What should the Specialist do to address the performance issues with the current solution?

A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model

What should the Specialist do to address the performance issues with the current solution?
A . Use the SageMaker batch transform feature
B . Compress the training data into Apache Parquet format.
C . Ensure that the input mode for the training job is set to Pipe.
D . Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

View Answer

Answer: C

Explanation:

The input mode for the training job determines how the training data is transferred from Amazon S3 to the SageMaker instance. There are two input modes: File and Pipe. File mode copies the entire training dataset from S3 to the local file system of the instance before starting the training job. This can cause a long delay before the training job launches, especially if the dataset is large. Pipe mode streams the data from S3 to the instance as the training job runs. This can reduce the startup time and improve the I/O throughput, as the data is read in smaller batches. Therefore, to address the performance issues with the current solution, the Specialist should ensure that the input mode for the training job is set to Pipe. This can be done by using the SageMaker Python SDK and setting the input_mode parameter to Pipe when creating the estimator or the fit method12. Alternatively, this can be done by using the AWS CLI and setting the InputMode parameter to Pipe when creating the training job3.

Reference: Access Training Data – Amazon SageMaker

Choosing Data Input Mode Using the SageMaker Python SDK – Amazon SageMaker CreateTrainingJob – Amazon SageMaker Service