Nova Customization SDK

The Nova Customization SDK is a comprehensive Python SDK for customizing Amazon Nova models. The SDK provides a unified interface for training, evaluation, monitoring, deployment, and inference of Amazon Nova models across different platforms including SageMaker AI and Amazon Bedrock. Whether you're adapting models to domain-specific tasks or optimizing performance for your use case, this SDK provides everything you need in one unified interface.

Benefits

Requirements

The SDK requires at least Python 3.12.

Installation

To install this SDK, please follow below command.

pip install amzn-nova-customization-sdk

Supported Models and Techniques

The SDK supports the following models and techniques within the Amazon Nova family:

Getting Started

1. Prepare Your Data

Load your dataset from local files or S3, and let the SDK handle the transformation to the correct format for your chosen training method. Or, provide formatted data and get started immediately.

from amzn_nova_customization_sdk.dataset.dataset_loader import JSONLDatasetLoader
from amzn_nova_customization_sdk.model.model_enums import Model, TrainingMethod

loader = JSONLDatasetLoader(question="input", answer="output")
loader.load("s3://your-bucket/training-data.jsonl")
loader.transform(method=TrainingMethod.SFT_LORA, model=Model.NOVA_LITE)

2. Configure Your Infrastructure

Choose your compute resources—the SDK validates configurations and ensures optimal setup.

from amzn_nova_customization_sdk.manager.runtime_manager import SMTJRuntimeManager, SMHPRuntimeManager


# SageMaker Training Jobs
runtime = SMTJRuntimeManager(
    instance_type="ml.p5.48xlarge",
    instance_count=4
)

# SageMaker HyperPod
runtime = SMHPRuntimeManager(
    instance_type="ml.p5.48xlarge",
    instance_count=4,
    cluster_name="my-hyperpod-cluster",
    namespace="kubeflow"
)

3. Train

Start training with just a few lines of code.

from amzn_nova_customization_sdk.model import NovaModelCustomizer
from amzn_nova_customization_sdk.model.model_enums import Model, TrainingMethod

customizer = NovaModelCustomizer(
    model=Model.NOVA_LITE_2,
    method=TrainingMethod.SFT_LORA,
    infra=runtime,
    data_s3_path="s3://your-bucket/prepared-data.jsonl"
)

result = customizer.train(job_name="my-training-job")

4. Monitor

Track your training progress directly from the SDK.

from amzn_nova_customization_sdk.monitor.log_monitor import CloudWatchLogMonitor

# Monitor training logs
customizer.get_logs()

# Or monitor directly via CloudWatchLogMonitor
monitor = CloudWatchLogMonitor.from_job_result(result)
monitor.show_logs(limit=10)

# Check job status
result.get_job_status() # InProgress, Completed, Failed

5. Evaluate

Evaluate model performance with a variety of built-in benchmarks, or design your own evaluations.

from amzn_nova_customization_sdk.recipe_config.eval_config import EvaluationTask

# Evaluate on benchmark tasks
eval_result = customizer.evaluate(
    job_name="model-eval",
    eval_task=EvaluationTask.MMLU,
    model_path=result.model_artifacts.checkpoint_s3_path
)

6. Deploy

Deploy your customized model to production with built-in support for Amazon Bedrock or SageMaker.

from amzn_nova_customization_sdk.model.model_enums import DeployPlatform

# Bedrock provisioned throughput
deployment = customizer.deploy(
    model_artifact_path=result.model_artifacts.checkpoint_s3_path,
    deploy_platform=DeployPlatform.BEDROCK_PT,
    unit_count=10
)

# Bedrock On-Demand
deployment = customizer.deploy(
    model_artifact_path=result.model_artifacts.checkpoint_s3_path,
    deploy_platform=DeployPlatform.BEDROCK_OD
)

# Sagemaker Real-time Inference
deployment = customizer.deploy(
    model_artifact_path=result.model_artifacts.checkpoint_s3_path,
    deploy_platform=DeployPlatform.SAGEMAKER,
    unit_count=10,
    sagemaker_instance_type="ml.p5.48xlarge",
    sagemaker_environment_variables={
        "CONTEXT_LENGTH": "12000",
        "MAX_CONCURRENCY": "16",
    }
)

Key Capabilities

On The Fly Recipe Creation

The SDK eliminates the need to search for the appropriate recipes or container URI for specific techniques.

Intelligent Data Processing

The SDK automatically transforms your data into the correct format for training. Whether you're working with JSON, JSONL, or CSV files, the data loader handles the conversion seamlessly. Data Loader supports text as well as multimodal data (images and videos).

Enterprise Infrastructure Support

The SDK works with both SageMaker Training Jobs and SageMaker HyperPod, automatically managing:

Comprehensive evaluation

Evaluate your customized models against standard benchmarks including:

Either use the benchmark defaults, or modify them to fit your needs:

Production Deployment

Deploy your models to Amazon Bedrock or SageMaker with options for:

Batch Inference

Run large-scale inference jobs efficiently:

Nova Forge

For Nova Forge subscribers, the SDK supports data mixing recipes.

Learn More

Ready to start customizing Nova models with the Nova Customization SDK? Check out our GitHub repository for detailed guides, API references, and additional examples: https://github.com/aws/nova-customization-sdk