Distributed hyperparameter tuning and load balancing for mathematical models

An apparatus for data processing, comprising:

• a processor,
• memory coupled with the processor;
• and
• instructions stored in the memory and executable by the processor to cause the apparatus to:
• generate a first plurality of combinations of hyperparameter values associated with training a mathematical model;
• identify a subset of combinations from the first plurality of combinations that are associated with a computational runtime that exceeds a first threshold;
• distribute the subset of combinations across a plurality of machines such that each machine of the plurality of machines is assigned a number of the subset of combinations that is less than a second threshold;
• test each of the first plurality of combinations against the mathematical model using the plurality of machines in a parallel processing operation to generate a first plurality of validation error values;
• and
• test a second plurality of combinations of hyperparameter values against the mathematical model using an objective function that is based at least in part on the first plurality of validation error values.

A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by a processor to:

• generate a first plurality of combinations of hyperparameter values associated with training a mathematical model;
• identify a subset of combinations from the first plurality of combinations that are associated with a computational runtime that exceeds a first threshold;
• distribute the subset of combinations across a plurality of machines such that each machine of the plurality of machines is assigned a number of the subset of combinations that is less than a second threshold;
• test each of the first plurality of combinations against the mathematical model using the plurality of machines in a parallel processing operation to generate a first plurality of validation error values;
• and
• test a second plurality of combinations of hyperparameter values against the mathematical model using an objective function that is based at least in part on the first plurality of validation error values.