Abstract: A resource scheduling method and apparatus, a device, and a storage medium are provided, and relates to the field of computer technology, and in particular to the field of deep learning technology. The method includes: acquiring a graphics processing unit (GPU) topology relationship of a cluster according to GPU connection information of each of computing nodes in the cluster; and in a case where a task request, for applying for a GPU resource, for a target task is received, determining a target computing node of the target task and a target GPU in the target computing node according to the task request and the GPU topology relationship, to complete GPU resource scheduling of the target task. The present disclosure can optimize the resource scheduling.

Abstract: An apparatus and a method for executing a customized production line using an artificial intelligence development platform, a computing device and a computer readable storage medium are provided. The apparatus includes: a production line executor configured to generate a native form of the artificial intelligence development platform based on a file set, the native form to be sent to a client accessing the artificial intelligence development platform so as to present a native interactive page of the artificial intelligence development platform; and a standardized platform interface configured to provide an interaction channel between the production line executor and the artificial intelligence development platform. The production line executor is further configured to generate an intermediate result by executing processing logic defined in the file set and to process the intermediate result by interacting with the artificial intelligence development platform via the standardized platform interface.

Abstract: A method includes: determining at least one typical object ratio from a first training data set by counting ratios of objects in training pictures of the first training data set; determining at least one picture scaling size based at least on the at least one typical object ratio; scaling the training pictures of the first training data set according to the at least one picture scaling size; obtaining a second training data set by slicing the scaled training pictures; training an object detection model using the second training data set; and performing object detection on a to-be-detected picture using the trained object detection model. The object detection method according to the embodiments of the present disclosure can be used to complete, without manual intervention, a task of detecting an extremely small object.

Abstract: A method and apparatus for generating a model, and a method and apparatus for recognizing information are provided. An implementation of the method for generating a model includes: acquiring a to-be-converted model, a topology description of the to-be-converted model, and device information of a target device; converting, based on the topology description and the device information, parameters and operators of the to-be-converted model to obtain a converted model applicable to the target device; and generating a deep learning prediction model based on the converted model. This embodiment enables the conversion of an existing model to a deep learning prediction model that can be applied to a target device.

Abstract: The present application discloses an image recognition method, apparatus, an electronic device and a storage medium, and relates to the field of neural networks and depth learning. An implementation solution may be as follows: loading a first image recognition model; inputting an image to be recognized into a first image recognition model; predicting the image to be recognized by using a first image recognition model to obtain an output result of a network layer of the first image recognition model; and performing post-processing on the output result of the network layer of the first image recognition model, to obtain an image recognition result.

Abstract: A method for management of an artificial intelligence development platform is provided. The artificial intelligence development platform is deployed with instances of a plurality of model services, and each of the model services is provided with one or more instances. The method includes: acquiring calling information of at least one model service; determining the activity of the at least one model service according to the calling information; and at least deleting all instances of the at least one model service in response to that the determined activity meets a first condition.

Abstract: A method and apparatus for generating a model, and a method and apparatus for recognizing information are provided. An implementation of the method for generating a model includes: acquiring a to-be-converted model, a topology description of the to-be-converted model, and device information of a target device; converting, based on the topology description and the device information, parameters and operators of the to-be-converted model to obtain a converted model applicable to the target device; and generating a deep learning prediction model based on the converted model. This embodiment enables the conversion of an existing model to a deep learning prediction model that can be applied to a target device.