Patent ID: 11954527
Assignee: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 10:
11. A machine learning system, comprising:
an input interface configured to receive an experiment request associated with a target dataset;
a machine learning model training executor configured to use resources with a used resource quantity of the machine learning system to execute at least one first experiment, wherein each of said at least one first experiment has a first minimum resource demand;
an experiment generator connected to the input interface, and configured to decide a second experiment according to the target dataset and decide a second minimum resource demand of the second experiment;
an experiment scheduler connected to the experiment generator and the machine learning model training executor, and configured to allocate resources with a quantity equal to the second minimum resource demand to the machine learning model training executor to execute the second experiment when a total resource quantity of the machine learning system meets a sum of the first minimum resource demand and the second minimum resource demand, and a difference between the total resource quantity and the used resource quantity meets the second minimum resource demand; and
a dynamic resource allocator connected to the machine learning model training executor, and configured to determine that the machine learning model training executor has one or more idle resources, and selectively allocating said one or more idle resources for at least one of said at least one first experiment and the second experiment,
wherein deciding the second minimum resource demand performed by the experiment generator comprises: according to execution efficiency data of the second experiment and a target execution performance, calculating the second minimum resource demand, wherein the execution efficiency data of the second experiment comprises execution time of a single trial corresponding to a maximum occupied resource quantity, and the target execution performance indicates a target quantity of trials of the second experiment completed in a preset period.