Azure Data Scientists Associate Practice Exam

Setting arguments for hyperparameters directly in the script allows for greater flexibility and control over the training process. By incorporating these arguments, the data scientist can easily manage and adjust the hyperparameters without the need for modifying the code itself or creating multiple versions of the script. This approach makes it simple to run different experiments with varying hyperparameters while maintaining consistent reproducibility across different runs.

In practice, this means that the data scientist can create a single script that accepts parameters for learning rates, batch sizes, and other hyperparameters as command-line arguments. This way, they can submit jobs through different configurations quickly and efficiently, using the same codebase.

Using multiple script files for each combination would lead to increased complexity, making it harder to track changes, maintain the code, and manage experiments. Setting hyperparameters before submitting the job can be useful, but it typically requires an external interface or job scheduler that supports this, which may not always be practical. Relying on default values in the script may not yield the best results, as the data scientist might miss the opportunity to optimize the model's performance based on varying data or training scenarios. Therefore, adding arguments directly in the script provides the most streamlined and effective approach for managing hyperparameters in PyTorch model training.