Training task optimization system, training task optimization method and non-transitory computer readable medium for operating the same

A training task optimization method, executed by a processor, comprising:

• receiving training environment information of a training task, wherein the training environment information carries information corresponding training samples in the training task, wherein a neural network comprises a plurality of layers, each of the layers corresponds to a plurality of algorithm candidates;
• calculating a first required space that represents spaces allocated to the training samples and output data corresponding to each of the plurality of layers;
• obtaining model parameters from the neural network and calculating a second required space that represents spaces allocated to the model parameters and gradients;
• calculating an available memory space by deducting the first required space and the second required space from memory factors;
• calculating a third required space that represents buffers prepared for algorithms in the layers, and the third required space is smaller than the available memory space, thereby calculating a memory distribution for the training task;
• determining a mini-batch size that is fit to the memory distribution, wherein the mini-batch size is an amount of the training samples being fetched in one mini-batch;
• and
• in response to the training environment information, outputting the mini-batch size for execution of the training task, wherein the training task is executed according to the mini-batch size so as to optimize training efficiency.

A non-transitory computer readable medium for training task optimization, and the non-transitory computer readable medium containing program instructions for causing a processor to perform a method of:

• receiving training environment information of a training task, wherein the training environment information carries information corresponding to training samples in the training task, wherein a neural network comprises a plurality of layers, each of the layers corresponds to a plurality of algorithm candidates;
• calculating a first required space that represents spaces allocated to the training samples and output data corresponding to each of the plurality of layers;
• obtaining model parameters from the neural network and calculating a second required space that represents spaces allocated to the model parameters and gradients;
• calculating an available memory space by deducting the first required space and the second required space from memory factors;
• calculating a third required space that represents buffers prepared for algorithms in the layers, and the third required space is smaller than the available memory space, thereby calculating a memory distribution for the training task;
• determining a mini-batch sizes that is fit to the memory distribution, wherein the mini-batch size is an amount of the training samples being fetched in one mini-batch;
• and
• in response to the training environment information, outputting the mini-batch size for execution of the training task, wherein the training task is executed according to the mini-batch size so as to optimize training efficiency.