
    print(f"Error creating endpoint: {e}")

Monitor endpoint creation

The following code polls the endpoint status until deployment is complete:

# Monitor endpoint creation progress
print("Waiting for endpoint creation to complete...")
print("This typically takes 15-30 minutes...\n")

while True:
    try:
        response = sagemaker.describe_endpoint(EndpointName=ENDPOINT_NAME)
        status = response['EndpointStatus']
        
        if status == 'Creating':
            print(f"⏳ Status: {status} - Provisioning infrastructure and loading model...")
        elif status == 'InService':
            print(f"✅ Status: {status}")
            print("\nEndpoint creation completed successfully!")
            print(f"Endpoint Name: {ENDPOINT_NAME}")
            print(f"Endpoint ARN: {response['EndpointArn']}")
            break
        elif status == 'Failed':
            print(f"❌ Status: {status}")
            print(f"Failure Reason: {response.get('FailureReason', 'Unknown')}")
            print("\nFull response:")
            print(response)
            break
        else:
            print(f"Status: {status}")
        
    except Exception as e:
        print(f"Error checking endpoint status: {e}")
        break
    
    time.sleep(30)  # Check every 30 seconds

Verify resource creation

You can verify that your resources were created successfully:

# Describe the model
model_info = sagemaker.describe_model(ModelName=MODEL_NAME)
print(f"Model Status: {model_info['ModelName']} created")

# Describe the endpoint configuration
config_info = sagemaker.describe_endpoint_config(EndpointConfigName=ENDPOINT_CONFIG_NAME)
print(f"Endpoint Config Status: {config_info['EndpointConfigName']} created")

Verify endpoint is ready

Regardless of which approach you chose, you can verify the endpoint configuration:

# Get detailed endpoint information
endpoint_info = sagemaker.describe_endpoint(EndpointName=ENDPOINT_NAME)

print("\n=== Endpoint Details ===")
print(f"Endpoint Name: {endpoint_info['EndpointName']}")
print(f"Endpoint ARN: {endpoint_info['EndpointArn']}")
print(f"Status: {endpoint_info['EndpointStatus']}")
print(f"Creation Time: {endpoint_info['CreationTime']}")
print(f"Last Modified: {endpoint_info['LastModifiedTime']}")

# Get endpoint config for instance type details
endpoint_config_name = endpoint_info['EndpointConfigName']
endpoint_config = sagemaker.describe_endpoint_config(EndpointConfigName=endpoint_config_name)

# Display production variant details
for variant in endpoint_info['ProductionVariants']:
    print(f"\nProduction Variant: {variant['VariantName']}")
    print(f"  Current Instance Count: {variant['CurrentInstanceCount']}")
    print(f"  Desired Instance Count: {variant['DesiredInstanceCount']}")
    # Get instance type from endpoint config
    for config_variant in endpoint_config['ProductionVariants']:
        if config_variant['VariantName'] == variant['VariantName']:
            print(f"  Instance Type: {config_variant['InstanceType']}")
            break

Troubleshooting endpoint creation failures

Common failure reasons:

sagemaker.delete_endpoint(EndpointName=ENDPOINT_NAME)

Step 5: Invoke the endpoint

Once your endpoint is InService, you can send inference requests to generate predictions from your Amazon Nova model. SageMaker supports synchronous endpoints (real-time with streaming/non-streaming modes) and asynchronous endpoints (Amazon S3-based for batch processing).

Set up the runtime client

Create a SageMaker Runtime client with appropriate timeout settings:

import json
import boto3
import botocore
from botocore.exceptions import ClientError

# Configure client with appropriate timeouts
config = botocore.config.Config(
    read_timeout=120,      # Maximum time to wait for response
    connect_timeout=10,    # Maximum time to establish connection
    retries={'max_attempts': 3}  # Number of retry attempts
)

# Create SageMaker Runtime client
runtime_client = boto3.client('sagemaker-runtime', config=config, region_name=REGION)

Create a universal inference function

The following function handles both streaming and non-streaming requests. It uses the INFERENCE_COMPONENT_NAME variable defined in Step 4. If you deployed using inference components (Option A), this was set to MODEL_NAME + "-IC". If you deployed using single model endpoints (Option B), this was not defined, so set it to None before running this step:

# Only needed if you followed Option B (single model endpoints) in Step 4:
# INFERENCE_COMPONENT_NAME = None

def invoke_nova_endpoint(request_body):
    """
    Invoke Nova endpoint with automatic streaming detection.
    Supports both inference component and single model endpoint deployments.
    
    Args:
        request_body (dict): Request payload containing prompt and parameters
    
    Returns:
        dict: Response from the model (for non-streaming requests)
        None: For streaming requests (prints output directly)
    """
    body = json.dumps(request_body)
    is_streaming = request_body.get("stream", False)
    
    # Build invoke parameters
    invoke_params = {
        'EndpointName': ENDPOINT_NAME,
        'ContentType': 'application/json',
        'Body': body
    }
    
    # Add InferenceComponentName if using inference components
    if INFERENCE_COMPONENT_NAME:
        invoke_params['InferenceComponentName'] = INFERENCE_COMPONENT_NAME
    
    try:
        print(f"Invoking endpoint ({'streaming' if is_streaming else 'non-streaming'})...")
        
        if is_streaming:
            response = runtime_client.invoke_endpoint_with_response_stream(**invoke_params)
            
            event_stream = response['Body']
            for event in event_stream:
                if 'PayloadPart' in event:
                    chunk = event['PayloadPart']
                    if 'Bytes' in chunk:
                        data = chunk['Bytes'].decode()
                        print("Chunk:", data)
        else:
            # Non-streaming inference
            invoke_params['Accept'] = 'application/json'
            response = runtime_client.invoke_endpoint(**invoke_params)
            
            response_body = response['Body'].read().decode('utf-8')
            result = json.loads(response_body)
            print("✅ Response received successfully")
            return result
    
    except ClientError as e:
        error_code = e.response['Error']['Code']
        error_message = e.response['Error']['Message']
        print(f"❌ AWS Error: {error_code} - {error_message}")
    except Exception as e:
        print(f"❌ Unexpected error: {str(e)}")

Example 1: Non-streaming chat completion

Use the chat format for conversational interactions:

# Non-streaming chat request
chat_request = {
    "messages": [
        {"role": "user", "content": "Hello! How are you?"}
    ],
    "max_tokens": 100,
    "max_completion_tokens": 100,  # Alternative to max_tokens
    "stream": False,
    "temperature": 0.7,
    "top_p": 0.9,
    "top_k": 50,
    "logprobs": True,
    "top_logprobs": 3,
    "reasoning_effort": "low",  # Options: "low", "high"
    "allowed_token_ids": None,  # List of allowed token IDs
    "truncate_prompt_tokens": None,  # Truncate prompt to this many tokens
    "stream_options": None
}

response = invoke_nova_endpoint(chat_request)

Sample response:

{
    "id": "chatcmpl-123456",
    "object": "chat.completion",
    "created": 1234567890,
    "model": "default",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "Hello! I'm doing well, thank you for asking. I'm here and ready to help you with any questions or tasks you might have. How can I assist you today?"
            },
            "logprobs": {
                "content": [
                    {
                        "token": "Hello",
                        "logprob": -0.123,
                        "top_logprobs": [
                            {"token": "Hello", "logprob": -0.123},
                            {"token": "Hi", "logprob": -2.456},
                            {"token": "Hey", "logprob": -3.789}
                        ]
                    }
                    # Additional tokens...
                ]
            },
            "finish_reason": "stop"
        }
    ],
    "usage": {
        "prompt_tokens": 12,
        "completion_tokens": 28,
        "total_tokens": 40
    }
}

Example 2: Simple text completion

Use the completion format for simple text generation:

# Simple completion request
completion_request = {
    "prompt": "The capital of France is",
    "max_tokens": 50,
    "stream": False,
    "temperature": 0.0,
    "top_p": 1.0,
    "top_k": -1,  # -1 means no limit
    "logprobs": 3,  # Number of log probabilities to return
    "allowed_token_ids": None,  # List of allowed token IDs
    "truncate_prompt_tokens": None,  # Truncate prompt to this many tokens
    "stream_options": None
}

response = invoke_nova_endpoint(completion_request)

Sample response:

{
    "id": "cmpl-789012",
    "object": "text_completion",
    "created": 1234567890,
    "model": "default",
    "choices": [
        {
            "text": " Paris.",
            "index": 0,
            "logprobs": {
                "tokens": [" Paris", "."],
                "token_logprobs": [-0.001, -0.002],
                "top_logprobs": [
                    {" Paris": -0.001, " London": -5.234, " Rome": -6.789},
                    {".": -0.002, ",": -4.567, "!": -7.890}
                ]
            },
            "finish_reason": "stop"
        }
    ],
    "usage": {
        "prompt_tokens": 6,
        "completion_tokens": 2,
        "total_tokens": 8
    }
}

Example 3: Streaming chat completion

# Streaming chat request
streaming_request = {
    "messages": [
        {"role": "user", "content": "Tell me a short story about a robot"}
    ],
    "max_tokens": 200,
    "stream": True,
    "temperature": 0.7,
    "top_p": 0.95,
    "top_k": 40,
    "logprobs": True,
    "top_logprobs": 2,
    "reasoning_effort": "high",  # For more detailed reasoning
    "stream_options": {"include_usage": True}
}

invoke_nova_endpoint(streaming_request)

Sample streaming output:

Chunk: data: {"id":"chatcmpl-029ca032-fa01-4868-80b7-c4cb1af90fb9","object":"chat.completion.chunk","created":1772060532,"model":"default","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning_content":null},"logprobs":null,"finish_reason":null}],"prompt_token_ids":null}
Chunk: data: {"id":"chatcmpl-029ca032-fa01-4868-80b7-c4cb1af90fb9","object":"chat.completion.chunk","created":1772060532,"model":"default","choices":[{"index":0,"delta":{"content":" Once","reasoning_content":null},"logprobs":{"content":[{"token":"\u2581Once","logprob":-0.6078429222106934,"bytes":[226,150,129,79,110,99,101],"top_logprobs":[{"token":"\u2581Once","logprob":-0.6078429222106934,"bytes":[226,150,129,79,110,99,101]},{"token":"\u2581In","logprob":-0.7864127159118652,"bytes":[226,150,129,73,110]}]}]},"finish_reason":null,"token_ids":null}]}
Chunk: data: {"id":"chatcmpl-029ca032-fa01-4868-80b7-c4cb1af90fb9","object":"chat.completion.chunk","created":1772060532,"model":"default","choices":[{"index":0,"delta":{"content":" upon","reasoning_content":null},"logprobs":{"content":[{"token":"\u2581upon","logprob":-0.0012345,"bytes":[226,150,129,117,112,111,110],"top_logprobs":[{"token":"\u2581upon","logprob":-0.0012345,"bytes":[226,150,129,117,112,111,110]},{"token":"\u2581a","logprob":-6.789,"bytes":[226,150,129,97]}]}]},"finish_reason":null,"token_ids":null}]}
Chunk: data: {"id":"chatcmpl-029ca032-fa01-4868-80b7-c4cb1af90fb9","object":"chat.completion.chunk","created":1772060532,"model":"default","choices":[{"index":0,"delta":{"content":" a","reasoning_content":null},"logprobs":{"content":[{"token":"\u2581a","logprob":-0.0001234,"bytes":[226,150,129,97],"top_logprobs":[{"token":"\u2581a","logprob":-0.0001234,"bytes":[226,150,129,97]},{"token":"\u2581time","logprob":-9.123,"bytes":[226,150,129,116,105,109,101]}]}]},"finish_reason":null,"token_ids":null}]}
Chunk: data: {"id":"chatcmpl-029ca032-fa01-4868-80b7-c4cb1af90fb9","object":"chat.completion.chunk","created":1772060532,"model":"default","choices":[{"index":0,"delta":{"content":" time","reasoning_content":null},"logprobs":{"content":[{"token":"\u2581time","logprob":-0.0023456,"bytes":[226,150,129,116,105,109,101],"top_logprobs":[{"token":"\u2581time","logprob":-0.0023456,"bytes":[226,150,129,116,105,109,101]},{"token":",","logprob":-6.012,"bytes":[44]}]}]},"finish_reason":null,"token_ids":null}]}

# Additional chunks...

Chunk: data: {"id":"chatcmpl-029ca032-fa01-4868-80b7-c4cb1af90fb9","object":"chat.completion.chunk","created":1772060532,"model":"default","choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":15,"completion_tokens":87,"total_tokens":102}}
Chunk: data: [DONE]

Example 4: Multimodal chat completion

Use multimodal format for image and text inputs:

# Multimodal chat request (if supported by your model)
multimodal_request = {
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
            ]
        }
    ],
    "max_tokens": 150,
    "temperature": 0.3,
    "top_p": 0.8,
    "stream": False
}

response = invoke_nova_endpoint(multimodal_request)

Sample response:

{
    "id": "chatcmpl-345678",
    "object": "chat.completion",
    "created": 1234567890,
    "model": "default",
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "The image shows..."
            },
            "finish_reason": "stop"
        }
    ],
    "usage": {
        "prompt_tokens": 1250,
        "completion_tokens": 45,
        "total_tokens": 1295
    }
}

Step 6: Clean up resources (Optional)

To avoid incurring unnecessary charges, delete the AWS resources you created during this tutorial. SageMaker endpoints incur charges while they're running, even if you're not actively making inference requests.

Initialize the cleanup client

import boto3
import time

# Initialize SageMaker client
sagemaker = boto3.client('sagemaker', region_name=REGION)

Delete inference component (if using Option A)

If you deployed using inference components, delete the inference component first before deleting the endpoint:

# Delete inference component (Option A only)
try:
    print("Deleting inference component...")
    sagemaker.delete_inference_component(InferenceComponentName=INFERENCE_COMPONENT_NAME)
    print(f"✅ Inference component '{INFERENCE_COMPONENT_NAME}' deletion initiated")
except Exception as e:
    print(f"❌ Error deleting inference component: {e}")

# Wait for inference component to be deleted before proceeding
print("Waiting for inference component deletion...")
while True:
    try:
        sagemaker.describe_inference_component(InferenceComponentName=INFERENCE_COMPONENT_NAME)
        time.sleep(10)
    except sagemaker.exceptions.ClientError as e:
        if e.response['Error']['Code'] == 'ValidationException':
            print("✅ Inference component successfully deleted")
            break
        else:
            print(f"Error: {e}")
            break

Delete the endpoint

try:
    print("Deleting endpoint...")
    sagemaker.delete_endpoint(EndpointName=ENDPOINT_NAME)
    print(f"✅ Endpoint '{ENDPOINT_NAME}' deletion initiated")
    print("Charges will stop once deletion completes (typically 2-5 minutes)")
except Exception as e:
    print(f"❌ Error deleting endpoint: {e}")

import time

print("Monitoring endpoint deletion...")
while True:
    try:
        response = sagemaker.describe_endpoint(EndpointName=ENDPOINT_NAME)
        status = response['EndpointStatus']
        print(f"Status: {status}")
        time.sleep(10)
    except sagemaker.exceptions.ClientError as e:
        if e.response['Error']['Code'] == 'ValidationException':
            print("✅ Endpoint successfully deleted")
            break
        else:
            print(f"Error: {e}")
            break

Delete the endpoint configuration

After the endpoint is deleted, remove the endpoint configuration:

try:
    print("Deleting endpoint configuration...")
    sagemaker.delete_endpoint_config(EndpointConfigName=ENDPOINT_CONFIG_NAME)
    print(f"✅ Endpoint configuration '{ENDPOINT_CONFIG_NAME}' deleted")
except Exception as e:
    print(f"❌ Error deleting endpoint configuration: {e}")

Delete the model (Option B only)

If you used single model endpoints, remove the SageMaker model object:

try:
    print("Deleting model...")
    sagemaker.delete_model(ModelName=MODEL_NAME)
    print(f"✅ Model '{MODEL_NAME}' deleted")
except Exception as e:
    print(f"❌ Error deleting model: {e}")