# Continuous Customization (Multi-Round Fine-Tuning)

Adds a subsequent fine-tuning round on top of an already-customized model. Uses the Model Package ARN from a previous training job as the base model instead of a SageMakerHub model name.

---

## Prerequisites

| Requirement                                                                 | How to obtain                                                                        |
| --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
| Training data S3 path for current round                                     | Collect from the conversation context, ask user if not available                     |
| Confirmation that data is in correct format for current finetuning strategy | from dataset-evaluation skill or user is sure it's correct                           |
| Fine-tuning technique                                                       | If not available from context, ask which technique for this round: SFT, DPO, or RLVR |
| Previous model package group name                                           | From the prior notebook, or help the user find it (Cell A instructions)              |
| Previous training job name                                                  | Ask user or get from user's account (Cell B instructions)                            |

---

## Notebook Placement

- **If the user has an existing notebook from the previous round**: Append these cells under a new markdown header describing the round, e.g., `## DPO Fine-Tuning (Round 2)`.
- **If no prior notebook exists**: Create a new notebook with a name reflecting the use case and techniques, e.g., `news-app-sft-to-dpo.ipynb`.

---

## Cells

### Cell A: Setup & Credentials

Re-establishes session variables. Required to ensure all variables are defined.

#### Code

```python
import boto3
from sagemaker.ai_registry.dataset import DataSet
from sagemaker.core.resources import ModelPackageGroup
from sagemaker.core.helper.session_helper import Session, get_execution_role

# Setup
sm_client = boto3.Session().client("sagemaker")
sagemaker_session = Session(sagemaker_client=sm_client)
bucket = sagemaker_session.default_bucket()
S3_OUTPUT_PATH = f"s3://{bucket}/finetuning-output/"
ROLE_ARN = get_execution_role()

# Configuration
NEW_TRAINING_DATA_S3 = ""  # S3 path to the dataset for this round
PREVIOUS_MODEL_PACKAGE_GROUP_NAME = ""  # Model package group name from the previous round
```

#### Auto-Fill Instructions

1. Set `NEW_TRAINING_DATA_S3` to the user's dataset S3 path for the current round.
2. Set `PREVIOUS_MODEL_PACKAGE_GROUP_NAME`:
   - **If the prior notebook is available**: Copy the model package group name from it.
   - **If not**: use the AWS CLI MCP tool `list-model-package-groups` with these flags:

     ```
     --query 'ModelPackageGroupSummaryList[].{Name:ModelPackageGroupName,Status:ModelPackageGroupStatus,Created:CreationTime}'
     --output table
     ```

     Optionally add `--name-contains <keyword>` to filter by name.
   - **If you can't identify the name from the list**: ask the user.

---

### Cell B: Retrieve Previous Model Package ARN

Looks up the model package ARN from the previous training job.

#### Agent Instructions

1. Ask the user if they have the training job name from the previous fine-tuning round or need help finding it.
2. **If the user provides the name** → Set it as `previous_training_job_name` in the cell.
3. **If the user needs help** → Use the AWS CLI MCP tool `list-training-jobs` with these flags:

   ```
   --status-equals Completed
   --query 'TrainingJobSummaries[].{Name:TrainingJobName,Status:TrainingJobStatus,Created:CreationTime}'
   --output table
   ```

   Present the results and let the user pick the correct job.
4. **If the user is unsure and wants to fill it later** → Leave the placeholder `<previous_training_job_name>` and tell them to replace it before running.

#### Code

```python
from sagemaker.core.resources import TrainingJob

previous_training_job_name = "<previous_training_job_name>"  # USER: paste your previous training job name here
job = TrainingJob.get(training_job_name=previous_training_job_name)
previous_model_package_arn = job.output_model_package_arn
print(f"Previous Model Package ARN: {previous_model_package_arn}")
```

> **Troubleshooting**: If this cell throws `ValidationException: Requested resource not found`, the job name is wrong or the notebook is connected to a different AWS region than where the job ran. Verify the region with `boto3.Session().region_name`.

---

### Cell C: Register New Dataset

Registers the current round's training data as a versioned `DataSet`.

#### Agent Instructions

- Set `name` to something descriptive of the use case and round, e.g., `"customer-support-chatbot-dpo-round2"`.

#### Code

```python
from sagemaker.ai_registry.dataset import DataSet

dataset = DataSet.create(
    name="<dataset_name>",
    source=NEW_TRAINING_DATA_S3,
    wait=True
)
new_dataset_arn = dataset.arn
print(f"New Training Dataset ARN: {new_dataset_arn}")
```

---

### Cell D: Configure and Start Training

Runs the next fine-tuning round. The key difference from the first round: `model` receives `previous_model_package_arn` instead of a base model name.

#### Agent Instructions

Choose the trainer class matching the user's technique for this round:

| Technique | Import                                                 |
| --------- | ------------------------------------------------------ |
| SFT       | `from sagemaker.train.sft_trainer import SFTTrainer`   |
| DPO       | `from sagemaker.train.dpo_trainer import DPOTrainer`   |
| RLVR      | `from sagemaker.train.rlvr_trainer import RLVRTrainer` |

#### Code (SFT example — swap trainer class for DPO/RLVR)

```python
from sagemaker.train.sft_trainer import SFTTrainer
from sagemaker.train.common import TrainingType

step2_trainer = SFTTrainer(
    model=previous_model_package_arn,
    training_type=TrainingType.LORA,
    model_package_group=PREVIOUS_MODEL_PACKAGE_GROUP_NAME,
    training_dataset=new_dataset_arn,
    s3_output_path=S3_OUTPUT_PATH,
    sagemaker_session=sagemaker_session,
    role=ROLE_ARN,
)
step2_job = step2_trainer.train(wait=True)

print(f"Training Job Name for current round: {step2_job.training_job_name}")
print(f"Training Status: {step2_job.training_job_status}")
```

---

## Rules

- ✅ Reuse the same `PREVIOUS_MODEL_PACKAGE_GROUP_NAME` from the first round so all model versions stay grouped together
- ❌ Do NOT pass `accept_eula` — it only applies to the initial base model download
- ❌ Do NOT re-create the `ModelPackageGroup` — it already exists from the first round