In a prediction apparatus for a learning system, an obtaining unit obtains, as input variables, at least one parameter indicative of a structure of a convolutional neural network, the number of nodes of a learning system, and a sub-batch number indicative of the number of pieces of training data collectively processed by at least one graphic processing unit. A predictor predicts at least one of learning time and an average mini-batch size as a function of the input variables obtained by the obtainer. The learning time is time required for one update of all the weights by a central processing unit. The average mini-batch size is an average number of pieces of training data used for the one update of all the weights.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A prediction apparatus for a learning system that includes a plurality of nodes each including a central processing unit and at least one graphics processing unit, the central processing unit of each node using the at least one graphics processing unit to calculate, based on a plurality of pieces of training data, a quantity of update of each weight included in a convolutional neural network, the central processing unit of each node performing a weight updating cycle that communicates the quantity of update of each weight with at least one other central processing unit of at least one other node to perform update of the corresponding weight of the convolutional neural network, the prediction apparatus comprising: an obtaining unit configured to obtain, as input variables, at least one parameter indicative of a structure of the convolutional neural network, the number of the nodes of the learning system; and a sub-batch number indicative of the number of pieces of training data collectively processed by the at least one graphic processing unit; and a predictor configured to predict at least one of learning time and an average mini-batch size as a function of the input variables obtained by the obtainer, the learning time being time required for one update of all the weights by the central processing unit, the average mini-batch size being an average number of pieces of training data used for the one update of all the weights. 2. The prediction apparatus according to claim 1, wherein the predictor is configured to predict the learning time in accordance with the following first equation: TEpoch=(NFile×TGPU)/(NNode×NGPU×NSubbatch) Where TEpoch represents the learning time; NNode represents the number of the nodes of the learning system; NSubbatch represents the sub-batch number; NFile represents the total number of the plurality of pieces of training data; NGPU represents the number of the at least one graphics processing unit of each node; and TGPU represents time required for the at least one graphics processing unit to calculate a quantity of the one update of all the weights. 3. The prediction apparatus according to claim 1, wherein the predictor is configured to predict the average mini-batch size in accordance with the following second equation: NBatch=(NNode×NGPU×NSubbatch×TAlleduce)/TGPU Where NBatch represents the average mini-batch size; NNode represents the number of the nodes of the learning system; NSubbatch represents the sub-batch number; NGPU represents the number of the at least one graphics processing unit of each node; TGPU represents time required for the at least one graphics processing unit to calculate a quantity of the one update of all the weights; and TAllreduce represents time required for the central processing unit of each node to perform the weight updating cycle. 4. The prediction apparatus according to claim 3, wherein the central processing unit of each node carries out a plurality of processes to perform the weight updating cycle, and the time TAllreduce is the sum of times required for the central processing unit of each node to carry out the respective processes. 5. The prediction apparatus according to claim 2, wherein the central processing unit of each node carries out a plurality of processes to perform the quantity of update of each weight, and the time TGPU is the sum of times required for the central processing unit of each node to carry out the respective processes. 6. The prediction apparatus according to claim 4, wherein each of the times required for the central processing unit of each node to carry out the respective processes is given as a linear function of the sub-batch number. 7. The prediction apparatus according to claim 6, further comprising: a parameter calculator configured to: measure first time required for the CPU of each node to perform each of the processes when a first value is used for the sub-batch number; measure second time required for the CPU of each node to perform each of the processes when a second value is used for the sub-batch number, the second value being different from the first value; and perform, for each of the processes, linear regression analysis based on a first pair of the first value of the sub-batch number and the corresponding first time, and a second pair of the second value of the sub-batch number and the corresponding second time to calculate constants of the linear function of the sub-batch number for the corresponding one of the processes. 8. The prediction apparatus according to claim 1, further comprising: a determiner configured to determine whether the average mini-batch size predicted by the predictor lies within a predetermined range. 9. The prediction apparatus according to claim 8, wherein the determiner is configured to: select plural pairs of values of the number of nodes of the learning system and the sub-batch number, the calculated average mini-batch size lying within the predetermined range when each of the selected pairs of values of the number of nodes of the learning system and the sub-batch number is used in the convolutional neural network; and identify one of the selected pairs of values of the number of nodes of the learning system and the sub-batch number, the learning time based on the identified one of the selected pairs of values of the number nodes of the learning system and the sub-batch number becoming minimum. 10. The prediction apparatus according to claim 8, wherein the determiner is configured to: select plural pairs of values of the number of nodes of the learning system and the sub-batch number, the calculated average mini-batch size lying within the predetermined range when each of the selected pairs of values of the number of nodes of the learning system and the sub-batch number is used in the convolutional neural network; and identify one of the selected pairs of values of the number of nodes of the learning system and the sub-batch number, the number of nodes of the learning system in the identified one of the selected pairs of values of the number of nodes the learning system and the sub-batch number becoming minimum. 11. The prediction apparatus according to claim 8, wherein the determiner is configured to: select plural pairs of values of the number of nodes of the learning system and the sub-batch number, the calculated average mini-batch size lying within the predetermined range when each of the selected pairs of values of the number of nodes of the learning system and the sub-batch number is used in the convolutional neural network; and identify one of the selected pairs of values of the number of nodes of the learning system and the sub-batch number, node time based on the identified one of the selected pairs of values of the number of nodes of the learning system and the sub-batch number becoming minimum, the node time being defined as the product of the number of nodes of the learning system and the learning time. 12. A prediction method for a learning system that includes a plurality of nodes each including a central processing unit and at least one graphics processing unit, the central processing unit of each node using the at least one graphics processing unit to calculate, based on a plurality of pieces of training data, a quantity of update of each weight included in a convolutional neural network, the central processing unit of each node performing a weight updating cycle that communicates the quantity of update of each weight with at least one other central processing unit of at least one other node to perform update of the corresponding weight of the convolutional neural network, the prediction method comprising: obtaining, as input variables, at least one parameter indicative of a structure of the convolutional neural network, the number of the nodes of the learning system; and a sub-batch number indicative of the number of pieces of training data collectively processed by the at least one graphic processing unit; and predicting at least one of learning time and an average mini-batch size as a function of the input variables obtained by the obtainer, the learning time being time required for one update of all the weights by the central processing unit, the average mini-batch size being an average number of pieces of training data used for the one update of all the weights. 13. A computer program product for a learning system that includes a plurality of nodes each including a central processing unit and at least one graphics processing unit, the central processing unit of each node using the at least one graphics processing unit to calculate, based on a plurality of pieces of training data, a quantity of update of each weight included in a convolutional neural network, the central processing unit of each node performing a weight updating cycle that communicates the quantity of update of each weight with at least one other central processing unit of at least one other node to perform update of the corresponding weight of the convolutional neural network, the computer program product comprising: a non-transitory computer-readable storage medium; and a set of computer program instructions stored in the computer-readable storage medium, the instructions causing a computer to carry out: a first step of obtaining, as input variables, at least one parameter indicative of a structure of the convolutional neural network, the number of the nodes of the learning system; and a sub-batch number indicative of the number of pieces of training data collectively processed by the at least one graphic processing unit; and a second step of predicting at least one of learning time and an average mini-batch size as a function of the input variables obtained by the obtainer, the learning time being time required for one update of all the weights by the central processing unit, the average mini-batch size being an average number of pieces of training data used for the one update of all the weights.