Subject archive for "tuning-hyperparameters"
Increasing model velocity for complex models by leveraging hybrid pipelines, parallelization and GPU acceleration
Data science is facing an overwhelming demand for CPU cycles as scientists try to work with datasets that are growing in complexity faster than Moore’s Law can keep up. Considering the need to iterate and retrain quickly, model complexity has been outpacing available compute resources and CPUs for several years, and the problem is growing quickly. The data science industry will need to embrace parallelization and GPU processing to efficiently utilize increasingly complex datasets.
By Nikolay Manchev10 min read
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