Note

This site contains only the example notebooks built on SageMaker Python SDK v3, which is what pip install sagemaker installs. Notebooks built on the deprecated SDK 2.x are archived in the v2 example notebooks documentation.

Nova Data Mixing

Data mixing blends your custom training data with Nova’s curated synthetic datasets (code, math, chat, planning, instruction-following, reasoning, etc.) to prevent catastrophic forgetting while specializing the model on your domain.

Important: Data mixing is only supported with serverless compute type. It is not available for serverful training jobs (SMTJ) or HyperPod clusters.

What you will learn

Configure DataMixingConfig with customer and Nova data percentages
Create an SFTTrainer with data mixing enabled
Set hyperparameters and submit a training job
Monitor job status

1. Setup

[ ]:

import json
import boto3

[ ]:

# === Fill in your AWS resources ===
REGION = "<your-region>"  # e.g. "us-east-1"
ROLE_ARN = "<your-execution-role-arn>"
S3_BUCKET = "<your-s3-bucket>"  # e.g. "sagemaker-us-east-1-123456789012"

S3_OUTPUT_PATH = f"s3://{S3_BUCKET}/sft-data-mixing/output"
TRAINING_DATASET = f"s3://{S3_BUCKET}/datasets/sft_training_data.jsonl"

2. Configure Data Mixing

Data mixing controls the blend between your custom training data and Nova’s internal curated datasets. customer_data_percent sets how much of the training data comes from your dataset. The remaining portion is distributed among Nova categories according to nova_data_percentages.

Available Nova categories include: code, math, chat, planning, instruction-following, reasoning, stem, rag, factuality, etc.

[ ]:

from sagemaker.train.data_mixing_config import DataMixingConfig

# 70% of training data from your dataset, 30% from Nova curated data
# Within Nova data: 30% code, 70% math
data_mixing_config = DataMixingConfig(
    customer_data_percent=70.0,
    nova_data_percentages={
        "code": 30.0,
        "math": 70.0,
    },
)

3. Create SFTTrainer with Data Mixing

Pass the DataMixingConfig to SFTTrainer. Since data mixing only works with serverless compute, no compute parameter is needed.

[ ]:

from sagemaker.train import SFTTrainer
from sagemaker.train.common import TrainingType

sft_trainer = SFTTrainer(
    model="amazon.nova-2-lite-v1",
    training_type=TrainingType.LORA,
    training_dataset=TRAINING_DATASET,
    s3_output_path=S3_OUTPUT_PATH,
    role=ROLE_ARN,
    data_mixing_config=data_mixing_config,
    base_job_name="sft-datamix",
)

4. Set Hyperparameters and Submit Training Job

[ ]:

# Set hyperparameters
sft_trainer.hyperparameters.max_steps = 50
sft_trainer.hyperparameters.learning_rate = 5e-6
sft_trainer.hyperparameters.global_batch_size = 32

# Submit (non-blocking)
training_job = sft_trainer.train(wait=False)
print(f"Training job submitted: {training_job}")

5. Monitor Training Job

[ ]:

from sagemaker.core.resources import TrainingJob

job = TrainingJob.get(training_job_name=training_job.training_job_name)
print(f"Status: {job.training_job_status}")
print(f"Secondary Status: {job.secondary_status}")

6. Alternative: Different Data Mix Configurations

Here are some common configuration patterns depending on your use case.

[ ]:

# High specialization: mostly your data
high_specialization = DataMixingConfig(
    customer_data_percent=90.0,
    nova_data_percentages={
        "reasoning": 100.0,
    },
)

# Balanced: equal split with multiple Nova categories
balanced_mix = DataMixingConfig(
    customer_data_percent=50.0,
    nova_data_percentages={
        "code": 40.0,
        "reasoning": 30.0,
        "math": 30.0,
    },
)

# Light specialization: preserve broad capabilities
light_specialization = DataMixingConfig(
    customer_data_percent=30.0,
    nova_data_percentages={
        "code": 25.0,
        "math": 25.0,
        "chat": 25.0,
        "reasoning": 25.0,
    },
)

Tips

High customer_data_percent (80–90%) — Use when your task is well-defined and you have enough data.
Balanced (50–70%) — Good default for most use cases.
Low customer_data_percent (20–40%) — Preserve base model capabilities with light specialization.
Nova category selection — Choose categories that complement your task (e.g., code + reasoning for a coding assistant).