Abstract: A method for performance tuning in Automated Machine Learning (Auto ML) includes obtaining preset application program interface and system resources of the automatic machine learning system. Performance index measurement values are obtained according to the preset application program interface when the system pre-trains deep learning training model candidates. A distribution strategy and a resource allocation strategy are determined according to the performance index measurement values and the system resources and computing resources of the system are allocated according to the distribution strategy and the resource allocation strategy. The disclosure also provides an electronic device and a non-transitory storage medium.

Abstract: A parameter synchronization method is implemented in a computing device. The parameter synchronization method includes importing a deep learning training task of a preset model into a server communicatively coupled to the computing device, recording a preset number of iterative processes during the deep learning training, dividing each iterative process into a number of phases according to time, determining whether a time ratio of an H2D phase, a D2H phase, and a CPU phase in each iterative process is greater than a preset value, and confirming the server to use a copy mode for performing parameter synchronization when the time ratio of the H2D, D2H, and CPU phases is determined to be greater than the preset value.

Abstract: A container manager used in a method for configuring a deep learning program acquires deep learning program, and analyzes at least one performance indicator from the acquired deep learning program and sends the at least one performance indicator to a server. The server determines a hardware configuration and a container image according to the at least one performance indicator, generates a label containing the name of the server, the determined hardware configuration, and the container image, and sends the label to the container manager. The container manager receives the label from the server, and determines whether the label contain the name of the server, and deploys a container for the deep learning program if the label contains the name of the server.

Abstract: A method for performance tuning in Automated Machine Learning (Auto ML) includes obtaining preset application program interface and system resources of the automatic machine learning system. Performance index measurement values are obtained according to the preset application program interface when the system pre-trains deep learning training model candidates. A distribution strategy and a resource allocation strategy are determined according to the performance index measurement values and the system resources and computing resources of the system are allocated according to the distribution strategy and the resource allocation strategy. The disclosure also provides an electronic device and a non-transitory storage medium.

Abstract: A parameter synchronization method is implemented in a computing device. The parameter synchronization method includes importing a deep learning training task of a preset model into a server communicatively coupled to the computing device, recording a preset number of iterative processes during the deep learning training, dividing each iterative process into a number of phases according to time, determining whether a time ratio of an H2D phase, a D2H phase, and a CPU phase in each iterative process is greater than a preset value, and confirming the server to use a copy mode for performing parameter synchronization when the time ratio of the H2D, D2H, and CPU phases is determined to be greater than the preset value.

Abstract: A container manager used in a method for configuring a deep learning program acquires deep learning program, and analyzes at least one performance indicator from the acquired deep learning program and sends the at least one performance indicator to a server. The server determines a hardware configuration and a container image according to the at least one performance indicator, generates a label containing the name of the server, the determined hardware configuration, and the container image, and sends the label to the container manager. The container manager receives the label from the server, and determines whether the label contain the name of the server, and deploys a container for the deep learning program if the label contains the name of the server.