CN114490116A - Data processing method and device, electronic equipment and storage medium - Google Patents
Data processing method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The disclosure relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, big data processing and the like. The method comprises the following steps: the method comprises the steps of obtaining data to be processed, determining a target operator type in a data processing model corresponding to the data to be processed, obtaining target operator resources described by the target operator type, and processing the data to be processed by adopting the target operator resources to obtain a data processing result. Therefore, the calling logic of the operator in the data processing model can be simplified, the operator calling efficiency and the operator calling effect are effectively improved, the data processing effect is effectively improved, and the data processing method is convenient to deploy and apply in artificial intelligence.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to the field of deep learning and big data processing technologies, and in particular, to a data processing method and apparatus, an electronic device, and a storage medium.
Background
Artificial intelligence is the subject of research that makes computers simulate some human mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), both at the hardware level and at the software level. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.
In the related art, the calling relationship of operators in a data processing model is complex, and an operator calling framework needs to be developed in a customized manner to complete data processing.
Disclosure of Invention
The disclosure provides a data processing method, a data processing apparatus, an electronic device, a storage medium and a computer program product.
According to a first aspect of the present disclosure, there is provided a data processing method, including: acquiring data to be processed; determining a target operator type in a data processing model corresponding to the data to be processed; acquiring target operator resources described by the target operator type; and processing the data to be processed by adopting the target operator resource to obtain a data processing result.
According to a second aspect of the present disclosure, there is provided a data processing apparatus comprising: the first acquisition module is used for acquiring data to be processed; the determining module is used for determining a target operator type in the data processing model corresponding to the data to be processed; the second acquisition module is used for acquiring the target operator resource described by the target operator type; and the first processing module is used for processing the data to be processed by adopting the target operator resource so as to obtain a data processing result.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the data processing method according to the first aspect of the disclosure.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the data processing method according to the first aspect of the present disclosure.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of the data processing method according to the first aspect of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure;
FIG. 2 is a schematic diagram according to a second embodiment of the present disclosure;
FIG. 3 is a schematic diagram according to a third embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a process level resource and thread level resource relationship in accordance with an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a proxy device according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of an RPC framework according to an embodiment of the present disclosure;
FIG. 7 is a schematic diagram according to a fourth embodiment of the present disclosure;
FIG. 8 is a schematic diagram according to a fifth embodiment of the present disclosure;
FIG. 9 shows a schematic block diagram of an example electronic device for implementing the data processing method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram according to a first embodiment of the present disclosure.
It should be noted that an execution main body of the data processing method of this embodiment is a data processing apparatus, the apparatus may be implemented by software and/or hardware, the apparatus may be configured in an electronic device, and the electronic device may include, but is not limited to, a terminal, a server, and the like.
The disclosure relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, natural language processing and the like.
Wherein, Artificial Intelligence (Artificial Intelligence), english is abbreviated as AI. The method is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.
Deep learning is to learn the intrinsic rules and expression levels of sample data, and the information obtained in the learning process is helpful to the interpretation of data such as characters, images and sounds. The final goal of deep learning is to make a machine capable of human-like analytical learning, and to recognize data such as characters, images, and sounds.
The big data processing refers to a process of analyzing and processing large-scale data in an artificial intelligence mode, and the big data can be summarized into 5V, and has large data Volume (Volume), high speed (Velocity), multiple types (Velocity), Value (Value) and authenticity (Veracity).
In the embodiment of the disclosure, the processing modes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the related data all conform to the regulations of related laws and regulations, and do not violate the customs of the public order.
As shown in fig. 1, the data processing method includes:
s101: and acquiring data to be processed.
The data to be processed currently may be referred to as data to be processed, and the data to be processed may include some data description information, and may be correspondingly processed by the data processing model, which is not limited to this.
For example, the data to be processed may be transaction record data in electronic commerce; the data to be processed can be deposit data, loan and other business record data in finance; the data to be processed may also be, for example, multimedia data in the form of audio and video, without limitation.
In the embodiment of the present disclosure, an operator for executing a data processing task in artificial intelligence may be applied to implement analysis, mining, and other processes on data to be processed.
When the to-be-processed data is acquired, the to-be-processed data may be identified and extracted in a system or a platform where the to-be-processed data operates, or the data to be currently processed may be acquired from a third-party data platform as the to-be-processed data, or any other possible implementation manner may be used to acquire the to-be-processed data, which is not limited herein.
S102: and determining a target operator type in the data processing model corresponding to the data to be processed.
The operators are basic computing units (algorithm modules) in an artificial intelligence framework, and have important influence on model training, prediction, deployment and the like of artificial intelligence, and the algorithm modules integrate and design corresponding algorithm processing logic.
For example, the operator may be an algorithm module for digital computation, image processing, data processing, and the like, and for example, the operator may be divided into a statistical computation operator, a mathematical operation operator, a variance analysis operator, a regression analysis operator, a sensitivity analysis operator, a cluster analysis operator, a fuzzy evaluation operator, and the like, which is not limited thereto.
The operator for processing the data to be processed may be referred to as a target operator, and the target operator may be used to match a corresponding target operator resource, perform operations such as calling, and the like, without limitation.
In the embodiment of the present disclosure, a processing task for processing data to be processed may be determined, the processing task is task decomposed to determine an operator type in a data processing model related to the processing task, and the related operator type is used as a target operator type, and the target operator type may be used to identify and call a target operator in the data processing model related to the processing task, or multiple target operator types in the data processing model may be determined according to a type of a data resource used in program calculation for the data to be processed, or a target operator type in the data processing model corresponding to the data to be processed may be determined by using any other possible implementation manner, which is not limited herein.
For example, the target operator type may be, for example, a digital calculation type, an image processing type, a statistical calculation type, a mathematical operation type, a variance analysis type, and the like, and accordingly, different target operator types may respectively identify a target operator related to the call, the digital calculation type identifies the digital calculation operator, the image processing type identifies the image processing operator, the statistical calculation type identifies the statistical calculation operator, the mathematical operation type identifies the mathematical operation operator, the variance analysis type identifies the variance analysis operator, and the like, which is not limited thereto.
After the data to be processed is obtained and the target operator type in the data processing model corresponding to the data to be processed is determined, the target operator resource described by the target operator type can be directly triggered and obtained, the target operator resource can be generated by disassembling, packaging, interface configuration and the like of algorithm resources and calling logic of a target operator in advance, so that the target operator resource described by the target operator type can be directly called, the data to be processed is processed by adopting the target operator resource, and the calling logic of an operator in the data processing model is simplified.
S103: and acquiring the target operator resources described by the target operator type.
The data resource required by the target operator in the artificial intelligence operation processing may be referred to as a target operator resource, and the target operator resource may be generated by abstracting, disassembling, packaging, interface configuration, and the like, in advance, a Remote Procedure Call (RPC) framework, the resource used by the target operator in the artificial intelligence operation processing, and the algorithm resource and the Call logic of the target operator are generated, which is not limited to this.
In some embodiments, the type of the data to be processed may be determined, and the operator resource used when processing other data of the same type is determined from the historical data processing task as the target operator resource.
In other embodiments, the target operator resource described by the target operator type may also be obtained from a resource pool related to a data processing model corresponding to the data to be processed, or the target operator resource described by the target operator type may be obtained from a big data processing platform, which is not limited to this.
For example, operator resources of a plurality of operators in the data processing model (for example, a dictionary, a configuration file, a user-customized model, and the like related to the corresponding operator are included, and a local variable to be called by the operator at each inference can also be included) are configured in the big data processing platform, and when the data to be processed is processed, the operator resources of the target operator identified by the target operator type are obtained from the big data processing platform as the target operator resources, which is not limited herein.
S104: and processing the data to be processed by adopting the target operator resource to obtain a data processing result.
The data processing method includes the steps of processing data to be processed by using target operator resources of corresponding target operators in a data processing model to obtain a processing result, which may be called a data processing result, calling and processing the target operators to obtain result data, or performing calculation, updating and other processing on the data to be processed according to the target operator resources to obtain a data processing result, and is not limited to this.
In some embodiments, when the target operator resource is used to process the data to be processed to obtain the data processing result, the target operators may be called to respectively correspond to the target operator resources according to a calling relationship between different target operators, and the data to be processed is processed by combining the target operator resources to obtain the data processing result, or when the target operator resource is used to process the data to be processed to obtain the data processing result, the data to be processed may also be processed by combining the target operator resource and the third-party service according to the calling relationship between the target operator and the third-party service to obtain the data processing result, of course, the data to be processed may also be processed by using any other possible processing method to achieve that the target operator resource is used to process the data to be processed to obtain the data processing result, which is not limited.
In the embodiment, by acquiring the data to be processed, determining a target operator type in the data processing model corresponding to the data to be processed, acquiring target operator resources described by the target operator type, and processing the data to be processed by adopting the target operator resources to obtain a data processing result, the target operator resources described by the target operator type are directly acquired, and the data to be processed is processed by adopting the target operator resources to simplify the calling logic of operators in the data processing model, effectively improve the operator calling efficiency and the operator calling effect, effectively improve the data processing effect, and facilitate the deployment and application of the data processing method in artificial intelligence.
Fig. 2 is a schematic diagram according to a second embodiment of the present disclosure.
As shown in fig. 2, the data processing method includes:
s201: and acquiring data to be processed.
S202: and determining a target operator type in the data processing model corresponding to the data to be processed.
For the description of S201-S202, reference may be made to the above embodiments, which are not described herein again.
S203: and determining a target operator corresponding to the target operator type.
In the embodiment of the present disclosure, the operator may be labeled with a corresponding operator type in advance, for example, the algorithmic functions of the individual operators may be categorized, summarized to form corresponding operator types, which, in turn, after determining the target operator type in the data processing model corresponding to the data to be processed, the operator type matched with the target operator type can be determined from a plurality of operator types configured in advance, and then the operator marked by the matched operator type is used as the target operator, or, the operators can be divided into one or more groups according to the groups, the target operator type of the corresponding one or more groups of operators is marked, and then the operators in the groups marked by the target operator type are determined as the target operators, or, the target operator corresponding to the target operator type may also be determined in any other possible manner, which is not limited to this.
S204: and acquiring process-level resources and thread-level resources related to the target operator.
During the process of processing the data to be processed, the global operation resource related to the target operator may be referred to as a process-level resource, and during the process of processing the data to be processed, the local operation resource related to the target operator may be referred to as a thread-level resource.
The process-level resources may include data resources such as dictionaries, configuration files, and user-customized models that are involved in the process of processing the data to be processed by using the target operator, and the thread-level resources may include data resources such as local variables that are used when the data inference is performed by using the target operator, which is not limited to this.
In the embodiment of the present disclosure, in the data processing process, a target operator may be determined according to a target operator type, and then a process-level resource and a thread-level resource related to the target operator are obtained, or a corresponding target operator type may also be determined in advance, and operations such as calling and reasoning services of the target operator are determined based on the target operator type, and a process-level resource and a thread-level resource related to the operations such as calling and reasoning services are obtained, or any other possible implementation manner may also be used to obtain a process-level resource and a thread-level resource related to the target operator, which is not limited to this.
In the embodiment of the present disclosure, the process-level resource and the thread-level resource corresponding to the target operator may be obtained according to a calling manner or a remote protocol related to the target operator, or, according to an algorithm processing logic of the target operator, a global operation resource and a local operation resource required in an arithmetic processing logic operation process are selected, and the global operation resource is used as the process-level resource, and the local operation resource is used as the thread-level resource, which is not limited to this.
S205: and taking the process-level resource and the thread-level resource as a target operator resource together.
After acquiring the process-level resources and the thread-level resources related to the target operator, the embodiments of the present disclosure may use the process-level resources and the thread-level resources as the target operator resources, that is, when acquiring the corresponding target operator resource based on the target operator type, the method can support acquiring the process-level resource and the thread-level resource of the corresponding target operator based on the target operator type, thereby being capable of facilitating the data processing model to call the process level resources and the thread level resources related to the target operator in the process of processing the data to be processed to a greater extent, leading the call of the process level resources and the thread level resources related to the target operator to be more convenient and faster, in addition, in the design mode, targeted and personalized development and configuration can be conveniently carried out on different target operators, decoupling between different target operators is effectively realized, and the calling and using convenience and efficiency of the target operators in the data processing process are effectively improved.
S206: and processing the data to be processed by adopting the target operator resource to obtain a data processing result.
After the process-level resources and the thread-level resources related to the target operator are obtained, the process-level resources and the thread-level resources can be jointly used as the target operator resources, and then the process-level resources and the thread-level resources of the target operator can be combined to process the data to be processed, so that a data processing result is obtained.
For example, the process-level resources may include data resources such as a dictionary, a configuration file, and a user-customized model, which are involved in the process of processing the to-be-processed data by using the target operator, and the thread-level resources may include data resources such as a local variable, which are used when the data inference is performed by using the target operator, so that when the to-be-processed data is processed by using the target operator resources, the to-be-processed data may be processed by using the dictionary, the configuration file, the user-customized model, and the like, and then the to-be-processed data is subjected to auxiliary processing based on the data resources such as the local variable, which are used when the data inference is invoked by the interface, so as to obtain a data processing result, which is not limited.
S207: and emptying the thread-level resources related to the target operator.
In the embodiment of the present disclosure, a corresponding clearing interface may be preset, and when the target operator is adopted to finish processing the to-be-processed data, the clearing interface may be called to clear or return the thread-level resource related to the target operator, which is not limited herein.
In the embodiment of the present disclosure, an interface for clearing or returning a data processing result corresponding to the target operator may be defined, the RPC framework calls the interface for clearing or returning the data processing result, and receives the returned data processing result, and the target operator implements format conversion and data encapsulation of the specific output data processing result, which is not limited to this.
For example, the thread-level resource handling state is set to "x" (x may be a combination of a number, a symbol, a character string, and the like, which is not limited), after the data processing is finished, the data information in the corresponding configuration file containing the thread-level resource is changed to "x", and the emptying processing of the thread-level resource related to the target operator is completed.
In the embodiment, by acquiring the data to be processed, determining a target operator type in the data processing model corresponding to the data to be processed, acquiring target operator resources described by the target operator type, and processing the data to be processed by adopting the target operator resources to obtain a data processing result, the target operator resources described by the target operator type are directly acquired, and the data to be processed is processed by adopting the target operator resources to simplify the calling logic of operators in the data processing model, effectively improve the operator calling efficiency and the operator calling effect, effectively improve the data processing effect, and facilitate the deployment and application of the data processing method in artificial intelligence. When the corresponding target operator resource is obtained based on the target operator type, the process-level resource and the thread-level resource of the corresponding target operator can be obtained based on the target operator type, so that the data processing model can call the process-level resource and the thread-level resource related to the target operator in the process of processing the data to be processed to a great extent, the call of the process-level resource and the thread-level resource related to the target operator is more convenient, in addition, in the design mode, the targeted and personalized development and configuration can be conveniently carried out aiming at different target operators, the decoupling among different target operators can be effectively realized, and the call and use convenience and the use efficiency of the target operator in the data processing process can be effectively improved. The thread-level resources related to the target operator are emptied, and the thread-level resources represent the local operation resources related to the corresponding target operator, so that excessive resource storage space consumption of the local operation resources can be effectively avoided, and the data processing efficiency is improved to a greater extent.
Fig. 3 is a schematic diagram according to a third embodiment of the present disclosure.
As shown in fig. 3, the data processing method includes:
s301: and acquiring data to be processed.
S302: and determining a target operator type in the data processing model corresponding to the data to be processed.
S303: and determining a target operator corresponding to the target operator type.
For the description of S301 to S303, reference may be made to the above embodiments, which are not described herein again.
S304: and determining a target calling mode related to the target operator.
The calling mode related to the target operator may be referred to as a target calling mode, and the calling mode may be used to describe a calling relationship between the target operator and other operators, a third party service, and different resources inside the target operator.
The target calling manner may specifically be, for example, a calling manner between various resources related to the target operator, or may also be, for example, a calling manner of the target operator to another operator, or may also be, for example, a calling manner between the target operator and a third-party service, which is not limited to this.
For example, the target calling manner may be that the target operator a calls the service B through a Remote Dictionary service (Redis) protocol; the target operator A locally calls a target operator C; the target operator C locally calls an operator D; the target operator C calls the operator E through a hypertext Transfer Protocol (HTTP), and both the direct calling mode (direct calling mode, that is, the target operator a locally calls the target operator C) related to the target operator a and the indirect calling mode (indirect calling mode, that is, the calling mode of the target operator C called by the target operator a for other contents) related to the target operator a may be referred to as target calling mode related to the target operator a.
When determining the target calling mode related to the target operator, the embodiments of the present disclosure may refer to the target operator type, determine the calling mode adapted to the target operator type from the RPC framework as the target calling mode, the RPC frame can preset the calling modes related to various operator types, realize that in the actual data processing process, the RPC framework manages and provides calling modes of each operator pair downstream, and supports subsequent actual calling processing, or analyzing the global calling relation of the target operator in the data processing model, analyzing the global calling relation to disassemble the target calling mode related to the target operator from the global calling relation, of course, any other possible manner may be adopted to determine the target invocation manner related to the target operator, which is not limited to this.
Optionally, in some embodiments, determining a target invocation mode related to the target operator may be determining a service invocation mode between the target operator and a third-party service, where the service invocation mode is taken as the target invocation mode; and/or determining resource calling modes of various resources in the target operator, wherein the resource calling mode is used as a target calling mode, the resources are process-level resources or thread-level resources, and the service calling mode and/or the resource calling mode are used as the target calling mode, so that the flexibility of operator calling in the data processing model can be effectively improved, the deployment and the application of the data processing method in the data processing model are effectively improved, the data processing mode can be effectively adapted to diversified types of calling modes in the data processing model, the diversified types of calling modes in the data processing model are fully utilized, the completeness and the comprehensiveness of operator calling in the data processing model are greatly improved, and the accuracy of data processing is guaranteed.
The calling method between the target operator and the corresponding various third-party services may be referred to as a service calling method, and the third-party services may support, for example, services of the hypertext transfer protocol HTTP, and the like, without limitation.
The method for calling different resources inside the target operator may be referred to as a resource calling method, where the resource may be the process-level resource or the thread-level resource, and the resource calling method may be a calling method between different process-level resources, or may also be a calling method between different thread-level resources, or may also be a calling method between a process-level resource and a thread-level resource, and the like, which is not limited herein.
In the embodiment of the present disclosure, interface information corresponding to a target operator may be determined according to a transmission protocol, and then the target operator and a third-party service are called, or a target operator resource may also be determined, a process-level resource and a thread-level resource related to the target operator are called, and a service calling manner for the third-party service and a resource calling manner for the target operator resource may both be used as a target calling manner, which is not limited to this.
In the embodiment of the present disclosure, as shown in fig. 4, fig. 4 is a schematic diagram illustrating a relationship between a process-level resource and a thread-level resource according to the embodiment of the present disclosure, after a data processing request is received, an RPC frame completes calls of an operator a and an operator B in sequence, the RPC frame can call the thread-level resources corresponding to the operator a and the operator B in sequence, the thread-level resources can execute a call operation through a series of corresponding interfaces, and the process-level resource corresponding to each operator can call the thread-level resource corresponding to the operator as needed, which is not limited to this.
Optionally, in some embodiments, the number of the target operators is multiple, where determining the target invocation manner related to the target operator further includes: and determining an inter-operator calling mode between the target operator and other target operators, wherein the inter-operator calling mode is used as the target calling mode, and the other target operators belong to a plurality of target operators, so that the adaptation effect of the data processing mode on diversified types of calling modes in the data processing model can be further improved, the completeness and comprehensiveness of operator calling in the data processing model are greatly ensured, and the accuracy of data processing is ensured.
The calling mode between the target operator and other target operators may be referred to as an inter-operator calling mode, and the inter-operator calling mode may be used to implement logical calling between multiple target operators.
For example, operator a may call operator B locally, or operator a may call operator B through a third-party protocol, and both may be regarded as an inter-operator call mode.
In some embodiments, the invocation among the target operators may be implemented by logic code, and the logic code may be defined by a corresponding computer program language, which is not limited thereto.
In other embodiments, the call between operators may also be implemented by using various logic systems related to mathematical operations, and the system related to mathematical operations may be a digital processing system, or may also be an image processing system, which is not limited to this.
In other embodiments, the inter-operator calling mode corresponding to each type of data embedding point and interface information may also be determined according to each type of data embedding point or interface information, which is not limited herein.
S305: the target calling mode is provided to the calling agent device.
The virtual or entity device for implementing the target call mode may be referred to as a call proxy device, and the call proxy device may be a service RPC framework for implementing a service call mode, a resource call mode, and an inter-operator call mode, which is not limited to this.
In the embodiment of the disclosure, a corresponding call agent device may be set, interface information and corresponding data embedding points required by a target call mode may be configured in the call agent device, an RPC framework may provide a general call agent device for a plurality of target operators, calls of other target operators, services, and target operator resources by the target operators may all be completed by the call agent device, the call agent device may provide a unified operator model management mechanism, operators in various data processing models may be developed in the call agent device, and no limitation is imposed on the operators.
In the embodiment of the disclosure, the calling agent device is used, so that the calling relation of a plurality of target operators to the downstream can be effectively simplified, the calling agent device can realize the calling of a plurality of third-party remote protocols based on local data resources, a plurality of data embedding points can be built in the calling agent device, and various calling processing logics among the target operators are simplified.
In the embodiment of the present disclosure, as shown in fig. 5, fig. 5 is a schematic diagram of an agent device according to the embodiment of the present disclosure, a plurality of operators may implement calling and reasoning services by a unified calling agent device, a calling relationship of each operator connected to the calling agent device may be simplified as calling of the downstream target operator by the calling agent device, and implementation of calling of various services between target operators is completed according to the calling agent device, so that a target calling manner is simplified, the target calling manner is simpler and more convenient, customized development of the target operator and an RPC framework is reduced, and a logic problem of complicated calling between target operators is effectively reduced.
S306: and acquiring the process level resources and the thread level resources related to the target operator by the calling agent device according to the target calling mode.
In the embodiment of the present disclosure, the process-level resource and the thread-level resource related to the target operator are obtained by the call agent device according to the target call mode, data resources such as a dictionary, a configuration file, a user customized model, a local variable, and the like required by the target call mode may be determined by the call and inference service of the call agent device to the target operator, and the obtained data resources are classified into the process-level resource and the thread-level resource, or the process-level resource and the thread-level resource corresponding to the interface information may also be obtained according to a plurality of interface information configured in the agent device, which is not limited to this.
S307: and processing the data to be processed by adopting the process level resources and the thread level resources to obtain a data processing result.
In the embodiment of the present disclosure, after the process-level resource and the thread-level resource related to the target operator are obtained by the call agent device according to the target call mode, the process-level resource and the thread-level resource of the target operator may be combined to process the data to be processed, so as to obtain a data processing result.
In the embodiment, the target operator resource described by the target operator type is directly obtained, and the target operator resource is adopted to process the data to be processed, so that the calling logic of the operator in the data processing model is simplified, the operator calling efficiency and the operator calling effect are effectively improved, the data processing effect is effectively improved, and the data processing method is convenient to deploy and apply in artificial intelligence. The data processing method comprises the steps of obtaining data to be processed, determining a target operator type in a data processing model corresponding to the data to be processed, determining a target operator corresponding to the target operator type, determining a target calling mode related to the target operator, providing the target calling mode into a calling proxy device, obtaining process-level resources and thread-level resources related to the target operator through the calling proxy device according to the target calling mode, processing the data to be processed by adopting the process-level resources and the thread-level resources to obtain a data processing result, abstracting the target operator due to the fact that the target calling mode related to the target operator is determined, obtaining the process-level resources and the thread-level resources related to the target operator according to the target calling mode, and calling the process-level resources and the thread-level resources related to the target operator in the process of processing the data to be processed by the data processing model to a great extent, the calling agent device is used for acquiring the process level resources and the thread level resources related to the target operator according to the target calling mode, the calling agent device can realize the calling of the target operator based on the upstream and downstream relations, the calling logics of various target calling modes are simplified, the calling logics of the target calling modes are more convenient and faster, meanwhile, the calling agent device can be automatically adapted and carry out model scheduling, and the scheduling management of various operators and resources in a data processing model can be effectively facilitated.
In summary, as shown in fig. 6, fig. 6 is a schematic diagram of an RPC framework according to the embodiment of the present disclosure, where the RPC framework abstracts resources used by a target operator in program computation, and divides the resources into process-level resources and thread-level resources. And the RPC frame completes the management and scheduling of the target operator according to the resource interface, the built-in basic components and the built-in upper-layer components. The RPC framework can also design a uniform data container for the process-level resources of the target operator, provide rich management interfaces, allow a plurality of user customized models to be loaded in one inference service, and allow a plurality of sets of inference services to be deployed to disperse the pressure caused by excessive customized model numbers when the number of the user customized models exceeds the upper limit which can be borne by a single service (such as the situation of excessive memory application). In a complete inference service calling network, when inference services are newly added, an RPC frame can be internally provided with routing capability, can acquire complete routing information from a terminal, and takes effect when a target operator service is called.
The process can be transparent to the target operator, so that a plurality of customized reasoning services can be dynamically adapted, when the number of the customized models of the user changes, the customized models can be automatically adapted and model scheduling can be carried out, relevant developers do not need to add extra development cost for the management of the customized models, the development of logic can be simplified, and meanwhile, the consumption of the development cost is reduced.
Fig. 7 is a schematic diagram according to a fourth embodiment of the present disclosure.
As shown in fig. 7, the data processing apparatus 70 includes:
a first obtaining module 701, configured to obtain data to be processed;
a determining module 702, configured to determine a target operator type in a data processing model corresponding to data to be processed;
a second obtaining module 703, configured to obtain a target operator resource described by a target operator type; and
the first processing module 704 is configured to process the data to be processed by using the target operator resource to obtain a data processing result.
In some embodiments of the present disclosure, as shown in fig. 8, fig. 8 is a schematic diagram according to a fifth embodiment of the present disclosure, the data processing apparatus 80, including: the device comprises a first obtaining module 801, a determining module 802, a second obtaining module 803 and a first processing module 804, wherein the second obtaining module 803 comprises:
a determining submodule 8031, configured to determine a target operator corresponding to the target operator type;
an obtaining submodule 8032, configured to obtain process-level resources and thread-level resources related to the target operator; and
the processing submodule 8033 is configured to use the process-level resource and the thread-level resource as a target operator resource; the process-level resources are global operation resources related to the target operator in the process of processing the data to be processed, and the thread-level resources are local operation resources related to the target operator in the process of processing the data to be processed.
In some embodiments of the present disclosure, among others, acquisition submodule 8032 includes:
a determining unit 80321, configured to determine a target calling manner related to a target operator;
the obtaining unit 80322 is configured to obtain, according to the target invocation manner, a process-level resource and a thread-level resource related to the target operator.
In some embodiments of the present disclosure, the determining unit 80321 is specifically configured to:
determining a service calling mode between a target operator and a third-party service, wherein the service calling mode is used as a target calling mode; and/or
And determining resource calling modes of various resources in the target operator, wherein the resource calling modes are used as target calling modes, and the resources are process-level resources or thread-level resources.
In some embodiments of the present disclosure, the number of target operators is plural;
the determining unit 80321 is specifically configured to:
and determining an inter-operator calling mode between the target operator and other target operators, wherein the inter-operator calling mode is used as a target calling mode, and the other target operators belong to a plurality of target operators.
In some embodiments of the present disclosure, the obtaining unit 80322 is specifically configured to:
providing the target calling mode to the calling agent device;
and acquiring the process level resources and the thread level resources related to the target operator by the calling agent device according to the target calling mode.
In some embodiments of the present disclosure, further comprising:
the second processing module 805 is configured to perform emptying processing on the thread-level resource related to the target operator after the target operator resource is used to process the data to be processed to obtain a data processing result.
It is understood that the data processing apparatus 80 in fig. 8 of the present embodiment and the data processing apparatus 70 in the foregoing embodiment, the first obtaining module 801 and the first obtaining module 701 in the foregoing embodiment, the determining module 802 and the determining module 702 in the foregoing embodiment, the second obtaining module 803 and the second obtaining module 703 in the foregoing embodiment, and the first processing module 804 and the first processing module 704 in the foregoing embodiment may have the same functions and structures.
It should be noted that the foregoing explanation of the data processing method is also applicable to the data processing apparatus of the present embodiment, and is not repeated herein.
In the embodiment, by acquiring the data to be processed, determining a target operator type in the data processing model corresponding to the data to be processed, acquiring target operator resources described by the target operator type, and processing the data to be processed by adopting the target operator resources to obtain a data processing result, the target operator resources described by the target operator type are directly acquired, and the data to be processed is processed by adopting the target operator resources to simplify the calling logic of operators in the data processing model, effectively improve the operator calling efficiency and the operator calling effect, effectively improve the data processing effect, and facilitate the deployment and application of the data processing method in artificial intelligence.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 9 shows a schematic block diagram of an example electronic device for implementing the data processing method of an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 9, the apparatus 900 includes a computing unit 901, which can perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)902 or a computer program loaded from a storage unit 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The calculation unit 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
A number of components in the device 900 are connected to the I/O interface 905, including: an input unit 906 such as a keyboard, a mouse, and the like; an output unit 907 such as various types of displays, speakers, and the like; a storage unit 908 such as a magnetic disk, optical disk, or the like; and a communication unit 909 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 909 allows the device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 901 performs the respective methods and processes described above, such as a data processing method. For example, in some embodiments, the data processing method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 900 via ROM 902 and/or communications unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the data processing method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), the Internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (17)
1. A method of data processing, comprising:
acquiring data to be processed;
determining a target operator type in a data processing model corresponding to the data to be processed;
acquiring target operator resources described by the target operator type; and
and processing the data to be processed by adopting the target operator resource to obtain a data processing result.
2. The method of claim 1, wherein said obtaining the target operator resource described by the target operator type comprises:
determining a target operator corresponding to the target operator type;
acquiring process level resources and thread level resources related to the target operator; and
taking the process-level resource and the thread-level resource together as the target operator resource;
the process-level resource is a global operation resource involved by the target operator in the process of processing the data to be processed, and the thread-level resource is a local operation resource involved by the target operator in the process of processing the data to be processed.
3. The method of claim 2, wherein said obtaining process-level resources and thread-level resources associated with the target operator comprises:
determining a target calling mode related to the target operator;
and acquiring process level resources and thread level resources related to the target operator according to the target calling mode.
4. The method of claim 3, wherein said determining a target invocation pattern associated with said target operator comprises:
determining a service calling mode between the target operator and a third-party service, wherein the service calling mode is used as the target calling mode; and/or
Determining resource calling modes of various resources in the target operator, wherein the resource calling modes are used as the target calling modes, and the resources are the process-level resources or the thread-level resources.
5. The method of claim 4, the number of target operators being a plurality;
wherein the determining a target calling mode related to the target operator further comprises:
and determining an inter-operator calling mode between the target operator and other target operators, wherein the inter-operator calling mode is used as the target calling mode, and the other target operators belong to a plurality of target operators.
6. The method of claim 3, wherein the obtaining process-level resources and thread-level resources associated with the target operator according to the target invocation manner comprises:
providing the target calling mode to a calling agent device;
and acquiring the process level resources and the thread level resources related to the target operator by the calling agent device according to the target calling mode.
7. The method of claim 2, further comprising, after said processing said data to be processed with said target operator resource to obtain a data processing result:
and emptying the thread-level resources related to the target operator.
8. A data processing apparatus comprising:
the first acquisition module is used for acquiring data to be processed;
the determining module is used for determining a target operator type in a data processing model corresponding to the data to be processed;
the second acquisition module is used for acquiring the target operator resource described by the target operator type; and
and the first processing module is used for processing the data to be processed by adopting the target operator resource so as to obtain a data processing result.
9. The apparatus of claim 8, wherein the second obtaining means comprises:
the determining submodule is used for determining a target operator corresponding to the target operator type;
the acquisition submodule is used for acquiring process level resources and thread level resources related to the target operator; and
the processing submodule is used for taking the process-level resource and the thread-level resource as the target operator resource together; the process-level resource is a global operation resource involved by the target operator in the process of processing the data to be processed, and the thread-level resource is a local operation resource involved by the target operator in the process of processing the data to be processed.
10. The apparatus of claim 9, wherein the acquisition submodule comprises:
the determining unit is used for determining a target calling mode related to the target operator;
and the acquisition unit is used for acquiring the process level resources and the thread level resources related to the target operator according to the target calling mode.
11. The apparatus according to claim 10, wherein the determining unit is specifically configured to:
determining a service calling mode between the target operator and a third-party service, wherein the service calling mode is used as the target calling mode; and/or
Determining resource calling modes of various resources in the target operator, wherein the resource calling modes are used as the target calling modes, and the resources are the process-level resources or the thread-level resources.
12. The apparatus of claim 11, the number of target operators is plural;
wherein, the determining unit is specifically configured to:
and determining an inter-operator calling mode between the target operator and other target operators, wherein the inter-operator calling mode is used as the target calling mode, and the other target operators belong to a plurality of target operators.
13. The apparatus according to claim 10, wherein the obtaining unit is specifically configured to:
providing the target calling mode to a calling agent device;
and acquiring the process level resources and the thread level resources related to the target operator by the calling agent device according to the target calling mode.
14. The apparatus of claim 9, further comprising:
and the second processing module is used for emptying the thread-level resources related to the target operator after the target operator resources are adopted to process the data to be processed so as to obtain a data processing result.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
17. A computer program product comprising a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115202851A (en) * | 2022-09-13 | 2022-10-18 | 创新奇智(浙江)科技有限公司 | Data task execution system and data task execution method |
| WO2023241616A1 (en) * | 2022-06-14 | 2023-12-21 | 华为云计算技术有限公司 | Data processing method based on cloud management platform, and cloud management platform |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626430A (en) * | 2019-04-18 | 2020-09-04 | 中科寒武纪科技股份有限公司 | Data processing method and related product |
| CN111966485A (en) * | 2020-06-30 | 2020-11-20 | 北京百度网讯科技有限公司 | Computing resource scheduling method and device, electronic equipment and storage medium |
| CN112199196A (en) * | 2020-10-21 | 2021-01-08 | 上海交通大学 | Resource allocation method, medium and server |
| CN112631754A (en) * | 2020-12-29 | 2021-04-09 | 浙江大华技术股份有限公司 | Data processing method, data processing device, storage medium and electronic device |
| CN112988873A (en) * | 2021-04-01 | 2021-06-18 | 陈之衎 | Data processing method and device |
-
2021
- 2021-12-27 CN CN202111616324.4A patent/CN114490116B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626430A (en) * | 2019-04-18 | 2020-09-04 | 中科寒武纪科技股份有限公司 | Data processing method and related product |
| CN111966485A (en) * | 2020-06-30 | 2020-11-20 | 北京百度网讯科技有限公司 | Computing resource scheduling method and device, electronic equipment and storage medium |
| CN112199196A (en) * | 2020-10-21 | 2021-01-08 | 上海交通大学 | Resource allocation method, medium and server |
| CN112631754A (en) * | 2020-12-29 | 2021-04-09 | 浙江大华技术股份有限公司 | Data processing method, data processing device, storage medium and electronic device |
| CN112988873A (en) * | 2021-04-01 | 2021-06-18 | 陈之衎 | Data processing method and device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023241616A1 (en) * | 2022-06-14 | 2023-12-21 | 华为云计算技术有限公司 | Data processing method based on cloud management platform, and cloud management platform |
| CN115202851A (en) * | 2022-09-13 | 2022-10-18 | 创新奇智(浙江)科技有限公司 | Data task execution system and data task execution method |
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