Topic 1 Question 202
A company's machine learning (ML) specialist is designing a scalable data storage solution for Amazon SageMaker. The company has an existing TensorFlow-based model that uses a train.py script. The model relies on static training data that is currently stored in TFRecord format.
What should the ML specialist do to provide the training data to SageMaker with the LEAST development overhead?
Put the TFRecord data into an Amazon S3 bucket. Use AWS Glue or AWS Lambda to reformat the data to protobuf format and store the data in a second S3 bucket. Point the SageMaker training invocation to the second S3 bucket.
Rewrite the train.py script to add a section that converts TFRecord data to protobuf format. Point the SageMaker training invocation to the local path of the data. Ingest the protobuf data instead of the TFRecord data.
Use SageMaker script mode, and use train.py unchanged. Point the SageMaker training invocation to the local path of the data without reformatting the training data.
Use SageMaker script mode, and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the SageMaker training invocation to the S3 bucket without reformatting the training data.
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コメント(3)
- 正解だと思う選択肢: D
Should be D. TFRecord could be uploaded to S3 directly and be used as SageMaker's data source. https://sagemaker-examples.readthedocs.io/en/latest/sagemaker_batch_transform/working_with_tfrecords/working-with-tfrecords.html#Upload-dataset-to-S3
👍 5VinceCar2022/11/28 - 正解だと思う選択肢: D
It has to option D.
👍 5Amit110119962022/11/28 - 正解だと思う選択肢: D
This option leverages SageMaker's built-in support for the TensorFlow framework and script mode. The existing train.py script can be used without any modifications. SageMaker will automatically download the training data from the specified S3 location to the instance running the training job.
This option saves development time by avoiding the need to rewrite the train.py script or reformat the training data.
👍 3AjoseO2023/02/19
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