HyperparameterTuner does not use spot instances

[toddstep]

PySDK Version
PySDK V3 (3.x)

Describe the bug
HyperparameterTuner.tune() does not use the enable_managed_spot_training flag that is provided under the model_trainer.compute object.
Rather, it launches the training jobs with EnableManagedSpotTraining set to False.

To reproduce
Two files:
training.py:

print("Test set: Average loss: 0.9")

demo.py (a simplified version of SageMaker V3 Hyperparameter Tuning Example):

# V3 Imports
from sagemaker.train import ModelTrainer
from sagemaker.train.configs import Compute, SourceCode, StoppingCondition
from sagemaker.train.tuner import HyperparameterTuner
from sagemaker.core.parameter import ContinuousParameter
from sagemaker.core.helper.session_helper import Session, get_execution_role

sagemaker_session = Session()
region = sagemaker_session.boto_region_name
role = get_execution_role()

# Configure source code
source_code = SourceCode(
    source_dir=".",
    entry_script="training.py"
)

# Configure compute resources
compute = Compute(
    enable_managed_spot_training=True
)

# Configure stopping condition
stopping_condition = StoppingCondition(
    max_runtime_in_seconds=3600,  # 1 hour
    max_wait_time_in_seconds=3600
)

# Get PyTorch training image
training_image = f"763104351884.dkr.ecr.{region}.amazonaws.com/pytorch-training:1.10.0-gpu-py38"

# Create ModelTrainer
model_trainer = ModelTrainer(
    training_image=training_image,
    source_code=source_code,
    compute=compute,
    stopping_condition=stopping_condition,
    role=role,
)

# Define hyperparameter ranges to tune
hyperparameter_ranges = {
    "lr": ContinuousParameter(0.001, 0.1),
}

# Define objective metric
objective_metric_name = "average test loss"

# Define metric definitions
metric_definitions = [
    {
        "Name": "average test loss",
        "Regex": "Test set: Average loss: ([0-9\\.]+)"
    }
]

# Create HyperparameterTuner
tuner = HyperparameterTuner(
    model_trainer=model_trainer,
    objective_metric_name=objective_metric_name,
    hyperparameter_ranges=hyperparameter_ranges,
    metric_definitions=metric_definitions,
)

tuner.tune(wait=True)

job = next(tuner.describe().get_all_training_jobs())
describe_job = sagemaker_session.sagemaker_client.describe_training_job(
    TrainingJobName=job.training_job_name
)
print("EnableManagedSpotTraining", describe_job['EnableManagedSpotTraining'])

Expected behavior
When running the provided script, the final line should be:

EnableManagedSpotTraining True

Screenshots or logs

$ python demo.py 
sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml
sagemaker.config INFO - Not applying SDK defaults from location: /home/sagemaker-user/.config/sagemaker/config.yaml
[02/26/26 16:47:04] INFO     SageMaker session not provided. Using default Session.                                            defaults.py:61
                    INFO     Base name not provided. Using default name:                                                       defaults.py:90
                             pytorch-training-job                                                                                            
                    INFO     Instance type not provided. Using default:                                                       defaults.py:105
                             ml.m5.xlarge                                                                                                    
                    INFO     Instance count not provided. Using default:                                                      defaults.py:108
                             1                                                                                                               
                    INFO     OutputDataConfig not provided. Using default:                                                    defaults.py:150
                             s3_output_path='s3://sagemaker-us-west-2-xxxxxxxxxxxx/pytorch-training-job' kms_key_id=None                     
                             compression_type='GZIP'                                                                                         
                             remove_job_name_from_s3_output_path=<sagemaker.core.utils.utils.Unassigned object at                            
                             0x7f298f0fa150> disable_model_upload=<sagemaker.core.utils.utils.Unassigned object at                           
                             0x7f298f0fa150> channels=<sagemaker.core.utils.utils.Unassigned object at 0x7f298f0fa150>                       
                    INFO     Training image URI:                                                                         model_trainer.py:548
                             763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:1.10.0-gpu-py38                                   
                    INFO     Creating hyper_parameter_tuning_job resource.                                                 resources.py:17214
                    INFO     Runs on sagemaker prod, region:us-west-2                                                            utils.py:354
[02/26/26 16:52:42] INFO     Final Resource Status: Completed                                                              resources.py:17443
EnableManagedSpotTraining False

System information
A description of your system. Please provide:

• SageMaker Python SDK version: 3.4.1
• Framework name (eg. PyTorch) or algorithm (eg. KMeans): PyTorch
• Framework version: 1.10.0
• Python version: 3.12.9
• CPU or GPU: CPU
• Custom Docker image (Y/N): N

Additional context
The following branch appears to fix this issue:

toddstep/sagemaker-python-sdk/tree/fix_hypertuner_managed_spot