ADAPTATION OF TASK PERFORMABLE BY PRE-TRAINED MODEL INTO PARALLEL HARDWARE

The computer-assisted parallelization of a task capable of being accomplished by a pre-trained machine learning model. Multiple learner models are created by, for each of at least some of the parallel compute resources, selecting one or more characteristics of a learner model based on one or more characteristics of the corresponding compute resource on which the learner model is to run. The learner models are then taught. The teaching occurs such that the learner model is capable of generating a task result when given the task. At task time, the tasks results are then aggregated to generate an aggregated task result. The learner models are thus collectively tailored to run efficiently on the corresponding hardware.