[D] how to parallelize optimal parameter search for DL NNs on multiple datasets?

A detailed technical question has gone viral on Reddit's Machine Learning community (r/MachineLearning), highlighting a common but complex hurdle in AI research: efficiently conducting exhaustive hyperparameter optimization across multiple models and datasets with constrained hardware. The poster, u/Mampacuk, outlines a scenario involving 5 distinct deep learning architectures (like CNNs or Transformers) and 11 datasets. Each model has its own set of 0 to 4 'free non-DL parameters' (e.g., learning rates, batch sizes) with 5-6 candidate values each, leading to a combinatorial explosion of configurations that need training and evaluation. The goal is to find the optimal parameters for each model-dataset pair.

The central technical bottleneck is the single GPU. Unlike CPU cores which can be logically partitioned, a GPU's memory and processing units are a shared resource, making true parallel execution of independent training jobs difficult. The poster notes each job has two phases—learning and prediction—with a model checkpoint passed between them, requiring careful naming to avoid overwrites. The community discussion is focused on practical solutions, such as using job schedulers to queue experiments, optimizing GPU memory usage to allow faster context switching between jobs, and leveraging libraries like Ray Tune or Optuna designed for distributed hyperparameter search. A key secondary question is whether to also sweep intrinsic DL parameters like the number of training epochs, which would further multiply the search space.