Abstract: A method for model adaptation, an electronic device, and a computer program product are disclosed. For example, the method comprises processing first input data by using a first machine learning model having first parameter set values, to obtain first feature information of the first input data, the first machine learning model having a capability of self-ordering and the first parameter set values being updated after the processing of the first input data; generating a first classification result for the first input data based on the first feature information by using a second machine learning model having second parameter set values; processing second input data by using the first machine learning model having the updated first parameter set values, to obtain second feature information of the second input data; and generating a second classification result for the second input data based on the second feature information by using the second machine learning model having the second parameter set values.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing data. According to exemplary implementations of the present disclosure, a method for processing data includes: determining a factor associated with a first input of a deep learning model, wherein the factor affects the number of threads for executing the deep learning model; generating a plurality of first partial inputs by using the first input based on the factor, wherein each first partial input in the plurality of first partial inputs is a part of the first input; and performing an operation on the plurality of first partial inputs by using the deep learning model, and generating an output of the deep learning model. Thereby, the data processing performance can be improved, and the resource requirement for data processing is lowered.

Abstract: Embodiments of the present disclosure provide a method for solving a problem, a computing system and a program product. A method for solving a problem includes determining information related to a to-be-solved problem; acquiring, based on the information, knowledge elements that can be used for the to-be-solved problem from a knowledge repository, the knowledge repository storing: solved problems, at least one executable task related to the solved problems, at least one processing flow for implementing the at least one executable task, and a corresponding function module included in the at least one processing flow; and determining, based at least on the acquired knowledge elements, a solution to the to-be-solved problem. By such arrangements, automatic problem solving can be achieved in a faster, simpler way with a lower cost through division of the repository and the knowledge elements.

Abstract: A method for determining a model compression rate comprises determining a near-zero importance value subset from an importance value set associated with a machine learning model, a corresponding importance value in the importance value set indicating an importance degree of a corresponding input of a processing layer of the machine learning model, importance values in the near-zero importance value subset being closer to zero than other importance values in the importance value set; determining a target importance value from the near-zero importance value subset, the target importance value corresponding to a turning point of a magnitude of the importance values in the near-zero importance value subset; determining a proportion of importance values less than the target importance value in the importance value set in the importance value set; and determining the compression rate for the machine learning model based on the determined proportion.

Abstract: A task scheduling method comprises the steps of: in response to the reception of a request for processing a plurality of task sets, creating a current to-be-scheduled task queue in a task processing system based on priorities of the plurality of task sets and tasks in the plurality of task sets, where a plurality of to-be-scheduled tasks in the current to-be-scheduled task queue are scheduled in the same round of scheduling; allocating computing resources used for scheduling the plurality of to-be-scheduled tasks; and enabling the plurality of to-be-scheduled tasks in the current to-be-scheduled task queue to be scheduled by using the computing resources. In this manner, a plurality of tasks with different priorities and quotas can be scheduled according to SLA levels of users, and the efficiency and flexibility of parallel services of cloud computing deep learning models are improved by using a run-time load-balancing scheduling solution.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing data. According to exemplary implementations of the present disclosure, a method for processing data includes: determining a factor associated with a first input of a deep learning model, wherein the factor affects the number of threads for executing the deep learning model; generating a plurality of first partial inputs by using the first input based on the factor, wherein each first partial input in the plurality of first partial inputs is a part of the first input; and performing an operation on the plurality of first partial inputs by using the deep learning model, and generating an output of the deep learning model. Thereby, the data processing performance can be improved, and the resource requirement for data processing is lowered.

Abstract: A task scheduling method comprises the steps of: in response to the reception of a request for processing a plurality of task sets, creating a current to-be-scheduled task queue in a task processing system based on priorities of the plurality of task sets and tasks in the plurality of task sets, where a plurality of to-be-scheduled tasks in the current to-be-scheduled task queue are scheduled in the same round of scheduling; allocating computing resources used for scheduling the plurality of to-be-scheduled tasks; and enabling the plurality of to-be-scheduled tasks in the current to-be-scheduled task queue to be scheduled by using the computing resources. In this manner, a plurality of tasks with different priorities and quotas can be scheduled according to SLA levels of users, and the efficiency and flexibility of parallel services of cloud computing deep learning models are improved by using a run-time load-balancing scheduling solution.

Abstract: Embodiments of the present disclosure provide a method for solving a problem, a computing system and a program product. A method for solving a problem includes determining information related to a to-be-solved problem; acquiring, based on the information, knowledge elements that can be used for the to-be-solved problem from a knowledge repository, the knowledge repository storing: solved problems, at least one executable task related to the solved problems, at least one processing flow for implementing the at least one executable task, and a corresponding function module included in the at least one processing flow; and determining, based at least on the acquired knowledge elements, a solution to the to-be-solved problem. By such arrangements, automatic problem solving can be achieved in a faster, simpler way with a lower cost through division of the repository and the knowledge elements.

Abstract: A method for determining a model compression rate comprises determining a near-zero importance value subset from an importance value set associated with a machine learning model, a corresponding importance value in the importance value set indicating an importance degree of a corresponding input of a processing layer of the machine learning model, importance values in the near-zero importance value subset being closer to zero than other importance values in the importance value set; determining a target importance value from the near-zero importance value subset, the target importance value corresponding to a turning point of a magnitude of the importance values in the near-zero importance value subset; determining a proportion of importance values less than the target importance value in the importance value set in the importance value set; and determining the compression rate for the machine learning model based on the determined proportion.

Abstract: The present disclosure relates to a method, device and product for managing application nodes in a distributed application system. In a method, status of a plurality of application nodes in the distributed application system is obtained. A failed application node is determined among the plurality of application nodes based on the obtained status. A parent application node of the failed application node is determined according to a hierarchical structure of the distributed application system, the hierarchical structure describing connection relationships among the plurality of application nodes.

Abstract: The present disclosure relates to a method, device and product for managing application nodes in a distributed application system. In a method, status of a plurality of application nodes in the distributed application system is obtained. A failed application node is determined among the plurality of application nodes based on the obtained status. A parent application node of the failed application node is determined according to a hierarchical structure of the distributed application system, the hierarchical structure describing connection relationships among the plurality of application nodes.

Abstract: A method for model adaptation, an electronic device, and a computer program product are disclosed. For example, the method comprises processing first input data by using a first machine learning model having first parameter set values, to obtain first feature information of the first input data, the first machine learning model having a capability of self-ordering and the first parameter set values being updated after the processing of the first input data; generating a first classification result for the first input data based on the first feature information by using a second machine learning model having second parameter set values; processing second input data by using the first machine learning model having the updated first parameter set values, to obtain second feature information of the second input data; and generating a second classification result for the second input data based on the second feature information by using the second machine learning model having the second parameter set values.