Which technique is NOT recommended for maximizing the efficiency of hyperparameter tuning on GPU clusters?

The technique that is not recommended for maximizing the efficiency of hyperparameter tuning on GPU clusters is performing the search sequentially on a single GPU. This approach is generally inefficient in the context of GPU clusters because it does not take full advantage of the parallel processing power offered by multiple GPUs. In hyperparameter tuning, the goal is to explore a wide range of hyperparameter configurations in order to find the optimal settings that yield the best performance. By performing the search on a single GPU, you limit the ability to conduct multiple trials simultaneously, dramatically increasing the time required to complete the hyperparameter tuning process.

Utilizing distributed hyperparameter tuning across multiple GPUs allows for multiple trials to run concurrently, thus speeding up the overall process significantly. Leveraging automatic mixed precision can also enhance the efficiency of training by allowing calculations to use lower precision without sacrificing model accuracy. Furthermore, using default hyperparameters as baselines is a common practice in the tuning process, as it provides a starting point to evaluate the performance of fine-tuned configurations. Consequently, the single GPU sequential approach is less efficient compared to alternative methods that capitalize on the multi-GPU capabilities inherent in modern clustering systems.