Method, device and storage medium for training a deep learning framework

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected with the at least one processor;
• wherein,
• instructions executable by the at least one processor are stored in the memory, and the instructions are executed by the at least one processor, so that the at least one processor is configured to:
• acquire at least one task node in a current task node cluster, that meets a preset opening condition when a target task meets a training start condition;
• judge whether a number of nodes of the at least one task node is greater than a preset number;
• synchronously train the deep learning framework of the target task by the at least one task node according to sample data when the number of nodes is greater than the preset number;
• and
• acquire a synchronously trained target deep learning framework when the target task meets a training completion condition.

A non-transitory computer-readable storage medium, in which computer instructions are stored, wherein the computer instructions are configured to cause a computer execute a method for training a deep learning framework, the method comprising:

• acquiring at least one task node in a current task node cluster, that meets a preset opening condition when a target task meets a training start condition;
• judging whether a number of nodes of the at least one task node is greater than a preset number;
• synchronously training the deep learning framework of the target task by the at least one task node according to sample data when the number of nodes is greater than the preset number;
• and
• acquiring a synchronously trained target deep learning framework when the target task meets a training completion condition.