What is the most effective way to handle delays in training jobs due to resource contention?

Implementing resource quotas is an effective strategy for managing delays in training jobs caused by resource contention. By setting resource quotas, you can ensure that different processes or users do not monopolize the available computational resources, such as GPUs or CPUs. This means that all jobs receive a fair allocation of resources, reducing the likelihood of delays for any single job due to heavy resource utilization by others.

When resource quotas are enforced, it stabilizes the environment by promoting fairness and predictability, which is particularly important in shared systems. It allows more users to operate simultaneously without causing bottlenecks, thus enhancing overall cluster performance and efficiency.

While increasing job priority might seem beneficial, it can lead to frustration among other users if their jobs are consistently deprioritized. Disabling job preemption might prevent some interruptions but could exacerbate delays when fewer high-priority jobs occupy resources. Adding more GPUs could alleviate resource contention to an extent, but it requires additional investment and may not always be feasible. Resource quotas, therefore, provide a structured way to manage and balance the available resources while ensuring fairness.