Memory-based data selection scheme for machine learning training on limited memory resources

A system for training of a machine learning model, said system comprising a general processing unit and a special processing unit, wherein an amount of general processing unit memory directly accessible by said general processing unit is larger than an amount of special processing unit memory directly accessible by said special processing unit, said system comprising:

• a loading unit adapted for loading a complete training data set into said general processing unit memory;
• an importance determination unit adapted for determining importance values relating to training data vectors in said provided training data set of said training data vectors;
• a transfer unit adapted for dynamically transferring training data vectors of said training data set from said general processing unit memory to a special processing unit memory using as decision criteria said importance value of said training data vector, wherein said importance value used is taken from an earlier training round of said machine learning model;
• and
• wherein said special processing unit is adapted for executing a training algorithm on said special processing unit with said training data vectors having said highest available importance values of one of said earlier training round.

A computer program product for training of a machine learning model in a mixed environment comprising a general processing unit and a special processing unit, wherein an amount of general processing unit memory, directly accessible by said general processing unit, is larger than an amount of special processing unit memory directly accessible by said special processing unit, said computer program product comprising a computer readable storage medium having program instructions embodied therewith, said program instructions being executable by one or more computing systems to cause said one or more computing systems:

• to load a complete training data set into said general processing unit memory;
• to determine importance values relating to training data vectors in said provided training data set of said training data vectors;
• to transfer dynamically training data vectors of said training data set from said general processing unit memory to a special processing unit memory using as decision criteria said importance value of said training data vector, wherein said importance value used is taken from an earlier training round of said machine learning model;
• and
• to execute a training algorithm on said special processing unit with said training data vectors having highest available importance values of one of the earlier training rounds.