Large deep learning model training method and system, device and medium

A computer device, comprising:

• at least one processor;
• and
• a storage device, storing a computer instruction executable by the processor, and upon execution by the processor, the computer instruction is configured to cause the processor to perform operations comprising performing the following steps on each topological layer:
• arranging tensors in an ascending order according to series numbers of topological layers where the tensors are required;
• sequentially moving the tensors to a Graphics Processing Unit (GPU) according to the arrangement, and determining whether a sum of the tensors already moved to the GPU exceeds a threshold;
• in response to the sum of the tensors already moved to the GPU exceeds the threshold, moving the excess part to a Central Processing Unit (CPU), and determining whether the current topological layer is a last topological layer;
• and
• in response to the current topological layer is the last topological layer, correcting a tensor with a positional anomaly.

A computer-readable storage medium, storing a computer program, and upon execution by a processor, the computer program causes the processor to perform operations comprising performing the following steps on each topological layer:

• arranging tensors in an ascending order according to series numbers of topological layers where the tensors are required;
• sequentially moving the tensors to a Graphics Processing Unit (GPU) according to the arrangement, and determining whether a sum of the tensors already moved to the GPU exceeds a threshold;
• in response to the sum of the tensors already moved to the GPU exceeds the threshold, moving the excess part to a Central Processing Unit (CPU), and determining whether the current topological layer is a last topological layer;
• and
• in response to the current topological layer is the last topological laver, correcting a tensor with a positional anomaly.