During a hyperparameter search on an NVIDIA GPU cluster, which two practices help maximize efficiency?

Using NVIDIA's automatic mixed precision (AMP) during hyperparameter tuning is a highly efficient practice because it enables faster training times while maintaining model performance. AMP allows the use of lower precision (such as FP16 instead of FP32) for computations. This reduces memory requirements and increases throughput on NVIDIA GPUs, allowing more training runs to be conducted in the same amount of time, which is especially advantageous during hyperparameter searches where many model evaluations are performed.

The other practices would either hinder efficiency or not contribute to the search in useful ways. Performing the search sequentially on a single GPU is inherently less efficient because GPUs can be utilized in parallel, and searching across multiple configurations simultaneously can significantly speed up the overall process. Relying solely on default hyperparameters limits the exploration of the hyperparameter space, potentially missing out on configurations that could lead to better model performance. Lastly, prioritizing hyperparameter tuning on CPU nodes would be inefficient because it underutilizes the GPU resources, which are typically more powerful for model training compared to CPUs and can process data at a much faster rate. Thus, leveraging automatic mixed precision is the optimal choice for maximizing efficiency during hyperparameter searches on an NVIDIA GPU cluster.