Adaptive configuration of a heterogeneous cluster environment

A method for use in managing a system comprising one or more computers, each computer comprising at least one hardware processor coupled to at least one memory, the method comprising a computer-implemented manager:

• generating a plurality of potential configurations for hardware resources of the system for each of a plurality of machine learning models;
• determining, for each of the plurality of machine learning models, the potential configuration of the plurality of potential configurations that exhibits a minimal execution time while satisfying accuracy and time constraints for the corresponding machine learning model, wherein the determining step comprises configuring the system according to a selected potential configuration, training the corresponding machine learning model on the configured system, and recording time required for training and accuracy reached;
• sorting, based on execution time to train the corresponding machine learning model of the plurality of machine learning models, the plurality of machine learning models and corresponding plurality of potential configurations that exhibit the minimal execution time;
• and
• configuring at least a subset of the hardware resources based on an optimal machine learning model of the plurality of sorted machine learning models.