The core issue is the I/O bottleneck caused by SageMaker's default File mode, which downloads the entire dataset from S3 to the training instance's local storage before training begins. This leads to significant startup delays, especially with large datasets. Pipe mode resolves this by streaming data directly from S3 to the training algorithm as it runs. This eliminates the initial download wait time, allowing training to start almost immediately and significantly improving data throughput. Since the constraint is to not modify the model scripts, changing the data input configuration from File to Pipe mode is the most efficient and appropriate action.

A. Increasing the instance size might improve network bandwidth but doesn't change the inefficient "download-then-train" mechanism of File mode. The primary bottleneck is the data access pattern, not the compute or network hardware itself.

B. Using Amazon Rekognition Custom Labels is a completely different, higher-level AI service. This would require abandoning the existing CNN model and scripts, directly violating the problem's constraints.

C. FastFile mode is an improvement over standard File mode as it uses multipart downloads to speed up the initial data transfer. However, it still downloads the entire dataset before training, making it less efficient than Pipe mode's streaming approach for I/O-bound jobs.

1. AWS SageMaker Developer Guide - Access Training Data: This document explicitly compares the different input modes. It states, "In Pipe mode, the data is streamed directly from Amazon S3 to your training algorithm without saving the data to the EBS volume of the training instance. This shortens the startup time for training jobs and offers better read throughput than File mode."

Source: AWS SageMaker Developer Guide, Section: "Access Training Data" -> "Select a data source" -> "Amazon S3".

2. AWS SageMaker API Reference - CreateTrainingJob: The documentation for the CreateTrainingJob operation shows that InputMode is a configurable parameter within the Channel specification. This confirms that switching from 'File' to 'Pipe' is a configuration change, not a code modification.

Source: AWS SageMaker API Reference, CreateTrainingJob -> InputDataConfig -> Channel -> InputMode.

3. AWS Machine Learning Blog - "Using Pipe input mode for Amazon SageMaker algorithms": This blog post details the performance benefits of Pipe mode. It highlights, "With pipe mode, your training job streams data directly from Amazon S3. Because the data is streamed, your training job starts sooner, finishes faster, and requires less disk space."

Source: AWS Machine Learning Blog, "Using Pipe input mode for Amazon SageMaker algorithms," published November 20, 2017.