Abstract: A data processing method and apparatus based on a recurrent neural network and a device are provided. The recurrent neural network includes multiple recurrent units, each recurrent unit includes multiple data processing nodes and a start node, at least one recurrent unit includes an end node, and at least one data processing node is included between the start node and the end node. During the processing of the first target processing object in a first recurrent unit, in a case that the first target processing object does not satisfy the first preset condition, the start node in the first recurrent unit is run to add a tag to the data processing nodes subsequent to the start node and stop addition of the tag in response to reaching the end node, and no processing is performed by the data processing nodes with the tag.

Abstract: A method and apparatus of converting a schema in a deep learning framework, an electronic device, and a computer storage medium are provided. The method of converting the schema in the deep learning framework includes: updating a first schema, based on first syntax elements in the first schema and a context relationship between the first syntax elements in the first schema, so as to obtain an updated first schema; generating second syntax elements corresponding to updated first syntax elements in the updated first schema, based on a mapping relationship between the updated first syntax elements in the updated first schema and second syntax elements in a second schema system; and combining the second syntax elements according to a context relationship between the updated first syntax elements, so as to generate a second schema.

Abstract: The present disclosure discloses a method, an apparatus and an electronic device for deploying an operator in a deep learning framework and relates to the field of artificial intelligence technology such as deep learning. And the solution is: acquiring a source file of the operator; compiling the source file of the operator to form a dynamic link library of the operator; generating an interface file transferred from the dynamic link library of the operator; generating an installable library file according to the dynamic link library and the interface file; installing the installable library file to a target programming language library.

Abstract: The present disclosure discloses a method, an apparatus and an electronic device for deploying an operator in a deep learning framework and relates to the field of artificial intelligence technology such as deep learning. And the solution is: acquiring a source file of the operator; compiling the source file of the operator to form a dynamic link library of the operator; generating an interface file transferred from the dynamic link library of the operator; generating an installable library file according to the dynamic link library and the interface file; installing the installable library file to a target programming language library.

Abstract: According to exemplary embodiments of the present disclosure, there is provided a method and apparatus of converting a schema in a deep learning framework, and a computer storage medium.

Abstract: A data processing method and apparatus based on a recurrent neural network and a device are provided. The recurrent neural network includes multiple recurrent units, each recurrent unit includes multiple data processing nodes and a start node, at least one recurrent unit includes an end node, and at least one data processing node is included between the start node and the end node. During the processing of the first target processing object in a first recurrent unit, in a case that the first target processing object does not satisfy the first preset condition, the start node in the first recurrent unit is run to add a tag to the data processing nodes subsequent to the start node and stop addition of the tag in response to reaching the end node, and no processing is performed by the data processing nodes with the tag.

Abstract: The disclosure provides a method and a device for operator registration processing based on deep learning, an electronic device, and a storage medium. Description information and logic operation information of a registered operator are obtained. An operator type of the registered operator is determined based on the logic operation information. Configuration information corresponding to the operator type and preset in a deep learning framework is obtained. It is detected whether the registered operator is registered completely based on the description information and the configuration information.