Resource configuration methods, resource configuration system, electronic device and storage medium

Abstract

Disclosed in the present application are resource configuration methods, a resource configuration system, an electronic device and a storage medium. A resource configuration method comprises: acquiring configuration requirement information corresponding to a computing power service; on the basis of the configuration requirement information, performing resource configuration on a general-purpose server and a graphics card resource pool to obtain a configuration result, wherein the general-purpose server is obtained on the basis of processor resource configuration, and the graphics card resource pool comprises a plurality of graphics processing units that support a hot-swapping function; and on the basis of the configuration result, providing a first instance for the computing power service, wherein the first instance comprises a processor resource allocated by the general-purpose server and a processor resource allocated by the graphics card resource pool. The present application solves the technical problems in the related art of low resource utilization rate and poor flexibility of the entire unit caused by heterogeneous models being constrained by the fixed configuration of processor resources of the entire unit.

Description

[0001] Resource Configuration Method, System, Electronic Device, and Storage Medium Technical Field This application relates to the field of cloud computing technology, and more specifically, to a resource configuration method, system, electronic device, and storage medium. Background With the continuous development of cloud computing technology, heterogeneous instances are increasingly used in computing service products. However, the graphics processing unit (GPU) models used in traditional heterogeneous instances have high failure rates and long maintenance cycles for complete machine failures. Furthermore, traditional heterogeneous instances are limited by the fixed configuration of CPU and GPU in the overall machine configuration, making it difficult to meet the diverse CPU and GPU resource allocation requirements in current complex cloud scenarios. In other words, traditional heterogeneous instances correspond to a large variety of heterogeneous machine models, resulting in significant resource fragmentation and low heterogeneous resource utilization. Currently, no effective solutions have been proposed to address these issues. SUMMARY The present application provides a resource configuration method, system, electronic device, and storage medium to at least address the technical issues in related technologies of low resource utilization and poor overall machine flexibility caused by heterogeneous machine models being limited by the fixed configuration of overall machine processor resources. According to one aspect of an embodiment of the present application, a resource configuration method is provided, comprising: obtaining configuration requirement information corresponding to a computing power service; performing resource configuration on a general-purpose server and a graphics card resource pool based on the configuration requirement information to obtain a configuration result, wherein the general-purpose server is obtained based on a processor resource configuration, and the graphics card resource pool includes multiple graphics processors that support online plug-in / plug-out functionality; and providing a first instance for the computing power service based on the configuration result, wherein the first instance includes processor resources allocated by the general-purpose server and processor resources allocated by the graphics card resource pool. According to another aspect of an embodiment of the present application, a resource configuration method is further provided, comprising: obtaining a resource configuration request through a first application programming interface, wherein request data carried in the resource configuration request includes configuration requirement information corresponding to a computing power service; and returning a resource configuration response through a second application programming interface, wherein response data carried in the resource configuration response includes instance information of a first instance provided for the computing power service, the first instance being determined based on a configuration result, the configuration result being obtained by performing resource configuration on a general server and a graphics card resource pool according to the configuration requirement information, the general server being obtained based on processor resource configuration, the graphics card resource pool including multiple graphics processors supporting online plug-in / plug-out functionality, and the first instance including processor resources allocated by the general server and processor resources allocated by the graphics card resource pool.According to another aspect of an embodiment of the present application, a resource configuration method is further provided, comprising: obtaining a currently input resource configuration request, wherein request data carried in the resource configuration request includes: configuration requirement information corresponding to a computing power service; returning a resource configuration reply in response to the resource configuration request, wherein the information carried in the resource configuration reply includes: instance information of a first instance provided for the computing power service, the first instance being determined based on a configuration result, the configuration result being obtained by performing resource configuration on a general server and a graphics card resource pool according to the configuration requirement information, the general server being obtained based on processor resource configuration, the graphics card resource pool including multiple graphics processors supporting online plug-in / plug-out functionality, the first instance including processor resources allocated by the general server and processor resources allocated by the graphics card resource pool; and displaying the instance information in a graphical user interface. According to another aspect of an embodiment of the present application, a resource configuration method is also provided, including: obtaining configuration requirement information corresponding to a computing power service; using the configuration requirement information and a resource configuration model to perform resource configuration on a general-purpose server and a graphics card resource pool to obtain a configuration result, wherein the resource configuration model is a neural network model pre-trained through machine learning using multiple sets of training data, the general-purpose server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online plug-in and unplug functionality; and providing a first instance for the computing power service based on the configuration result, wherein the first instance includes processor resources allocated to the general-purpose server and processor resources allocated to the graphics card resource pool. According to another embodiment of the present application, a resource configuration system is provided, comprising: a general server configured based on processor resources, memory resources, and service manager resources; a graphics card resource pool comprising multiple graphics processors supporting online plug-in / unplug functionality; and a graphics card interface box connected to the general server and the graphics card resource pool, configured to perform resource configuration on the general server and the graphics card resource pool based on configuration requirement information corresponding to a computing service, obtaining a configuration result. The configuration result is used to support the general server in providing a first instance for the computing service, the first instance comprising processor resources allocated by the general server and processor resources allocated by the graphics card resource pool. According to another embodiment of the present application, an electronic device is provided, comprising: a memory storing an executable program; and a processor configured to execute the program, wherein, when the program is executed, any one of the aforementioned resource configuration methods is executed. According to another embodiment of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein, when the program is executed, the device containing the computer-readable storage medium is controlled to execute any one of the aforementioned resource configuration methods.According to another aspect of an embodiment of the present application, a computer program product is provided, comprising a computer program. When executed by a processor, the computer program implements any of the resource configuration methods described above. In this embodiment of the present application, configuration requirement information corresponding to a computing power service is obtained; further, resource configuration is performed on a general server and a graphics card resource pool based on the configuration requirement information to obtain a configuration result, wherein the general server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online plug-and-unplug functionality; and based on the configuration result, a first instance is provided for the computing power service, wherein the first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool. Thus, the present application achieves the goal of flexibly configuring processor resources based on configuration requirements, thereby achieving the technical effect of improving resource utilization and overall machine flexibility in heterogeneous machine models in elastic computing scenarios. This further addresses the technical problems in related arts of low resource utilization and poor overall machine flexibility caused by heterogeneous machine models being limited by fixed configurations of overall machine processor resources. It should be noted that the general description above and the detailed description below are merely examples and explanations of the present application and do not constitute limitations on the present application. BRIEF DESCRIPTION OF THE DRAWINGS The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the accompanying drawings: Figure 1 shows a hardware structure block diagram of a computer terminal (or mobile device) configured to implement a resource configuration method; Figure 2 is a flow chart of a resource configuration method according to Example 1 of the present application; Figure 3 is a schematic diagram of a computing node in an elastic computing system according to the prior art; Figure 4 is a schematic diagram of an optional elastic computing system according to Example 1 of the present application; Figure 5 is a flow chart of a resource configuration method according to Example 2 of the present application; Figure 6 is a flow chart of a resource configuration method according to Example 3 of the present application; Figure 7 is a flow chart of a resource configuration method according to Example 4 of the present application; Figure 8 is a structural schematic diagram of a resource configuration system according to Example 5 of the present application; Figure 9 is a structural schematic diagram of a resource configuration device according to Example 6 of the present application; Figure 10 is a structural schematic diagram of another resource configuration device according to Example 6 of the present application; Figure 11 is a structural schematic diagram of another resource configuration device according to Example 6 of the present application; Figure 12 is a structural block diagram of an electronic device according to Example 7 of the present application.DETAILED DESCRIPTION To help those skilled in the art better understand the present invention, the technical solutions in the embodiments of the present invention will be described clearly and completely below, in conjunction with the accompanying drawings. It should be noted that the described embodiments represent only a portion of the embodiments of the present invention, and are not exhaustive. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention. It should be noted that the terms "first," "second," and so on, in the specification and claims of the present application, and in the accompanying drawings, are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that such terms are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not necessarily limited to the steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, product, or apparatus. First, some nouns and terms used in describing the embodiments of this application are explained as follows: Graphics Processing Unit Box (GPU BOX): This typically contains multiple GPU cards. Connecting a GPU BOX to a computer provides graphics processing capabilities, for example, accelerating the computer's graphics processing, data processing, or deep learning tasks. The GPU BOX can be connected to one or more general-purpose computing servers as heads via high-speed data transmission interface cables (e.g., PCIe cables) to provide GPU instances.

[0002] A GPU instance is a virtual server instance provided in a cloud computing environment that is equipped with a graphics processing unit (GPU) specifically designed to accelerate graphics processing and scientific computing tasks. GPU instances are typically designed for workloads requiring extensive parallel computing, such as machine learning, deep learning, data analytics, and scientific computing. Because GPUs offer significant advantages over traditional central processing units (CPUs) in parallel computing, they can deliver higher performance and efficiency when processing workloads in these areas. Therefore, many cloud computing service providers offer GPU instances as part of their cloud computing services to meet user needs for high-performance and parallel computing. A Peripheral Component Interconnect Express (PCIE) switch is a device designed to provide a high-speed data transmission interface connecting various hardware devices within a computer. For example, a PCIE switch is designed to enable switching between the CPU and PCIE devices or to expand ports. PCIE SWITCH features multi-host implementation (also known as MultiHost, meaning a network service or platform can simultaneously host multiple hosts or websites), port fault identification and isolation, and flexible port allocation. An instance refers to a set of computing resources sold by elastic computing products, consisting of a combination of CPU, memory, network, GPU, or storage. For example, instances can include virtual machine instances, container instances, and GPU instances. A Mobile Operations Center (MOC) is configured to implement virtualization functions related to input / output, computing, and so on in cloud computing. In this application, an MOC may refer to a Smart Network Card. Example 1: According to an embodiment of the present application, an embodiment of a resource configuration method is also provided. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system, such as a set of computer-executable instructions. Although the flowcharts illustrate a logical order, in some cases, the steps shown or described may be executed in a different order. The method embodiment provided in Example 1 of the present application can be executed in a mobile terminal, a computer terminal, or a similar computing device. FIG1 shows a hardware structure block diagram of a computer terminal (or mobile device) configured to implement a resource configuration method.As shown in FIG1 , a computer terminal 10 (or mobile device 10) may include one or more processors 102 (illustrated as 102a, 102b, 102n in the figure) (the processor 102 may include, but is not limited to, a processing device such as a microcontroller unit (MCU) or a field programmable gate array (FPGA)), a memory 104 configured to store data, and a transmission device 106 configured to perform communication functions. In addition, the computer terminal 10 may also include: a display, an input / output interface (I / O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of a computer bus), a network interface, a cursor control device (such as a mouse, a touchpad, etc.), a keyboard, a power supply, and / or a camera. Those skilled in the art will appreciate that the structure shown in FIG1 is merely illustrative and does not limit the structure of the electronic device described above. For example, the computer terminal 10 may include more or fewer components than shown in FIG1 , or have a configuration different from that shown in FIG1 . It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." This data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be fully or partially integrated into any of the other components of the computer terminal 10 (or mobile device). As described in the embodiments of this application, this data processing circuitry functions as a processor control (e.g., selecting a path for a variable resistor terminal connected to an interface). The memory 104 may be configured to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the resource configuration method described in the embodiments of this application. The processor 102 executes the software programs and modules stored in the memory 104 to execute various functional applications and data processing, thereby implementing the resource configuration method described above. The memory 104 may include high-speed random access memory (RAM) or non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memories remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the aforementioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.The transmission device 106 is configured to connect to a network via a network interface to receive or transmit data. Specific examples of the aforementioned network may include a wired and / or wireless network provided by the communications provider of the computer terminal 10. In one example, the transmission device 106 includes a network interface controller (NIC), which can connect to other network devices via a base station to enable communication with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module configured to communicate with the Internet wirelessly. The display shown in FIG1 may be, for example, a touchscreen liquid crystal display (LCD), which enables a user to interact with the user interface of the computer terminal 10 (or mobile device). It should be noted that, in some optional embodiments, the computer device (or mobile device) shown in FIG1 may include hardware components (including circuitry), software components (including computer code stored on a computer-readable medium), or a combination of both hardware and software components. It should be noted that FIG1 is merely an example of a specific embodiment and is intended to illustrate the types of components that may be present in the aforementioned computer device (or mobile device). In the above-mentioned operating environment, the present application provides a resource configuration method as shown in Figure 2. Figure 2 is a flowchart of a resource configuration method according to Example 1 of the present application. As shown in Figure 2, the resource configuration method includes: Step S21, obtaining configuration requirement information corresponding to a computing power service; Step S22, performing resource configuration on a general-purpose server and a graphics card resource pool based on the configuration requirement information to obtain a configuration result, wherein the general-purpose server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online plug-and-play functionality; Step S23, providing a first instance for the computing power service based on the configuration result, wherein the first instance includes processor resources allocated to the general-purpose server and processor resources allocated to the graphics card resource pool. The computing power service described above is a computing resource service in cloud computing that dynamically scales based on computing scenario requirements. The computing power service allows users to automatically increase or decrease computing resources based on actual needs, and even change the ratio of different resource types to meet different workloads and business requirements. The flexible computing resource allocation method in the computing power service can improve computing resource utilization, reduce costs, and provide faster response and higher availability. In the application scenario, the above computing power service can be an elastic computing service.In this scenario, the resource configuration method provided in the embodiments of this application can dynamically adjust the scale and performance of computing resources based on user needs, thereby providing flexible and adaptable computing instance resources for elastic computing services. Specifically, the method provided in the embodiments of this application enables flexible selection of central processing unit (CPU) and graphics processing unit (GPU) resources to be allocated when configuring resources for elastic computing services. The resource configuration method corresponding to the computing power service in the embodiments of this application can be applied to assist in implementing scientific computing tasks, machine learning tasks (such as training deep learning models, image recognition, natural language processing, etc.), cloud gaming tasks (such as providing high-performance game graphics rendering and real-time interaction), big data analysis tasks, and other tasks in pre-defined application scenarios. These pre-defined application scenarios include, but are not limited to, cloud computing scenarios in fields such as e-commerce, education, healthcare, conferencing, social networking, financial products, logistics, and navigation. The configuration requirement information can be determined by user input data or by the computing power service and pre-defined configuration rules. The configuration requirement information can represent the quantity, type, and specifications of the processor resources required by the computing power service. Based on the configuration requirement information, resource configuration for the general-purpose server and graphics card resource pool may be implemented by selecting, combining, and packaging resources available on the general-purpose server and the graphics processor resources available on the graphics card resource pool, thereby obtaining the configuration result. The configuration result is used to determine at least the processor resources to be used when providing computing instances for the computing service. The configuration result may also be used to determine the memory resources, service manager resources, and other resources to be used when providing computing instances for the computing service. For example, the configuration result may include packaging of the processor resources, packaging of the processor resources with memory resources and service manager resources, or resource identifiers for the processor resources, memory resources, or service manager resources. It should be noted that the processor resources configured for the general-purpose server and the processor resources configured for the graphics card resource pool may be different types. For example, the processor resources configured in the general-purpose server may be central processing unit resources, while the processor resources in the graphics card resource pool may be graphics processor resources. In particular, the multiple graphics processors in the graphics card resource pool may support online pluggable functionality. Furthermore, the first instance provided for the computing service based on the configuration result can be a GPU instance, which can include CPU resources allocated by the general server and GPU resources allocated by the graphics card resource pool. In particular, the GPU resources allocated by the graphics card resource pool can be single GPU card resources, 2 GPU card resources, 4 GPU card resources, 8 GPU card resources, etc.Thus, the present application configures resources for a general-purpose server and a graphics card resource pool based on configuration requirement information and provides a first instance based on the configuration result. This enables reuse of processor resources in the general-purpose server and flexible configuration of multiple graphics processors in the graphics card resource pool (for example, flexibly selecting at least one graphics processor to be mounted on the general-purpose server to provide a GPU instance). This results in a rich variety of configuration results to meet the diverse needs of computing services in different scenarios. In an embodiment of the present application, configuration requirement information corresponding to a computing service is obtained; resources are further configured for the general-purpose server and the graphics card resource pool based on the configuration requirement information to obtain a configuration result. The general-purpose server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online pluggable functionality. Based on the configuration result, a first instance is provided for the computing service. The first instance includes processor resources allocated to the general-purpose server and processor resources allocated to the graphics card resource pool. As a result, the present application achieves the goal of flexibly configuring processor resources based on configuration requirements, thereby achieving the technical effect of improving resource utilization and overall machine flexibility in heterogeneous machines in elastic computing scenarios. This further addresses the technical issues in related technologies such as low resource utilization and poor overall machine flexibility caused by the fixed configuration of processor resources in heterogeneous machines. In this application scenario, several compute nodes (CNs) provided by existing elastic computing systems are shown in Figure 3. In Figure 3, node 1 is a CPU server supporting elastic computing CPU instances, node 2 is a single-GPU server supporting elastic computing GPU instances, node 3 is a 4-GPU server supporting elastic computing GPU instances (i.e., four GPU cards mounted on the CPU), and node 4 is an 8-GPU server supporting elastic computing GPU instances (i.e., eight GPU cards mounted on the CPU). In the overall machine topology of the above elastic computing system, compared to CPU servers, GPU servers, 4-GPU servers, or 8-GPU servers have additional GPU cards. However, due to the higher power consumption of GPU cards and the complex structure and heat dissipation design of GPU servers, the failure rate of GPU servers is much higher than that of CPU servers. In addition, existing GPU servers typically use the current standard PCIE card form factor and do not support online maintenance. If any component in the entire machine (such as the smart network card, memory, CPU, GPU, etc.) fails, the entire machine needs to be taken offline for repair, which results in poor resource availability of the GPU server.Furthermore, different cloud service instances have significantly different requirements for CPU and GPU configuration ratios. For example, search and recommendation scenarios have higher CPU requirements, while large-model inference scenarios require higher GPU computing power or memory. Traditional AI applications (such as image recognition) have relatively balanced CPU and GPU requirements. This requires cloud-based elastic computing systems to flexibly configure CPU and GPU resources to meet the resource requirements of different scenarios. Considering the combination of multiple CPU platforms and multiple GPUs significantly increases the variety of GPU instance models (typically calculated by multiplying the number of CPU platforms, GPU models, and GPU cards), significantly increasing the difficulty of developing and operating cloud-based elastic computing systems. To address this issue, based on the existing elastic computing systems described above, an elastic computing system architecture as shown in FIG4 is provided according to embodiments of the present application. This architecture includes multiple general-purpose servers, switches, and a graphics processor resource pool (also known as a graphics card resource pool). It should be noted that in the elastic computing system architecture provided in embodiments of the present application, the general-purpose servers are configured to reuse some of the models corresponding to the CPU instances in the computing power service, and the general-purpose servers have reserved PCIE interface expansion capabilities. In particular, in embodiments of the present application, multiple general-purpose servers in the elastic computing system can be of the same server type, configured with the same CPU platform, memory, and service manager (in this example, a SmartNIC MOC), or they can be of different server types, configured with different CPU platforms or different CPU models and memory and service managers. Thus, the elastic computing system provided by embodiments of the present application can implement the functionality of all machine types (i.e., the number of CPU platforms, GPU models, and GPU cards multiplied together) in a traditional elastic computing system, thereby enhancing resource availability. It is readily apparent that the elastic computing system architecture provided by embodiments of the present application decouples different types of processor resources at the hardware level. For example, the central processing unit (CPU) is configured in a general-purpose server, and the graphics processor (GPU) is configured in a graphics processor resource pool. The general-purpose server and the graphics processor resource pool are connected via a data transmission switch with multi-host functionality (e.g., a PCIE switch). Consequently, the present application not only enables flexible configuration of different types of processor resources based on configuration requirements, but also enables online fault identification and repair of processor resources through the software functions of the data transmission switch, thereby improving the overall resource availability of the elastic computing system.In an optional embodiment, in step S21, obtaining configuration requirement information corresponding to a computing power service includes the following method steps: Step S211: Obtaining a computing task category corresponding to the computing power service; Step S212: Generating configuration requirement information based on the computing task category, wherein the configuration requirement information is used to indicate the proportions of different types of processor resources to be configured for the computing power service. In this optional embodiment, the computing task categories corresponding to the computing power service may be first, second, and third categories. The configuration requirement information corresponding to the first category indicates that, among the processor resources to be configured, CPU resources exceed GPU resources (or the ratio of CPU resources to GPU resources exceeds a first threshold), the configuration requirement information corresponding to the second category indicates that, among the processor resources to be configured, CPU resources exceed GPU resources (or the ratio of CPU resources to GPU resources falls below a second threshold), and the configuration requirement information corresponding to the third category indicates that, among the processor resources to be configured, CPU resources are relatively balanced with GPU resources (or the ratio of CPU resources to GPU resources falls within a specified numerical range). For example, search and recommendation scenarios have high CPU requirements, so elastic computing tasks in these scenarios typically fall into the first category mentioned above. Large model reasoning scenarios have high GPU computing power or memory requirements, so elastic computing tasks in these scenarios typically fall into the second category mentioned above. Traditional artificial intelligence (such as image recognition) has relatively balanced CPU and GPU requirements, so elastic computing tasks in traditional artificial intelligence scenarios typically fall into the third category mentioned above. In application scenarios, the configuration requirement information can be generated based on preset configuration rules and the computing task types. The preset configuration rules are used to determine the proportion of resources to be configured for each computing task type. The process of automatically generating configuration requirement information based on the computing task type corresponding to the computing service and then determining the proportion of resources to be configured for different types of processor resources can be implemented by computer code in the internal chip of the PCIE switch (i.e., the switch). Therefore, through the above method steps, the embodiments of the present application can adapt to the resource configuration requirements corresponding to the computing service in real time and perform subsequent resource configuration steps, providing high flexibility and increasing resource availability for the entire elastic computing system.In an optional embodiment, in step S22, resource configuration is performed on the general server and graphics card resource pool based on the configuration requirement information to obtain a configuration result. The method includes the following steps: Step S221: Determining, based on the configuration requirement information, at least one target graphics processor to be mounted to the general server from multiple graphics processors in the graphics card resource pool; Step S222: Combining and packaging resources on the general server and the target graphics processor to obtain a configuration result. As shown in FIG4 , in the elastic computing system provided in this embodiment of the present application, the graphics card resource pool includes multiple graphics processors (i.e., pluggable GPU cards). Based on the configuration requirement information, the type, quantity, and specifications of the processor resources required for the instance of the current computing task configured for the computing service can be determined. Based on this, at least one GPU card to be mounted to the general server is selected from the multiple GPU cards in the graphics card resource pool based on the configuration requirement information. For example, a current computing task of a computing service requires a 4-GPU server. After the computing task is connected to general server 1, it is uploaded by general server 1 to the switch. Based on the configuration requirement information corresponding to the computing task, the software module in the switch selects four GPU cards (i.e., the target GPU) from the graphics card resource pool to be mounted on general server 1. This selection may be performed by determining the identifiers of the four GPU cards. Furthermore, resource combination and packaging processing is performed on general server 1 and the four GPU cards to obtain a configuration result. This configuration result is used to provide a GPU instance (i.e., a first instance) for the computing task. The computing resource corresponding to this GPU instance is a 4-GPU server. In an optional embodiment, in step S222, resource combination and packaging processing is performed on the general server and the target GPU to obtain a configuration result. The method includes the following steps: Step S2221: Sending a first plug-in / plug-out instruction to the target GPU, wherein the first plug-in / plug-out instruction is used to control the target GPU to adjust its plug-in / plug-out state so that it can be mounted on the general server; Step S2222: Packaging processing is performed on the general server and the target GPU to obtain a configuration result. As shown in FIG4 , the method steps described above in the embodiment of the present application can be implemented by a computer program in a built-in chip of a switch. In an application scenario, the multiple graphics processors in the graphics resource pool are notification-based pluggable GPU cards. During execution of the program in the built-in chip of the PCIE switch, after determining at least one target graphics processor to be mounted to a general-purpose server, a first plug-in / plug-out instruction is generated and sent to each of the at least one target graphics processors. In response to the first plug-in / plug-out instruction, each target graphics processor adjusts its plug-in / plug-out state to be mounted to the general-purpose server.Furthermore, the general server and at least one target graphics processor are packaged according to a preset target packaging format to obtain a configuration result. In an optional embodiment, the resource configuration method further includes the following method steps: Step S24: Providing a second instance for the computing service based on the configuration requirement information, wherein the second instance includes processor resources provided by the general server. In an application scenario, if the configuration requirement information determines that the current computing task of the computing service only requires a CPU instance and does not require a GPU card, a second instance (i.e., a CPU instance) is provided for the computing service based on the general server. The second instance may include CPU resources, memory resources, or service manager resources allocated by the general server. In an optional embodiment, the resource configuration method further includes the following method steps: Step S251: Performing fault identification on the general server and multiple graphics processors in the graphics resource pool to obtain a fault identification result, wherein the fault identification result is used to determine the faulty component; Step S252: Remediating the faulty component based on the fault identification result. In the above-described optional embodiment, since the general server and graphics card resource pool are decoupled at the hardware level, upon identifying a fault in the CPU resources of a general server or a GPU card in the graphics card resource pool, there is no need to remove the entire server for repair. Instead, the software system in the switch's built-in chip can isolate, replace, and reallocate the faulty resource. Specifically, real-time fault identification is performed on the general server and graphics card resource pool to obtain a fault identification result. The fault identification result may indicate that the entire server is fault-free or include the component identifier of a faulty component. The faulty component may be the CPU, memory, or service manager component (MOC) in the general server, or the GPU card in the graphics card resource pool. The fault repair tool (e.g., pre-programmed fault repair code) in the switch's built-in chip then completes the fault repair process for the faulty component based on the fault identification result. In an optional embodiment, in step S252, based on the fault identification result, the fault repair is performed on the fault component, including the following method steps: Step S2521, based on the fault identification result, determining a replacement component corresponding to the faulty component from the general server and graphics card resource pool, wherein the replacement component is an available component of the same category as the faulty component and has not experienced a fault; Step S2522, sending a second plug-in instruction to the faulty component and the replacement component, wherein the second plug-in instruction is used to control the offline isolation of the faulty component and control the replacement component to adjust the plug-in state so that it can be mounted on the general server.Through the above method steps, a faulty component (such as a CPU component or GPU card) in a processor resource packaged as an instance can be replaced with a usable component of the same category as the faulty component and that has not experienced a fault. The usable component can be a component in an available state, which is the state of a component not packaged into an instance. In an exemplary application scenario, based on the elastic computing system shown in FIG4 , an instance 1 is provided, consisting of a general-purpose server 1 and GPU 0. During fault identification of the general-purpose server and graphics card resource pool, it is discovered that general-purpose server 1 has experienced a CPU, memory, or SmartNIC fault, while GPU 0 has not experienced a fault. Based on a preset fault isolation strategy, general-purpose server 1 is isolated and taken offline, and a replacement and maintenance process is initiated (e.g., a technician is notified for repair or a replacement is pending). Based on a preset resource reallocation strategy, GPU 0 is quickly mounted to general-purpose server 2 (assuming that general-purpose server 2 has the same configuration category as general-purpose server 1 and is in an available state) to provide the above-described instance 1. This improves the uptime of GPU 0, thereby increasing the availability of the GPU resources. In another exemplary application scenario, based on the elastic computing system shown in FIG4 , an instance 2 consisting of a general-purpose server 4 and a GPU 7 is provided. During fault identification of the general-purpose server and graphics card resource pool, if GPU 7 fails but general-purpose server 4 does not, based on a preset fault isolation policy, GPU 7 is marked as faulty and a maintenance process is initiated (e.g., notifying a technician for repair or waiting for replacement). Based on a preset resource reallocation policy, a replacement component, such as GPU 6, is identified from unallocated GPU cards in the graphics card resource pool. GPU 6 is then mounted and mapped to general-purpose server 4 to form instance 2. Specifically, if no unallocated GPU cards exist in the graphics card resource pool when GPU 7 is detected as faulty, a new CPU instance can be provisioned based on general-purpose server 4 to improve resource availability for general-purpose server 4.It should be noted that, depending on the application scenario, general-purpose server 1 can be combined with GPU 0 to form a single-GPU instance, designed to meet the computational processing requirements of the more complex service logic in search and recommendation scenarios. General-purpose server 1 can be combined with GPU 1 and GPU 2 to form a dual-GPU instance, suitable for traditional AI scenarios such as image recognition where CPU and GPU resource requirements are relatively balanced. General-purpose server 2 can be combined with GPU 4, GPU 5, GPU 6, and GPU 7 to form a four-GPU instance to meet the computational processing requirements of large-model inference scenarios. General-purpose server 3 can provide a CPU instance independently or form a GPU instance with all GPU cards in the graphics resource pool. In these scenarios, fault identification and repair can be performed for each component in each instance. It should be noted that in the application scenario, each general-purpose server can be an independent entity, configured with different CPU platforms and different memory specifications (such as different capacities and speeds). Each general-purpose server can be interconnected with a switch (in this example, a PCIE switch) via PCIE cables. Each general-purpose server can be a standalone CPU instance or combined with GPU cards to provide a GPU instance. Furthermore, the aforementioned switch and graphics processor resource pool can also be configured as a GPU BOX model. That is, in addition to providing power, cooling, and structural support for multiple GPU cards, the switch (in this example, a PCIE switch) serves as a key component within the GPU BOX. The switch's uplink port features multi-host functionality to support PCIE interface access from multiple general-purpose servers. The switch's downlink port can access a graphics resource pool, which can be of the same manufacturer and model, or from different manufacturers and models. The switch's built-in chip can be a commercial device or a proprietary chip (for example, a design compatible with some logic from a proprietary intelligent chip in a cloud service platform). For example, a typical configuration for the aforementioned GPU BOX model might include four general-purpose servers as the server head, a GPU BOX with a PCIE switch as the base for the GPU resources, and eight GPU cards with online swappable functionality. In the application scenario, the number of the above-mentioned general-purpose servers and the number of GPU cards can be flexibly adjusted according to the specifications and performance of the PCIE switch and GPU BOX and the requirements of the cloud application scenario.As described above, this application achieves real-time fault identification, online fault repair, and online resource reallocation of processor resources by decoupling CPU and GPU resources at the hardware level. It also improves the configuration flexibility and resource availability of the entire machine's CPU and GPU resources. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, storage, and display) involved in this application are all authorized by the user or fully authorized by all parties. The collection, use, and processing of relevant data must comply with relevant laws, regulations, and standards of relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or reject. It should be noted that for the sake of simplicity, the aforementioned method embodiments are described as a series of actions. However, those skilled in the art should be aware that this application is not limited by the order of the actions described, as certain steps can be performed in a different order or simultaneously according to this application. Furthermore, those skilled in the art should also be aware that the embodiments described in this specification are preferred embodiments, and the actions and modules involved are not necessarily required for this application. Through the above description of the embodiments, those skilled in the art will clearly understand that the methods according to the above embodiments can be implemented using software and a necessary general-purpose hardware platform, or alternatively, hardware. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, computer, server, or network device) to execute the methods described in each embodiment of this application. Example 2: In the operating environment described in Example 1, this application provides another resource configuration method as shown in Figure 5.FIG5 is a flowchart of a resource configuration method according to Example 2 of the present application. As shown in FIG5 , the resource configuration method includes: Step S51, obtaining a resource configuration request via a first application programming interface, wherein the request data carried in the resource configuration request includes configuration requirement information corresponding to a computing power service; Step S52, returning a resource configuration response via a second application programming interface, wherein the response data carried in the resource configuration response includes instance information of a first instance provided for the computing power service, wherein the first instance is determined based on a configuration result, which is obtained by configuring resources for a general server and a graphics card resource pool based on the configuration requirement information, wherein the general server is configured based on processor resources, the graphics card resource pool includes multiple graphics processors that support online pluggable functionality, and the first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool. Based on the above method steps, a method for implementing a resource configuration service is provided, which runs on a cloud server. The cloud server obtains a resource configuration request from a service caller via a first application programming interface (API). Based on the request data carried in the resource configuration request, it executes a resource configuration process, thereby obtaining instance information of a first instance corresponding to the configuration result. Furthermore, the cloud server returns a resource configuration response to the service caller via a second application programming interface, providing the instance information of the first instance corresponding to the configuration result to the service caller. The resource configuration method for the computing power service described in the embodiments of the present application can be applied to assist in implementing scientific computing tasks, machine learning tasks (such as training deep learning models, image recognition, natural language processing, etc.), cloud gaming tasks (such as providing high-performance game graphics rendering and real-time interaction), big data analysis tasks, and other tasks in predefined application scenarios. These predefined application scenarios include, but are not limited to, cloud computing scenarios in fields such as e-commerce, education, healthcare, conferencing, social networking, financial products, logistics, and navigation. In these application scenarios, the computing power service can be an elastic computing service. In this scenario, the resource configuration method provided in the embodiments of the present application can dynamically adjust the scale and performance of computing resources based on user needs, thereby providing flexible and variable computing instance resources for the elastic computing service. In particular, the method provided in embodiments of the present application enables flexible selection of central processing unit (CPU) and graphics processing unit (GPU) resources to be allocated when configuring resources for elastic computing services. This configuration requirement information can be determined by user input or by the computing service and pre-set configuration rules. This configuration requirement information can indicate the quantity, type, and specifications of the processor resources required by the computing service.Based on the configuration requirement information, resource configuration for the general-purpose server and graphics card resource pool may be implemented by selecting, combining, and packaging resources available on the general-purpose server and the graphics processor resources available on the graphics card resource pool, thereby obtaining the configuration result. The configuration result is used to determine at least the processor resources to be used when providing computing instances for the computing service. The configuration result may also be used to determine the memory resources, service manager resources, and other resources to be used when providing computing instances for the computing service. For example, the configuration result may include packaging of the processor resources, packaging of the processor resources with memory resources and service manager resources, or resource identifiers for the processor resources, memory resources, or service manager resources. It should be noted that the processor resources configured for the general-purpose server and the processor resources configured for the graphics card resource pool may be different types. For example, the processor resources configured in the general-purpose server may be central processing unit resources, while the processor resources in the graphics card resource pool may be graphics processor resources. In particular, the multiple graphics processors in the graphics card resource pool may support online pluggable functionality. Furthermore, the first instance provided for the computing power service based on the configuration result can be a GPU instance. This GPU instance can include CPU resources allocated by the general server and GPU resources allocated by the graphics card resource pool. In particular, the GPU resources allocated by the graphics card resource pool can include single GPU card resources, dual GPU card resources, quad GPU card resources, or eight GPU card resources. Thus, this application configures resources for the general server and the graphics card resource pool based on configuration requirement information and provides the first instance based on the configuration result. This enables the reuse of processor resources in the general server and the flexible configuration of multiple graphics processors in the graphics card resource pool (for example, flexibly selecting at least one graphics processor to be mounted on the general server to provide a GPU instance). This results in a rich variety of configuration results to meet the diverse needs of computing power services in different scenarios. In an embodiment of the present application, a resource configuration request is obtained through a first application programming interface, wherein the request data carried in the resource configuration request includes: configuration requirement information corresponding to the computing power service; and a resource configuration response is returned through a second application programming interface, wherein the response data carried in the resource configuration response includes: instance information of a first instance provided for the computing power service, the first instance being determined based on a configuration result, and the configuration result being obtained by performing resource configuration on a general server and a graphics card resource pool according to the configuration requirement information, the general server being obtained based on processor resource configuration, the graphics card resource pool including multiple graphics processors supporting online plug-in functionality, and the first instance including processor resources allocated by the general server and processor resources allocated by the graphics card resource pool.As a result, this application achieves the goal of flexibly configuring processor resources based on configuration requirements, thereby achieving the technical effect of improving resource utilization and overall machine flexibility in heterogeneous machines in elastic computing scenarios. This further addresses the technical issues in related technologies such as low resource utilization and poor overall machine flexibility caused by heterogeneous machines being limited by the fixed configuration of overall machine processor resources. It should be noted that the preferred implementation of this embodiment can be found in the relevant description of Example 1 and will not be repeated here. Example 3: In the operating environment of Example 1, this application provides another resource configuration method as shown in Figure 6. FIG6 is a flowchart of a resource configuration method according to Example 3 of the present application. As shown in FIG6 , the resource configuration method includes: Step S61: Receiving a currently input resource configuration request, wherein the request data carried in the resource configuration request includes configuration requirement information corresponding to the computing power service; Step S62: Returning a resource configuration reply in response to the resource configuration request, wherein the resource configuration reply includes instance information of a first instance provided for the computing power service, wherein the first instance is determined based on a configuration result, which is obtained by configuring resources for a general server and a graphics card resource pool based on the configuration requirement information, wherein the general server is configured based on processor resources, the graphics card resource pool includes multiple graphics processors supporting online pluggable functionality, and the first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool; Step S63: Displaying the instance information in a graphical user interface. According to the above method steps, a visualization solution for resource configuration is provided. The terminal device provides a graphical user interface that displays at least one resource configuration scenario. The graphical user interface also includes input components (such as text input boxes and voice input controls) and display components (such as text display windows and image display windows). Users use the input components to input resource configuration requests, specifying configuration requirements for the computing power service. After detecting the user's input, the resource configuration process is executed based on the configuration requirements, obtaining instance information for the first instance corresponding to the configuration result. Furthermore, the instance information for the first instance corresponding to the configuration result is displayed via the display component within the graphical user interface.The resource configuration method corresponding to the computing power service described in the embodiments of the present application can be applied to assist in implementing scientific computing tasks, machine learning tasks (such as training deep learning models, image recognition, natural language processing, etc.), cloud gaming tasks (such as providing high-performance game graphics rendering and real-time interaction), big data analysis tasks, and other tasks in pre-defined application scenarios. These pre-defined application scenarios include, but are not limited to, cloud computing scenarios in fields such as e-commerce, education, healthcare, conferencing, social networks, financial products, logistics, and navigation. In this application scenario, the computing power service can be an elastic computing service. In this scenario, the resource configuration method provided in the embodiments of the present application can dynamically adjust the scale and performance of computing resources based on user needs, thereby providing flexible and variable computing instance resources for the elastic computing service. In particular, the method provided in the embodiments of the present application can flexibly select central processing unit (CPU) resources and graphics processing unit (GPU) resources to be allocated when configuring resources for the elastic computing service. The configuration requirement information can be determined by user input data or by the computing power service and pre-defined configuration rules. The configuration requirement information can represent the quantity, type, and specifications of the processor resources required by the computing service. Based on the configuration requirement information, resource configuration for the general-purpose server and graphics card resource pool can be implemented by selecting, combining, and packaging resources between the resources provided by the general-purpose server and the graphics processor resources provided by the graphics card resource pool, thereby obtaining the configuration result. The configuration result is used to determine at least the processor resources to be used when providing computing instances for the computing service. The configuration result can also be used to determine the memory resources, service manager resources, and other resources to be used when providing computing instances for the computing service. For example, the configuration result can include packaging of the processor resources, packaging of the processor resources with memory resources and service manager resources, or resource identifiers for the processor resources, memory resources, or service manager resources. It should be noted that the processor resources configured for the general-purpose server and the processor resources configured for the graphics card resource pool can be different types. For example, the processor resources configured in the general-purpose server can be central processing unit resources, while the processor resources in the graphics card resource pool can be graphics processor resources. In particular, the multiple graphics processors in the graphics card resource pool can support online pluggable functionality. Furthermore, the first instance provided for the computing service based on the configuration result can be a GPU instance, which can include CPU resources allocated by the general server and GPU resources allocated by the graphics card resource pool. In particular, the GPU resources allocated by the graphics card resource pool can be single GPU card resources, 2 GPU card resources, 4 GPU card resources, 8 GPU card resources, etc.Thus, the present application configures resources for a general-purpose server and a graphics card resource pool based on configuration requirement information and provides a first instance based on the configuration result. This enables reuse of processor resources in the general-purpose server and flexible configuration of multiple graphics processors in the graphics card resource pool (for example, flexibly selecting at least one graphics processor to be mounted on the general-purpose server to provide a GPU instance). This results in a rich and diverse configuration result to meet the diverse needs of computing services in different scenarios. In an embodiment of the present application, a currently input resource configuration request is obtained, wherein the request data carried in the resource configuration request includes configuration requirement information corresponding to the computing service. In response to the resource configuration request, a resource configuration reply is returned, wherein the resource configuration reply includes instance information of a first instance provided for the computing service. The first instance is determined based on the configuration result, which is obtained by configuring resources for the general-purpose server and the graphics card resource pool based on the configuration requirement information. The general-purpose server is configured based on processor resources, the graphics card resource pool includes multiple graphics processors that support online plugging and unplugging, and the first instance includes processor resources allocated by the general-purpose server and processor resources allocated by the graphics card resource pool. The instance information is then displayed in a graphical user interface. As a result, this application achieves the goal of flexibly configuring processor resources based on configuration requirements, thereby achieving the technical effect of improving resource utilization and overall machine flexibility in heterogeneous machines in elastic computing scenarios. This further addresses the technical issues in related technologies such as low resource utilization and poor overall machine flexibility caused by heterogeneous machines being limited by the fixed configuration of overall machine processor resources. It should be noted that the preferred implementation of this embodiment can be found in the relevant descriptions of Example 1 or Example 2 and will not be repeated here. Example 4: In the operating environment of Example 1, this application provides another resource configuration method as shown in Figure 7. FIG7 is a flowchart of a resource configuration method according to Example 4 of the present application. As shown in FIG7 , the resource configuration method includes: Step S71, obtaining configuration requirement information corresponding to a computing power service; Step S72, using the configuration requirement information and a resource configuration model, performing resource configuration on a general-purpose server and a graphics card resource pool to obtain a configuration result. The resource configuration model is a neural network model pre-trained through machine learning using multiple sets of training data. The general-purpose server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online plug-and-play functionality. Step S73, providing a first instance for the computing power service based on the configuration result. The first instance includes processor resources allocated to the general-purpose server and processor resources allocated to the graphics card resource pool. Each of the multiple sets of training data corresponding to the resource configuration model includes a configuration requirement sample and corresponding real-world mounting information.An initial neural network model is used to predict the input configuration requirement information to obtain preset mounting information; a training loss is calculated based on the actual mounting information and the predicted mounting information; and network parameters of the initial neural network model are adjusted based on the training loss to obtain a resource configuration model. The resource configuration method provided in the embodiments of the present application also includes other optional implementations, which can be referred to the relevant descriptions in the aforementioned embodiments and are not described in detail here. In one optional embodiment, in step S72, the configuration requirement information and the resource configuration model are used to perform resource configuration on the general server and the graphics card resource pool to obtain a configuration result. The method includes the following steps: Step S721: Inputting the configuration requirement information into the resource configuration model to obtain information to be mounted, wherein the information to be mounted is used to determine at least one target graphics processor to be mounted to the general server from multiple graphics processors in the graphics card resource pool; Step S722: Combining and packaging resources on the general server and the target graphics processor to obtain a configuration result. The computing power service may be an elastic computing service. In this regard, in the elastic computing system provided in the embodiments of the present application, a graphics card resource pool includes multiple graphics processors (i.e., pluggable GPU cards). In the application scenario, a pre-trained resource configuration model is used to determine, based on the configuration requirement information, the type, quantity, and specifications of the processor resources required for the instance of the current computing task configured for the computing power service. Based on this, at least one GPU card to be attached to the general-purpose server is selected from the multiple GPU cards in the graphics card resource pool according to the configuration requirement information. In an embodiment of the present application, a currently input resource configuration request is obtained, wherein the request data carried in the resource configuration request includes configuration requirement information corresponding to a computing power service. In response to the resource configuration request, a resource configuration reply is returned, wherein the resource configuration reply includes instance information of a first instance provided for the computing power service. The first instance is determined based on a configuration result, which is obtained by configuring resources for a general server and a graphics card resource pool according to the configuration requirement information. The general server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online pluggable functionality. The first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool. The instance information is then displayed in a graphical user interface. Thus, the present application achieves the goal of flexibly configuring processor resources based on configuration requirements, thereby achieving the technical effect of improving resource utilization and overall flexibility in heterogeneous machines in elastic computing scenarios. This further addresses the technical problems in related technologies of low resource utilization and poor overall flexibility caused by the fixed configuration of processor resources in heterogeneous machines.It should be noted that the preferred implementation of this embodiment can be found in the relevant descriptions in Example 1, Example 2, or Example 3, and will not be repeated here. Example 5: According to an embodiment of the present application, a system embodiment for implementing the above-mentioned resource configuration method is also provided. Figure 8 is a schematic structural diagram of a resource configuration system according to Example 5 of the present application. As shown in Figure 8, the resource configuration system includes: a general server 801, configured based on processor resources, memory resources, and service manager resources; a graphics card resource pool 802, including multiple graphics processors supporting online plug-and-play functionality; and a graphics card interface box 803, connected to the general server and the graphics card resource pool, configured to perform resource configuration on the general server and the graphics card resource pool based on configuration requirement information corresponding to the computing power service, obtaining a configuration result. The configuration result is used to support the general server in providing a first instance for the computing power service. The first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool. Optionally, in the above-mentioned resource configuration system, the graphics card interface box is also configured to identify and repair faults in the general server and the graphics card resource pool. Optionally, in the resource configuration system, the general server is further configured to provide a second instance for the computing service, where the second instance includes processor resources provided by the general server. Based on this resource configuration system, the resource configuration methods provided in Examples 1, 2, 3, or 4 can be implemented. The resource configuration methods corresponding to the computing service can be applied to assist in implementing scientific computing tasks, machine learning tasks (such as training deep learning models, image recognition, natural language processing, etc.), cloud gaming tasks (such as providing high-performance game graphics rendering and real-time interaction), big data analysis tasks, and other tasks in pre-defined application scenarios. These pre-defined application scenarios include, but are not limited to, cloud computing scenarios in fields such as e-commerce, education, healthcare, conferencing, social networking, financial products, logistics, and navigation. The configuration requirement information can be determined by user input data or by the computing service and pre-defined configuration rules. The configuration requirement information can indicate the quantity, type, and specifications of the processor resources required by the computing service. Based on the configuration requirement information, resource configuration for the general-purpose server and graphics card resource pool may be implemented by selecting, combining, and packaging resources provided by the general-purpose server and graphics processor resources provided by the graphics card resource pool to obtain the configuration result. The configuration result is used to determine at least the processor resources to be used when providing computing instances for the computing service. The configuration result may also be used to determine memory resources, service manager resources, and other resources to be used when providing computing instances for the computing service.For example, the configuration result may include: the encapsulation result of the processor resources, the encapsulation result of the processor resources with memory resources and service manager resources, or resource identifiers of the processor resources, memory resources, or service manager resources. It should be noted that the types of processor resources configured for the general-purpose server and the processor resources configured for the graphics card resource pool may be different. For example, the processor resources configured in the general-purpose server may be central processing unit resources, while the processor resources in the graphics card resource pool may be graphics processor resources. In particular, the multiple graphics processors in the graphics card resource pool may also support online plugging and unplugging. Furthermore, the first instance provided for the computing service based on the configuration result may be a GPU instance. This GPU instance may include CPU resources allocated by the general-purpose server and GPU resources allocated by the graphics card resource pool. In particular, the GPU resources allocated by the graphics card resource pool may be single-GPU card resources, dual-GPU card resources, quad-GPU card resources, or eight-GPU card resources. Thus, the present application configures resources for a general-purpose server and a graphics card resource pool based on configuration requirement information and provides a first instance based on the configuration result. This enables reuse of processor resources in the general-purpose server and flexible configuration of multiple graphics processors in the graphics card resource pool (for example, flexibly selecting at least one graphics processor and attaching it to the general-purpose server to provide a GPU instance). This results in a rich and diverse configuration to meet the diverse needs of computing services in different scenarios. In an embodiment of the present application, a resource configuration system is proposed, comprising: a general-purpose server configured based on processor resources, memory resources, and service manager resources; a graphics card resource pool comprising multiple graphics processors supporting online pluggable functionality; and a graphics card interface box connected to the general-purpose server and the graphics card resource pool and configured to configure resources for the general-purpose server and the graphics card resource pool based on the configuration requirement information corresponding to the computing service, thereby obtaining a configuration result. The configuration result is used to enable the general-purpose server to provide a first instance for the computing service. The first instance includes processor resources allocated by the general-purpose server and processor resources allocated by the graphics card resource pool. As a result, the present application achieves the goal of flexibly configuring processor resources based on configuration requirements, thereby achieving the technical effect of improving resource utilization and overall machine flexibility in heterogeneous machines in elastic computing scenarios. This further addresses the technical issues in the related art of low resource utilization and poor overall machine flexibility caused by heterogeneous machines being limited by the fixed configuration of overall machine processor resources. It should be noted that the preferred implementation of this embodiment can be found in the relevant descriptions in Examples 1, 2, 3, or 4, and will not be repeated here. Example 6: According to the embodiments of the present application, an embodiment of an apparatus configured to implement the above-mentioned resource configuration method is also provided.FIG9 is a schematic structural diagram of a resource configuration device according to Example 6 of the present application. As shown in FIG9 , the device includes: an acquisition module 901 configured to acquire configuration requirement information corresponding to a computing power service; a configuration module 902 configured to perform resource configuration on a general-purpose server and a graphics card resource pool based on the configuration requirement information, obtaining a configuration result, wherein the general-purpose server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online plug-and-play functionality; and a first instance module 903 configured to provide a first instance for the computing power service based on the configuration result, wherein the first instance includes processor resources allocated to the general-purpose server and processor resources allocated to the graphics card resource pool. Optionally, the acquisition module 901 is further configured to: acquire a computing task category corresponding to the computing power service; and generate configuration requirement information based on the computing task category, wherein the configuration requirement information indicates the proportion of different types of processor resources to be configured required by the computing power service. Optionally, the configuration module 902 is further configured to: determine, based on the configuration requirement information, at least one target graphics processor to be mounted to the general server from multiple graphics processors in the graphics card resource pool; perform resource combination and packaging processing on the general server and the target graphics processor to obtain a configuration result. Optionally, the configuration module 902 is further configured to: send a first plug-in / plug-out instruction to the target graphics processor, wherein the first plug-in / plug-out instruction is used to control the target graphics processor to adjust its plug-in / plug-out state so as to be mounted to the general server; and perform packaging processing on the general server and the target graphics processor to obtain a configuration result. Optionally, in addition to all of the above modules, the resource configuration apparatus further includes: a second instance module 904 (not shown), configured to: provide a second instance for the computing power service based on the configuration requirement information, wherein the second instance includes processor resources provided by the general server. Optionally, in addition to all of the above modules, the resource configuration device further includes a fault module 905 (not shown), configured to: identify faults on multiple graphics processors in the general server and graphics card resource pool to obtain fault identification results, wherein the fault identification results are used to determine the faulty component; and repair the faulty component based on the fault identification results. Optionally, the fault module 905 is further configured to: determine a replacement component corresponding to the faulty component from the general server and graphics card resource pool based on the fault identification results, wherein the replacement component is an available component of the same category as the faulty component and has not experienced a fault; and send a second plug-in / plug-out instruction to the faulty component and the replacement component, wherein the second plug-in / plug-out instruction is used to control the offline isolation of the faulty component and the adjustment of the plug-in / plug-out status of the replacement component so that it can be mounted on the general server.In an embodiment of the present application, an acquisition module is used to obtain configuration requirement information corresponding to a computing power service. Furthermore, a configuration module is used to configure resources for a general-purpose server and a graphics card resource pool based on the configuration requirement information, obtaining a configuration result. The general-purpose server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online plug-and-unplug functionality. Based on this configuration result, a first instance is provided for the computing power service using a first instance module. The first instance includes processor resources allocated to the general-purpose server and processor resources allocated to the graphics card resource pool. Thus, the present application achieves the goal of flexibly configuring processor resources based on configuration requirements, thereby improving resource utilization and overall flexibility in heterogeneous machines in elastic computing scenarios. This addresses the technical issues in related technologies such as low resource utilization and poor overall flexibility caused by heterogeneous machines being limited by fixed processor resource configurations. It should be noted that the acquisition module 901, configuration module 902, and first instance module 903 described above correspond to steps S21 to S23 in Example 1. The examples and application scenarios implemented by these three modules and the corresponding steps are the same, but are not limited to the content disclosed in Example 1. It should be noted that the above modules or units may be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, 102n). The above modules may also be part of an apparatus and run in the computer terminal 10 provided in Example 1. According to an embodiment of the present application, a device embodiment configured to implement the resource configuration method described in Example 2 is also provided. Figure 10 is a structural diagram of another resource configuration device according to Example 6 of the present application. As shown in Figure 10, the device includes: a request module 1001, configured to obtain a resource configuration request through a first application programming interface, wherein the request data carried in the resource configuration request includes: configuration requirement information corresponding to the computing power service; a response module 1002, configured to return a resource configuration response through a second application programming interface, wherein the response data carried in the resource configuration response includes: instance information of a first instance provided for the computing power service, the first instance being determined based on a configuration result, and the configuration result being obtained by performing resource configuration on a general server and a graphics card resource pool according to the configuration requirement information, the general server being obtained based on processor resource configuration, the graphics card resource pool including multiple graphics processors supporting online plug-in and unplugging functions, and the first instance including processor resources allocated by the general server and processor resources allocated by the graphics card resource pool.It should be noted that the request module 1001 and response module 1002 correspond to steps S51 to S52 in Example 2. The examples and application scenarios implemented by these two modules and the corresponding steps are the same, but are not limited to the content disclosed in Example 2. It should be noted that the above modules or units may be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules may also be part of an apparatus and run in the computer terminal 10 provided in Example 1. According to an embodiment of the present application, an apparatus embodiment configured to implement the resource configuration method in Example 3 is also provided. FIG11 is a schematic structural diagram of another resource configuration device according to Example 6 of the present application. As shown in FIG11 , the device includes: a request module 1101, configured to obtain a currently input resource configuration request, wherein the request data carried in the resource configuration request includes: configuration requirement information corresponding to the computing power service; a reply module 1102, configured to return a resource configuration reply in response to the resource configuration request, wherein the information carried in the resource configuration reply includes: instance information of a first instance provided for the computing power service, the first instance being determined based on a configuration result, the configuration result being obtained by performing resource configuration on a general server and a graphics card resource pool according to the configuration requirement information, the general server being obtained based on processor resource configuration, the graphics card resource pool including multiple graphics processors supporting online plug-in functionality, the first instance including processor resources allocated by the general server and processor resources allocated by the graphics card resource pool; and a display module 1103, configured to display the instance information in a graphical user interface. It should be noted that the request module 1101, reply module 1102, and presentation module 1103 correspond to steps S61 to S63 in Example 3. The examples and application scenarios implemented by these three modules and the corresponding steps are the same, but are not limited to the contents disclosed in Example 3. It should be noted that the above modules or units may be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, 102n). The above modules may also be part of an apparatus and run in the computer terminal 10 provided in Example 1. It should be noted that the preferred implementation of this embodiment can be found in the relevant descriptions in Example 1, Example 2, Example 3, or Example 4, and will not be repeated here.Example 7 According to an embodiment of the present application, an electronic device is further provided. The electronic device can be any computer device in a computer device group. Optionally, in this embodiment, the electronic device can be replaced with a terminal device such as a mobile terminal. Optionally, in this embodiment, the electronic device can be located in at least one of multiple network devices in a computer network. In this embodiment, the electronic device can execute program code for the following steps in a resource configuration method: obtaining configuration requirement information corresponding to a computing power service; configuring resources for a general server and a graphics card resource pool based on the configuration requirement information to obtain a configuration result, wherein the general server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online plug-in functionality; and providing a first instance for the computing power service based on the configuration result, wherein the first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool. Alternatively, FIG12 is a block diagram of an electronic device according to Embodiment 7 of the present application. As shown in FIG12 , the electronic device 120 may include one or more processors 1202 (only one is shown), a memory 1204, a storage controller 1206, and a peripheral interface 1208. The peripheral interface 1208 is connected to a radio frequency module, an audio module, and a display. The memory 1204 may be configured to store software programs and modules, such as program instructions / modules corresponding to the resource configuration method and apparatus in the embodiments of the present application. The processor executes the software programs and modules stored in the memory to execute various functional applications and data processing, thereby implementing the resource configuration method described above. The memory 1204 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 1204 may further include memory located remotely from the processor. Such remote memory may be connected to the electronic device 120 via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. Processor 1202 can access information and applications stored in memory via a transmission device to perform the following steps: obtaining configuration requirement information corresponding to a computing power service; configuring resources for a general-purpose server and a graphics card resource pool based on the configuration requirement information to obtain a configuration result, wherein the general-purpose server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online pluggable functionality; and providing a first instance for the computing power service based on the configuration result, wherein the first instance includes processor resources allocated by the general-purpose server and processor resources allocated by the graphics card resource pool.Optionally, the processor 1202 may further execute program code for the following steps: obtaining a computing task category corresponding to the computing power service; generating configuration requirement information based on the computing task category, wherein the configuration requirement information indicates the proportion of different types of processor resources to be configured for the computing power service. Optionally, the processor 1202 may further execute program code for the following steps: determining, based on the configuration requirement information, at least one target graphics processor to be mounted to a general-purpose server from multiple graphics processors in a graphics card resource pool; combining and packaging resources of the general-purpose server and the target graphics processor to obtain a configuration result. Optionally, the processor 1202 may further execute program code for the following steps: sending a first plug-in / plug-out instruction to the target graphics processor, wherein the first plug-in / plug-out instruction is used to control the target graphics processor to adjust its plug-in / plug-out state so that it can be mounted to the general-purpose server; packaging the general-purpose server and the target graphics processor to obtain a configuration result. Optionally, the processor 1202 may further execute program code for the following steps: providing a second instance for the computing power service based on the configuration requirement information, wherein the second instance includes processor resources provided by the general-purpose server. Optionally, the processor 1202 may further execute program code for the following steps: performing fault identification on multiple graphics processors in the general server and graphics card resource pool to obtain a fault identification result, wherein the fault identification result is used to determine the faulty component; and performing fault repair on the faulty component based on the fault identification result. Optionally, the processor 1202 may further execute program code for the following steps: determining, based on the fault identification result, a replacement component corresponding to the faulty component from the general server and graphics card resource pool, wherein the replacement component is an available component of the same category as the faulty component and has not experienced a fault; and sending a second plug-in / plug-out instruction to the faulty component and the replacement component, wherein the second plug-in / plug-out instruction is used to control the offline isolation of the faulty component and the adjustment of the plug-in / plug-out state of the replacement component so that it can be mounted on the general server.The processor 1202 can call the information and application stored in the memory through the transmission device to perform the following steps: obtain a resource configuration request through the first application programming interface, wherein the request data carried in the resource configuration request includes: configuration requirement information corresponding to the computing power service; return a resource configuration response through the second application programming interface, wherein the response data carried in the resource configuration response includes: instance information of the first instance provided for the computing power service, the first instance is determined based on the configuration result, the configuration result is obtained by configuring resources for the general server and the graphics card resource pool according to the configuration requirement information, the general server is obtained based on the processor resource configuration, the graphics card resource pool includes multiple graphics processors that support online plug-in functions, and the first instance includes processor resources allocated by the general server and the processor resources allocated by the graphics card resource pool. The processor 1202 can call the information and application stored in the memory through the transmission device to perform the following steps: obtain the currently input resource configuration request, wherein the request data carried in the resource configuration request includes: configuration requirement information corresponding to the computing power service; respond to the resource configuration request, return a resource configuration reply, wherein the information carried in the resource configuration reply includes: instance information of the first instance provided for the computing power service, the first instance is determined based on the configuration result, the configuration result is obtained by configuring resources for the general server and the graphics card resource pool according to the configuration requirement information, the general server is obtained based on the processor resource configuration, the graphics card resource pool includes multiple graphics processors that support online plug-in and unplug functions, the first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool; and display the instance information in the graphical user interface. Processor 1202 can access information and applications stored in memory via a transmission device to perform the following steps: obtaining configuration requirement information corresponding to a computing power service; using the configuration requirement information and a resource configuration model to perform resource configuration for a general-purpose server and a graphics card resource pool to obtain a configuration result, wherein the resource configuration model is a neural network model pre-trained through machine learning using multiple sets of training data; the general-purpose server is configured based on processor resources; and the graphics card resource pool includes multiple graphics processors that support online plug-and-play functionality; providing a first instance for the computing power service based on the configuration result, wherein the first instance includes processor resources allocated by the general-purpose server and processor resources allocated by the graphics card resource pool. Optionally, processor 1202 can also execute program code for the following steps: inputting the configuration requirement information into the resource configuration model to obtain to-be-mounted information, wherein the to-be-mounted information is used to determine at least one target graphics processor to be mounted to the general-purpose server from among the multiple graphics processors in the graphics card resource pool; and performing resource combination and packaging processing on the general-purpose server and the target graphics processor to obtain a configuration result.An embodiment of the present application provides an electronic device configured to implement the aforementioned resource configuration method. Configuration requirement information corresponding to a computing service is obtained; further, resource configuration is performed on a general server and a graphics card resource pool based on the configuration requirement information to obtain a configuration result. The general server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online plug-and-play functionality. Based on the configuration result, a first instance is provided for the computing service. The first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool. Thus, the present application achieves the goal of flexibly configuring processor resources based on configuration requirements, thereby improving resource utilization and overall flexibility in heterogeneous machines in elastic computing scenarios. This further addresses the technical issues in related art such as low resource utilization and poor overall flexibility caused by heterogeneous machines being limited by fixed processor resource configurations. Those skilled in the art will appreciate that the structure shown in FIG. 12 is merely illustrative, and the electronic device may also be a terminal device such as a smartphone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a PDA, or a mobile internet device (MID). Figure 12 does not limit the structure of the electronic device described above. For example, electronic device 120 may include more or fewer components (such as a network interface, a display device, etc.) than those shown in Figure 12, or may have a configuration different from that shown in Figure 12. Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by a program instructing the hardware associated with the terminal device. The program can be stored in a computer-readable storage medium, which may include a flash drive, ROM, RAM, a magnetic disk, or an optical disk. Example 8 According to an embodiment of the present application, a computer-readable storage medium is also provided. Optionally, in this embodiment, the storage medium may be configured to store program code executed by the resource configuration method provided in Example 1, Example 2, Example 3, or Example 4. Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining configuration requirement information corresponding to a computing service; performing resource configuration on a general-purpose server and a graphics card resource pool based on the configuration requirement information to obtain a configuration result, wherein the general-purpose server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online plug-and-unplug functionality; providing a first instance for the computing service based on the configuration result, wherein the first instance includes processor resources allocated by the general-purpose server and processor resources allocated by the graphics card resource pool. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining a computing task category corresponding to the computing service; generating configuration requirement information based on the computing task category, wherein the configuration requirement information is used to indicate the to-be-configured ratio of different types of processor resources required by the computing service. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: determining, based on the configuration requirement information, at least one target graphics processor to be mounted to the general-purpose server from multiple graphics processors in the graphics card resource pool; and performing resource combination and packaging processing on the general-purpose server and the target graphics processor to obtain a configuration result. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: sending a first plug-in / plug-out instruction to a target graphics processor, wherein the first plug-in / plug-out instruction is used to control the target graphics processor to adjust its plug-in / plug-out state so that it can be mounted on a general-purpose server; and performing packaging processing on the general-purpose server and the target graphics processor to obtain a configuration result. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: providing a second instance for a computing service based on configuration requirement information, wherein the second instance includes processor resources provided by the general-purpose server. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: performing fault identification on the general-purpose server and multiple graphics processors in the graphics resource pool to obtain a fault identification result, wherein the fault identification result is used to determine a faulty component; and repairing the faulty component based on the fault identification result. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: based on the fault identification result, determining a replacement component corresponding to the faulty component from a general server and a graphics card resource pool, wherein the replacement component is an available component that is of the same category as the faulty component and has not experienced a fault; and sending a second plug-in / plug-out instruction to the faulty component and the replacement component, wherein the second plug-in / plug-out instruction is used to control offline isolation of the faulty component and control adjustment of the plug-in / plug-out state of the replacement component so as to be mounted on the general server.Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining a resource configuration request through a first application programming interface, wherein request data carried in the resource configuration request includes: configuration requirement information corresponding to the computing power service; returning a resource configuration response through a second application programming interface, wherein response data carried in the resource configuration response includes: instance information of a first instance provided for the computing power service, the first instance being determined based on a configuration result, the configuration result being obtained by performing resource configuration on a general server and a graphics card resource pool according to the configuration requirement information, the general server being obtained based on processor resource configuration, the graphics card resource pool including multiple graphics processors supporting online plug-in / plug-out functionality, and the first instance including processor resources allocated by the general server and processor resources allocated by the graphics card resource pool. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining a currently input resource configuration request, wherein the request data carried in the resource configuration request includes: configuration requirement information corresponding to the computing power service; returning a resource configuration reply in response to the resource configuration request, wherein the information carried in the resource configuration reply includes: instance information of a first instance provided for the computing power service, the first instance being determined based on a configuration result, the configuration result being obtained by performing resource configuration on a general server and a graphics card resource pool according to the configuration requirement information, the general server being obtained based on processor resource configuration, the graphics card resource pool including multiple graphics processors supporting online plug-in / plug-out functionality, the first instance including processor resources allocated by the general server and processor resources allocated by the graphics card resource pool; and displaying the instance information in a graphical user interface. Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining configuration requirement information corresponding to the computing power service; performing resource configuration on the general server and the graphics card resource pool using the configuration requirement information and a resource configuration model to obtain a configuration result, wherein the resource configuration model is a neural network model pre-trained by machine learning using multiple sets of training data, the general server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online plug-in and unplugging functions; and providing a first instance for the computing power service based on the configuration result, wherein the first instance includes processor resources allocated to the general server and processor resources allocated to the graphics card resource pool.Optionally, in this embodiment, a computer-readable storage medium is configured to store program code for executing the following steps: inputting configuration requirement information into a resource configuration model to obtain to-be-mounted information, wherein the to-be-mounted information is used to determine at least one target graphics processor to be mounted to a general-purpose server from multiple graphics processors in a graphics resource pool; and performing resource combination and packaging processing on the general-purpose server and the target graphics processor to obtain a configuration result. According to an embodiment of the present application, a computer-readable storage medium configured to implement the above-described resource configuration method is provided. Configuration requirement information corresponding to a computing power service is obtained; further, resource configuration is performed on the general-purpose server and the graphics resource pool based on the configuration requirement information to obtain a configuration result, wherein the general-purpose server is configured based on processor resources, and the graphics resource pool includes multiple graphics processors that support online plugging and unplugging. Based on the configuration result, a first instance is provided for the computing power service, wherein the first instance includes processor resources allocated by the general-purpose server and processor resources allocated by the graphics resource pool. Thus, the present application achieves the goal of flexibly configuring processor resources based on configuration requirements, thereby achieving the technical effect of improving resource utilization and overall machine flexibility in heterogeneous machines in elastic computing scenarios. This further addresses the technical issues in related arts of low resource utilization and poor overall machine flexibility caused by heterogeneous machines being limited by the fixed configuration of overall machine processor resources. According to an embodiment of the present application, a computer program product is also provided. Optionally, in this embodiment, the computer program product can provide resource configuration services based on the resource configuration method provided in Embodiment 1, 2, 3, or 4 above. Optionally, in this embodiment, the computer program product can be a set of pre-written instructions and codes based on the resource configuration method. The computer program product can run on various computer platforms, including personal computers, servers, mobile devices, and the like. Optionally, in this embodiment, the instructions and codes corresponding to the computer program product are used to implement the following method steps: obtaining configuration requirement information corresponding to the computing power service; performing resource configuration on the general server and the graphics card resource pool according to the configuration requirement information to obtain a configuration result, wherein the general server is obtained based on the processor resource configuration, and the graphics card resource pool includes multiple graphics processors that support online plug-in and unplugging functions; and providing a first instance for the computing power service based on the configuration result, wherein the first instance includes the processor resources allocated by the general server and the processor resources allocated by the graphics card resource pool.The computer program product described above can provide resource configuration services in application scenarios involving processor resource configuration for cloud computing services, achieving the goal of flexibly configuring processor resources based on configuration requirements. This achieves the technical effect of improving resource utilization and overall machine flexibility in heterogeneous machines in elastic computing scenarios, thereby resolving the technical problems in the related art of low resource utilization and poor overall machine flexibility caused by heterogeneous machines being limited by the fixed configuration of overall machine processor resources. The serial numbers of the above-mentioned embodiments of this application are for illustrative purposes only and do not represent the advantages or disadvantages of the embodiments. In the above-mentioned embodiments of this application, the description of each embodiment has its own emphasis. For portions not detailed in a particular embodiment, reference should be made to the relevant descriptions of other embodiments. In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units described is only one logical functional division. In actual implementation, other division methods may be used. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not implemented. Furthermore, the coupling, direct coupling, or communication connection shown or discussed may be through interfaces, indirect coupling, or communication connection between units or modules, and may be electrical or otherwise. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the objectives of the present embodiment as needed. Furthermore, the functional units in the various embodiments of the present application may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. These integrated units may be implemented in either hardware or software functional units. If these integrated units are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, ROM, RAM, a mobile hard drive, a magnetic disk, or an optical disk.The above description is only a preferred embodiment of the present application. It should be noted that a person skilled in the art can make several improvements and modifications without departing from the principles of the present application, and such improvements and modifications should also be considered within the scope of protection of the present application.

Claims

Claims 1. A resource allocation method, wherein: include: Obtain configuration requirements information corresponding to the computing power service; Resources are configured for a general server and a graphics card resource pool according to the configuration requirement information to obtain a configuration result, wherein the general server is obtained based on processor resource configuration, and the graphics card resource pool includes multiple graphics processors that support online plug-and-unplug functionality; and a first instance is provided for the computing power service based on the configuration result, wherein the first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool.

2. The resource configuration method according to claim 1, wherein obtaining the configuration requirement information corresponding to the computing power service comprises: Obtain the computing task category corresponding to the computing power service; The configuration requirement information is generated based on the computing task category, wherein the configuration requirement information is used to represent the proportion of different types of processor resources to be configured required by the computing power service.

3. The resource configuration method according to claim 1, wherein performing resource configuration on the general server and the graphics card resource pool according to the configuration requirement information, and obtaining the configuration result comprises: determining, according to the configuration requirement information, at least one target graphics processor to be mounted to the general server from the plurality of graphics processors in the graphics card resource pool; Resource combination and packaging processing is performed on the general server and the target graphics processor to obtain the configuration result.

4. The resource configuration method according to claim 3, wherein the step of combining and packaging resources of the general server and the target GPU to obtain the configuration result comprises: Sending a first plug-in instruction to the target graphics processor, wherein the first plug-in instruction is used to control the target graphics processor to adjust the plug-in state so as to be mounted to the general server; and performing packaging processing on the general server and the target graphics processor to obtain the configuration result.

5. The resource configuration method according to claim 1, further comprising: A second instance is provided for the computing service according to the configuration requirement information, wherein the second instance includes processor resources provided by the general server.

6. The resource configuration method according to claim 1, further comprising: Fault identification is performed on the general server and the plurality of graphics processors in the graphics resource pool to obtain a fault identification result, wherein the fault identification result is used to determine a faulty component; and based on the fault identification result, the fault component is repaired.

7. The resource configuration method according to claim 6, wherein based on the fault identification result, the faulty component 28 Troubleshooting includes: Based on the fault identification result, a replacement component corresponding to the faulty component is determined from the general server and the graphics card resource pool, where the replacement component is an available component that is of the same category as the faulty component and has not experienced a fault; and a second plug-in / plug-out instruction is sent to the faulty component and the replacement component, where the second plug-in / plug-out instruction is used to control offline isolation of the faulty component and control adjustment of the plug-in / plug-out state of the replacement component so that it can be mounted on the general server.

8. A resource allocation method, wherein: include: A resource configuration request is obtained through a first application programming interface, wherein request data carried in the resource configuration request includes: configuration requirement information corresponding to the computing power service; and a resource configuration response is returned through a second application programming interface, wherein response data carried in the resource configuration response includes: instance information of a first instance provided for the computing power service, the first instance being determined based on a configuration result, the configuration result being obtained by configuring resources of a general-purpose server and a graphics card resource pool according to the configuration requirement information, the general-purpose server being obtained based on processor resource configuration, the graphics card resource pool including multiple graphics processors supporting online plug-in / plug-out functionality, and the first instance including processor resources allocated by the general-purpose server and processor resources allocated by the graphics card resource pool.

9. A resource allocation method, wherein: include: Obtain a currently input resource configuration request, wherein request data carried in the resource configuration request includes: configuration requirement information corresponding to a computing power service; return a resource configuration reply in response to the resource configuration request, wherein information carried in the resource configuration reply includes: instance information of a first instance provided for the computing power service, the first instance being determined based on a configuration result, the configuration result being obtained by performing resource configuration on a general-purpose server and a graphics card resource pool according to the configuration requirement information, the general-purpose server being obtained based on processor resource configuration, the graphics card resource pool including multiple graphics processors supporting online plug-in / plug-out functionality, the first instance including processor resources allocated by the general-purpose server and processor resources allocated by the graphics card resource pool; and displaying the instance information in a graphical user interface.

10. A resource allocation method, wherein: include: Obtain configuration requirements information corresponding to the computing power service; Using the configuration requirement information and the resource configuration model, resource configuration is performed on a general-purpose server and a graphics card resource pool to obtain a configuration result, wherein the resource configuration model is a neural network model pre-trained through machine learning using multiple sets of training data, the general-purpose server is configured based on processor resources, and the graphics card resource pool includes multiple graphics processors that support online pluggable functionality; A first instance is provided for the computing power service based on the configuration result, wherein the first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool.

11. The resource configuration method according to claim 10, wherein the configuration requirement information and the resource configuration model are used to perform resource configuration on a general server and a graphics card resource pool, and the configuration result obtained includes: Inputting the configuration requirement information into the resource configuration model to obtain information to be mounted, wherein the information to be mounted is used to determine at least one target graphics processor to be mounted to the general server from the multiple graphics processors in the graphics card resource pool; and performing resource combination and packaging processing on the general server and the target graphics processor to obtain the configuration result.

12. A resource allocation system, wherein: include: General purpose servers, configured based on processor resources, memory resources, and service manager resources; Graphics resource pool, including multiple graphics processors that support online plug-in and unplug functionality; A graphics card interface box is connected to the general server and the graphics card resource pool, and is configured to perform resource configuration on the general server and the graphics card resource pool according to configuration requirement information corresponding to the computing power service, to obtain a configuration result, wherein the configuration result is used to support the general server in providing a first instance for the computing power service, where the first instance includes processor resources allocated by the general server and processor resources allocated by the graphics card resource pool.

13. The resource configuration system according to claim 12, wherein the graphics card interface box is further configured to perform fault identification and fault repair on the general server and the graphics card resource pool.

14. The resource configuration system according to claim 12, wherein the general server is further configured to provide a second instance for computing power service, The second instance includes processor resources provided by the general purpose server.

15. An electronic device, wherein: include: a memory storing an executable program; A processor is configured to run the program, wherein the program executes the resource configuration method according to any one of claims 1 to 9 when running.

16. A computer-readable storage medium, wherein: The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the computer-readable storage medium is located is controlled to execute the resource configuration method according to any one of claims 1 to 11.

17. A computer program product, wherein: The method comprises a computer program, wherein when the computer program is executed by a processor, the resource configuration method according to any one of claims 1 to 11 is implemented.

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