**PySDK Version**
Describe the bug
Code is using method self._validate_and_load_hyperparameters_file instead of self._validate_and_fetch_hyperparameters_file`:
if is_nova:
if hyperparameters and isinstance(hyperparameters, str):
hyperparameters = cls._validate_and_load_hyperparameters_file(hyperparameters) # bug
model_trainer_args["hyperparameters"].update(hyperparameters)
elif hyperparameters and isinstance(hyperparameters, dict):
model_trainer_args["hyperparameters"].update(hyperparameters)
To reproduce
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.train.configs import Compute
model_trainer = ModelTrainer.from_recipe(
training_image="327873000638.dkr.ecr.us-east-1.amazonaws.com/hyperpod-recipes:verl-v1.0.0-smtj",
training_recipe="training/nova/nova_1_0/nova_lite/CPT/nova_lite_1_0_p5x16_gpu_pretrain",
compute=Compute(instance_type="ml.m5.xlarge"),
hyperparameters="test",
)
│ │
│ 287 │ │ │ private_attributes = self.__dict__.get('__private_attributes__') │
│ 288 │ │ │ if private_attributes and item in private_attributes: │
│ 289 │ │ │ │ return private_attributes[item] │
│ ❱ 290 │ │ │ raise AttributeError(item) │
│ 291 │ │
│ 292 │ @classmethod │
│ 293 │ def __prepare__(cls, *args: Any, **kwargs: Any) -> dict[str, object]: │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
AttributeError: _validate_and_load_hyperparameters_file
Expected behavior
No AttributeError to be raised when using hyperparameter file (string).
**PySDKVersion**Describe the bug
Code is using method
self._validate_and_load_hyperparameters_fileinstead of self._validate_and_fetch_hyperparameters_file`:To reproduce
Expected behavior
No
AttributeErrorto be raised when using hyperparameter file (string).