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Update docstrings of logs_for_job and train methods #157

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6 changes: 3 additions & 3 deletions src/sagemaker/session.py
Original file line number Diff line number Diff line change
Expand Up @@ -211,8 +211,8 @@ def train(self, image, input_mode, input_config, role, job_name, output_config,
job_name (str): Name of the training job being created.
output_config (dict): The S3 URI where you want to store the training results and optional KMS key ID.
resource_config (dict): Contains values for ResourceConfig:
instance_count (int): Number of EC2 instances to use for training.
instance_type (str): Type of EC2 instance to use for training, for example, 'ml.c4.xlarge'.
instance_count (int): Number of EC2 instances to use for training.
instance_type (str): Type of EC2 instance to use for training, for example, 'ml.c4.xlarge'.
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Can you put these in a bulleted list?

hyperparameters (dict): Hyperparameters for model training. The hyperparameters are made accessible as
a dict[str, str] to the training code on SageMaker. For convenience, this accepts other types for
keys and values, but ``str()`` will be called to convert them before training.
Expand Down Expand Up @@ -556,7 +556,7 @@ def logs_for_job(self, job_name, wait=False, poll=5): # noqa: C901 - suppress c

Args:
job_name (str): Name of the training job to display the logs for.
wait (bool): Whether to keep looking for new log entries until the job completes (default: True).
wait (bool): Whether to keep looking for new log entries until the job completes (default: False).
poll (int): The interval in seconds between polling for new log entries and job completion (default: 5).

Raises:
Expand Down