
Dear readers, this summer has flown by, and now it is time to tie up any loose ends. As the countdown continues, my colleague and I are hard at work wrapping everything up.
When at first my supervisor told me that he wanted no job run times to be under-predicted, the task seemed daunting, almost impossible. How could I make sure that my algorithm never erred in such a way? In this post I explain how I tackled this problem and how the classical accuracy metric can be misleading and even futile in certain machine learning environments.
Project reference: 2015 The broad userbase of clusters is not familiar with the ins-and-outs of the systems they are working on and such familiarity is not really necessary. As a result many will enqueue jobs with maximum available run time …
Machine Learning for the rescheduling of SLURM jobs Read More »