Method, electronic device, and computer program product for processing machine learning model

An electronic device, including:

• at least one processing unit;
• and
• at least one memory which is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit, wherein the instructions, when being executed by the at least one processing unit, cause the device to perform actions including:
• acquiring a computational graph, wherein nodes in the computational graph represent functions related to the machine learning model, and directed edges in the computational graph represent dependencies between the functions;
• determining multiple sequential portions of the computational graph wherein the multiple portions will be executed sequentially and functions corresponding to nodes in each portion can be executed in parallel;
• and
• assigning, to the multiple portions, execution instances for executing functions corresponding to nodes in the corresponding portions, wherein the number of execution instances assigned to each portion is associated with time required to execute functions corresponding to nodes in the portion.

A computer program product tangibly stored in a non-transitory computer-readable medium and including machine-executable instructions, wherein the machine-executable instructions, when being executed, cause a machine to perform steps of a method for processing a computational graph, including:

• acquiring a computational graph, wherein nodes in the computational graph represent functions related to the machine learning model, and directed edges in the computational graph represent dependencies between the functions;
• determining multiple sequential portions of the computational graph wherein the multiple portions will be executed sequentially and functions corresponding to nodes in each portion can be executed in parallel;
• and
• assigning, to the multiple portions, execution instances for executing functions corresponding to nodes in the corresponding portions, wherein the number of execution instances assigned to each portion is associated with time required to execute functions corresponding to nodes in the portion.