Computing power resource metering method and device of intelligent computing center

Abstract

The invention provides a computing power resource metering method and device of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the computing power resource metering method of the intelligent computing center comprises the following steps: S1, receiving a target request sent by a target user, the target request being used for requesting to process a target task; s2, creating a target container according to the target request, processing the target task through the target container, and obtaining an initial operation moment of the target container; and S3, under the condition that the target container stops running, the running stopping moment of the target container is obtained, use information of the computing power resources corresponding to the target container in a target time period is measured, and the target time period is a time period between the starting running moment and the running stopping moment. In this way, the metering effect of the computing power resources consumed in the target task processing process is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent computing centers, smart computing centers and computing power infrastructure, and in particular to a computing power resource metering method and device for an intelligent computing center. Background Art

[0002] With the rapid development of artificial intelligence technology, "intelligent computing centers" and "intelligent computing centers" have emerged.

[0003] An "Intelligent Computing Center" is a facility that uses large-scale heterogeneous computing resources, including general-purpose and intelligent computing power, to provide the computing power, data, and algorithms required for AI applications (such as AI deep learning model development, model training, and model inference). The Intelligent Computing Center encompasses facilities, hardware, and software, and provides a full stack of capabilities, from bottom-level computing power to top-level application enablement.

[0004] “Intelligent Computing Center” includes but is not limited to “Smart Computing Center”.

[0005] "Intelligent Computing Center" refers to an artificial intelligence computing center. It is a type of computing power infrastructure that is based on artificial intelligence theory, adopts artificial intelligence computing architecture, and provides computing power services, data services, and algorithm services required for artificial intelligence applications.

[0006] "Computing power" is the core of "intelligent computing center" and "intelligent computing center". It is the ability of computer equipment or computing / data center to process information. It is the ability of computer hardware and software to work together to perform certain computing needs. It is the computing power to achieve target result output by processing information data. It is a new type of productivity that integrates information computing power, network carrying capacity, and data storage capacity. It mainly provides services to society through computing power infrastructure.

[0007] When the current intelligent computing center receives a request for processing a target task, it needs to allocate computing resources to the target task. However, the current intelligent computing center lacks a method to measure the computing resources consumed during the processing of the target task. It can be seen that since the emergence of the intelligent computing center, the lack of a method to measure the computing resources consumed during the processing of the target task is an urgent problem to be solved. Summary of the Invention

[0008] The present invention provides a method and device for measuring computing power resources of an intelligent computing center, which are used to solve the problem of the current lack of a method for measuring computing power resources consumed in the process of processing target tasks.

[0009] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0010] In a first aspect, the present invention provides a method for measuring computing power resources of an intelligent computing center, comprising:

[0011] Step S1: receiving a target request sent by a target user, wherein the target request is used to request processing of a target task;

[0012] Step S2: creating a target container according to the target request, processing the target task through the target container, and obtaining the start running time of the target container;

[0013] Step S3: When the target container stops running, the termination time of the target container is obtained, and the usage information of the computing resources corresponding to the target container in the target time period is measured. The target time period is the time period between the start running time and the termination running time.

[0014] Optionally, step S2 includes:

[0015] Step S21: selecting a target processor according to the target request, where the target processor includes at least one of the following: a central processing unit (CPU) or a graphics processing unit (GPU);

[0016] Step S22: creating the target container according to the target processor, processing the target task through the target container, and obtaining the start running time of the target container.

[0017] Optionally, the target processor includes the CPU, and step S22 includes:

[0018] Step S221: Create the target container according to the CPU, and obtain first information of the CPU, where the first information includes the number of cores of the CPU;

[0019] Step S222: When the first information of the CPU is a first preset number, the target task is processed through the target container, and a start running time of the target container is obtained.

[0020] Optionally, the computing power resource includes the GPU, and step S22 includes:

[0021] Step S223: creating the target container according to the GPU, and obtaining second information of the GPU, where the second information includes the number and operating status of the GPU;

[0022] Step S224: When the number of the GPUs is a second preset number and the running state is a target state, the target task is processed through the target container, and a start running time of the target container is obtained.

[0023] Optionally, the usage information includes at least one of the following: the usage duration of the computing power resources, and the usage time period of the computing power resources.

[0024] Optionally, the usage information includes the usage duration of the computing resources. After step S3, the following steps are further included:

[0025] Step S4: Determine whether the usage time of the computing power resource is greater than the time corresponding to the target time period;

[0026] Step S5: When it is determined that the usage duration of the computing resources is greater than the duration corresponding to the target time period, the usage information of the computing resources corresponding to the target container within the target time period is re-measured.

[0027] In a second aspect, the present invention provides a computing resource metering device for an intelligent computing center, comprising:

[0028] A receiving module is used to receive a target request sent by a target user, wherein the target request is used to request processing of a target task;

[0029] a processing module, configured to create a target container according to the target request, process the target task through the target container, and obtain a start running time of the target container;

[0030] A metering module is configured to obtain, when the target container terminates operation, the termination time of the target container and measure usage information of computing resources corresponding to the target container within a target time period, where the target time period is the time period between the start time and the termination time.

[0031] In a third aspect, the present invention provides an electronic device comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the method for measuring computing power resources of an intelligent computing center as described in the first aspect above are implemented.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the computing power resource metering method of the intelligent computing center as described in the first aspect above.

[0033] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, which, when executed by a processor, implement the steps of the method for measuring computing power resources of an intelligent computing center as described in the first aspect above.

[0034] In the present invention, a target request sent by a target user is received, where the target request is used to request processing of a target task; a target container is created according to the target request, the target task is processed through the target container, and the start running time of the target container is obtained; when the target container terminates running, the termination running time of the target container is obtained, and usage information of computing power resources corresponding to the target container within a target time period is measured, where the target time period is the time period between the start running time and the termination running time.

[0035] In this way, the start running time of the target container is obtained, and, when the target container stops running, the end running time of the target container is obtained, and the usage information of the computing power resources corresponding to the target container in the target time period is measured. The target time period is the time period between the start running time and the end running time, thereby providing a method for measuring the computing power resources consumed in the process of processing the target task, that is, enhancing the measurement effect of the computing power resources consumed in the process of processing the target task. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0037] Figure 1 A flow chart of a method for measuring computing power resources in an intelligent computing center provided by the present invention;

[0038] Figure 2 A schematic diagram of the structure of a computing power resource metering device for an intelligent computing center provided by the present invention;

[0039] Figure 3 This is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0040] The technical solutions in the present invention will be described clearly and completely below in conjunction with the drawings in the present invention. Obviously, the content described is part of the content of the present invention, not all of it. Based on the content in the present invention, all other content obtained by ordinary technicians in this field without creative work shall fall within the scope of protection of the present invention. The "computing power" described in the present invention refers to: the ability of computer equipment or computing / data centers to process information, the ability of computer hardware and software to work together to perform certain computing requirements, and the computing power to achieve target result output by processing information data. It is a new type of productivity that integrates information computing power, network carrying capacity, and data storage capacity, and mainly provides services to society through computing power infrastructure.

[0041] The "computing power" (Computational Power, CP) mentioned in the present invention refers to: the ability of a data center server to process data and output results. It is a comprehensive indicator to measure the computing power of a data center, including general computing power, super computing power and intelligent computing power. The commonly used unit of measurement is the number of floating-point operations performed per second (FLOPS, 1EFLOPS=10^18FLOPS). The larger the value, the stronger the comprehensive computing power. According to calculations, 1EFLOPS is approximately the computing power output of 5 Tianhe-2A or 500,000 mainstream server CPUs or 2 million mainstream notebooks. The calculation formula is: CP=CP 通用 +CP 智能 +CP 超级 .

[0042] The "carrying capacity" (Network Power, NP) mentioned in the present invention refers to: it is the performance of the data transmission capability of the computing power facility, which includes comprehensive capabilities such as network architecture, network bandwidth, transmission latency, intelligent management and scheduling, etc. It involves network transmission within and between data centers, and is a comprehensive indicator for measuring network transmission scheduling capabilities.

[0043] The "Storage Power" (SP) described in this invention refers to the comprehensive capabilities of a data center in terms of data storage capacity, performance, security and reliability, and environmental friendliness. It is a comprehensive indicator for measuring a data center's data storage capacity, encompassing both external storage devices such as storage arrays and internal server storage. Storage capacity is commonly measured in exabytes (EB, 1EB = 2^60 bytes), while performance is commonly measured in IOPS / TB (Input / Output Operations Per Second / TB). Disaster recovery ratio is a key indicator of security and reliability.

[0044] The "computing power infrastructure" mentioned in the present invention refers to a new type of information infrastructure that integrates information computing power, network carrying capacity, and data storage capacity, and can realize the centralized calculation, storage, transmission and application of information.

[0045] The "new information infrastructure" mentioned in the present invention refers to: mainly including network infrastructure such as 5G networks, fiber-optic broadband networks, backbone networks, international communication networks, satellite Internet, computing power infrastructure such as data centers, general computing power centers, intelligent computing centers, supercomputing centers, and new technology facilities such as artificial intelligence, blockchain, and quantum computing.

[0046] The "computing power" mentioned in the present invention includes: general computing power, intelligent computing power and super computing power.

[0047] The "general computing power" mentioned in the present invention refers to the computing power provided by servers based on CPU (Central Processing Unit) chips, which is used to support basic general computing such as cloud computing and edge computing.

[0048] The "intelligent computing power" mentioned in this invention refers to: a computing platform based on specialized chips such as GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), and ASIC (Application Specific Integrated Circuit) for various innovative artificial intelligence applications, such as natural language processing and machine vision.

[0049] The "supercomputing power" mentioned in the present invention refers to the computing power provided by high-performance computing clusters such as supercomputers. It utilizes the centralized computing resources of multiple computer systems working in parallel and uses a dedicated operating system to handle extremely complex or data-intensive problems. It is mainly used for calculations in cutting-edge scientific fields, such as planetary simulation, drug molecule design, genetic analysis, etc.

[0050] The "intelligent computing center" described in this article refers to a facility that provides the computing power, data, and algorithms required for artificial intelligence applications (such as AI deep learning model development, model training, and model inference) by utilizing large-scale heterogeneous computing resources, including general-purpose computing power (CPU) and intelligent computing power (GPU, FPGA, ASIC, etc.). The intelligent computing center encompasses facilities, hardware, and software, and can provide a full stack of capabilities, from bottom-level computing power to top-level application enablement.

[0051] The "intelligent computing center" mentioned in the present invention includes but is not limited to the "intelligent computing center".

[0052] The "intelligent computing center" mentioned in the present invention is an artificial intelligence computing center, which is a type of computing power infrastructure based on artificial intelligence theory, adopts artificial intelligence computing architecture, and provides computing power services, data services and algorithm services required for artificial intelligence applications.

[0053] The "computing power center" mentioned in the present invention refers to: a facility that is mainly composed of infrastructure such as wind, fire, water, electricity, and IT hardware and software equipment, and has computing power, transportation capacity, and storage capacity, including general data centers, intelligent computing centers, supercomputing centers, etc.

[0054] The "supercomputing center" mentioned in the present invention refers to: a supercomputing data center, which is a data center based on a supercomputer or a large-scale computing cluster, which can provide large-scale computing, storage and network services and other functions, and is widely used in application scenarios such as aerospace, national defense, oil exploration, climate modeling and genome sequencing.

[0055] The "computing resources" mentioned in the present invention refer to: technologies and facilities with information computing, transmission, storage and application capabilities required for the development of a digital society, including but not limited to computing resources such as CPUs and GPUs, network resources such as switches and routers, storage resources such as storage arrays and distributed storage, security resources such as firewalls and intrusion detection systems, and supporting and guarantee resources such as wind, fire, water and electricity.

[0056] The “model” mentioned in the present invention includes but is not limited to a “large language model” and a “multimodal large model”.

[0057] The "large language model" mentioned in the present invention refers to a large language model (LLM), which is a language model with a large parameter scale. It is designed to understand and generate human language. It is trained with a large amount of text data and can perform a wide range of tasks including text summarization, translation, sentiment analysis, etc.

[0058] The "Multimodal Large Models" mentioned in the present invention refer to models that combine multimodal information such as text, images, video, and audio for training, including but not limited to multimodal large language models.

[0059] See Figure 1 , Figure 1 This is a flow chart of a method for measuring computing power resources in an intelligent computing center provided by the present invention. Figure 1 As shown, the following steps are included:

[0060] Step S1: receiving a target request sent by a target user, wherein the target request is used to request processing of a target task.

[0061] Among them, the specific method of receiving the target request sent by the target user is not limited here. Optionally, the present invention can be applied to electronic devices, and the electronic device can be called a computing power platform of an intelligent computing center. The target user can make target input on the computing power platform, and the target input can be used to input the target request. It should be noted that the specific method of target input is not limited here. For example, the target input can include touch input, press input or voice input.

[0062] Optionally, the target user can also input the target request through other electronic devices, and the other electronic devices can be electrically connected to the computing power platform. In this way, the target user can input on the other electronic devices, so that the other electronic devices send the target request to the computing power platform.

[0063] The target task is not specifically limited here. Optionally, the target task may be a computing task, such as a navigation route computing task, a model training computing task, etc.

[0064] Step S2: creating a target container according to the target request, processing the target task through the target container, and obtaining a start running time of the target container.

[0065] Among them, the target task can correspond to the target container one by one, that is, each time a target task to be processed is received, a target container can be created specifically for processing the target task. In this way, since the target container is a container created specifically for processing the target task, the matching degree between the target container and the target task is higher, thereby improving the efficiency of the target container in processing the target task and improving the accuracy of processing the target task.

[0066] It should be noted that, optionally, creating a target container according to a target request includes: determining specification parameters of the target container according to parameters of the target task, and creating the target container according to the specification parameters. In this way, since the specification parameters of the target container are determined according to the parameters of the target task, the accuracy of the determined specification parameters of the target container is improved, the waste of the specification parameters of the target container is avoided, the specification parameters of the target container are maximized, and the efficiency of the target container in processing the target task is higher.

[0067] It should be noted that the parameters of the target task may include at least one of the following: the achievement goal of the target task, the specification parameters of the target container that is most compatible with the target task, etc.

[0068] The specification parameters of the target container may include at least one of the following: a calling interface of the target container, a calling object of the target container, and other information.

[0069] The start time of the target container can be understood as the start time of the target container processing the target task.

[0070] Step S3: When the target container stops running, the termination time of the target container is obtained, and the usage information of the computing resources corresponding to the target container in the target time period is measured. The target time period is the time period between the start running time and the termination running time.

[0071] Among them, when the target container stops running, it can indicate that the target task processing is completed, or it indicates that the target task processing is interrupted. At this time, the usage information of the computing power resources corresponding to the target container within the target time period can be measured, thereby enhancing the measurement accuracy of the usage information of the computing power resources corresponding to the target container within the target time period.

[0072] It should be noted that, optionally, when the target container terminates its operation, it can be determined whether the target task is completed. If the target task is completed, the target container can be deleted. If the target task is not completed, the target container is retained to facilitate subsequent target containers to continue processing the target task.

[0073] In addition, optionally, when the target container terminates and it is determined that the target task is not completed, it can be determined that the target task processing is interrupted, and the reason for the interruption of the target task processing can be detected. If the reason for the interruption of the target task processing is the receipt of a control instruction input by the user for controlling the interruption of the target task processing, it is necessary to wait until the control instruction input by the user for controlling the continuation of the target task processing is received before continuing to process the target task through the target container; if the reason for the interruption of the target task processing is due to a target container failure, the progress node that the target container has completed processing can be recorded, and a backup container can be created according to the parameters of the target task, and the target task can be continued to be processed from the progress node through the backup container until the target task processing is completed.

[0074] It should be noted that, optionally, if the reason for the interruption of target task processing is a target container failure, you can wait for a preset period of time. If the target container failure is still not repaired after the preset period of time, you can create the above-mentioned backup container and continue to process the target task from the progress node through the backup container until the target task processing is completed.

[0075] Optionally, the usage information includes at least one of the following: the duration of computing resource usage and the time period of computing resource usage. Thus, since the usage information includes at least one of the following: the duration of computing resource usage and the time period of computing resource usage, the types of usage information are more diverse and the measurement results of computing resource usage information are more accurate.

[0076] It should be noted that after measuring the usage information of the above-mentioned computing power resources, when a task to be processed is received, and the similarity between the task to be processed and the target task is higher than the preset similarity, that is, the similarity between the task to be processed and the target task is high, then the usage information of the above-mentioned computing power resources can be obtained, and a container to be processed can be created based on the usage information of the above-mentioned computing power resources, and the above-mentioned container to be processed is used to process the above-mentioned task to be processed. In this way, the created container to be processed can be more compatible with the task to be processed, and the specification parameters of the created container to be processed can be maximized, thereby avoiding waste of specification parameters of the container to be processed.

[0077] In the present invention, through steps S1 to S3, the start running time of the target container is obtained, and, when the target container stops running, the end running time of the target container is obtained, and the usage information of the computing power resources corresponding to the target container in the target time period is measured. The target time period is the time period between the start running time and the end running time, thereby providing a method for measuring the computing power resources consumed in the target task processing process, that is, enhancing the measurement effect of the computing power resources consumed in the target task processing process.

[0078] Optionally, step S2 includes:

[0079] Step S21: selecting a target processor according to the target request, where the target processor includes at least one of the following: a CPU and a GPU;

[0080] Step S22: creating the target container according to the target processor, processing the target task through the target container, and obtaining the start running time of the target container.

[0081] Among them, selecting the target processor according to the target request can be understood as: the target request can carry information about the target task requested for processing, so that the target processor can be determined based on the information of the target task. For example, if the target task is a task for display, the selected target processor can be mainly the GPU. If the target task is a task for calculation, the selected target processor can be mainly the CPU. If the target task is a task for display and calculation, the selected target processor can include the CPU and the GPU.

[0082] Here, creating a target container according to a target processor can be understood as: creating a target container according to computing resources of the target processor, and the target container can call the computing resources of the target processor.

[0083] In the present invention, a target processor is selected according to a target request, and a target container is created according to the target processor. In this way, the created target container can better meet the requirements of processing the target task, that is, the accuracy of the created target container is higher.

[0084] Optionally, the target processor includes the CPU, and step S22 includes:

[0085] Step S221: Create the target container according to the CPU, and obtain first information of the CPU, where the first information includes the number of cores of the CPU;

[0086] Step S222: When the first information of the CPU is a first preset number, the target task is processed through the target container, and a start running time of the target container is obtained.

[0087] The number of CPU cores refers to the number of processing cores within the CPU, also known as the number of physical cores. Each core can independently handle computing tasks. The more cores there are, the stronger the CPU's parallel processing capabilities and the higher its performance.

[0088] It should be noted that the specific value of the first preset number is not limited here.

[0089] In the present invention, when the first information of the CPU is the first preset number, it can be indicated that the number of CPU cores has been verified correctly. Only after the number of CPU cores has been verified correctly, the target task is processed through the target container, and the start running time of the target container is obtained. In this way, the accuracy of processing the target task through the target container is improved, and the occurrence of the phenomenon of erroneous processing of the target task through the target container is avoided.

[0090] Optionally, the computing power resource includes the GPU, and step S22 includes:

[0091] Step S223: creating the target container according to the GPU, and obtaining second information of the GPU, where the second information includes the number and operating status of the GPU;

[0092] Step S224: When the number of the GPUs is a second preset number and the running state is a target state, the target task is processed through the target container, and a start running time of the target container is obtained.

[0093] Among them, the target state can be understood as a normal state, that is, when the operating state of the GPU is the target state, the GPU is in a normal state. For example: when the GPU is in a working state, the parameters of the GPU are the preset parameters of the GPU in the working state, which can indicate that the GPU is in a normal state.

[0094] In the present invention, when the number of GPUs is the second preset number and the operating status is the target status, it can be indicated that the number of GPUs and the operating status are verified to be correct. Only after the number of GPUs and the operating status are verified to be correct, the target task is processed through the target container, and the start operating time of the target container is obtained. In this way, the accuracy of processing the target task through the target container is also improved, and the occurrence of the phenomenon of erroneous processing of the target task through the target container is avoided.

[0095] Optionally, the usage information includes the usage duration of the computing resources. After step S3, the following steps are further included:

[0096] Step S4: Determine whether the usage time of the computing power resource is greater than the time corresponding to the target time period;

[0097] Step S5: When it is determined that the usage duration of the computing resources is greater than the duration corresponding to the target time period, the usage information of the computing resources corresponding to the target container within the target time period is re-measured.

[0098] Among them, since the target container is used to process the target task, the processing time of the target task must be less than or equal to the running time of the target container, and the running time of the target container is the time corresponding to the target time period, and the processing time of the target task can be understood as the usage time of the above-mentioned computing power resources. When the measured usage time of the computing power resources is greater than the time corresponding to the target time period, it can be determined that there must be an error in the measurement result.

[0099] In the present invention, when the usage duration of the computing power resources is greater than the duration corresponding to the target time period, it indicates that there is an obvious error in the measurement result of the usage duration of the computing power resources at this time, and it is necessary to re-measure the usage information of the computing power resources corresponding to the target container within the target time period, thereby improving the accuracy of the measurement result of the usage information of the computing power resources corresponding to the target container within the target time period.

[0100] See also Figure 2 , Figure 2 A schematic diagram of the structure of a computing power resource metering device for an intelligent computing center provided by the present invention is shown as follows: Figure 2 As shown, the computing power resource metering device 200 of the intelligent computing center includes:

[0101] Receiving module 201, used for receiving a target request sent by a target user, wherein the target request is used to request processing of a target task;

[0102] A processing module 202 is configured to create a target container according to the target request, process the target task through the target container, and obtain a start time of the target container.

[0103] The metering module 203 is used to obtain the termination time of the target container when the target container terminates its operation, and to measure the usage information of the computing resources corresponding to the target container within a target time period, where the target time period is the time period between the start time and the termination time.

[0104] Optionally, the processing module 202 includes:

[0105] A selection submodule, configured to select a target processor according to the target request, wherein the target processor includes at least one of the following: a CPU and a GPU;

[0106] The creation submodule is configured to create the target container according to the target processor, process the target task through the target container, and obtain a start execution time of the target container.

[0107] Optionally, the target processor includes the CPU, and the creation submodule includes:

[0108] A first creating unit is configured to create the target container according to the CPU and obtain first information of the CPU, where the first information includes the number of cores of the CPU;

[0109] The first processing unit is configured to process the target task through the target container and obtain a start running time of the target container when the first information of the CPU is a first preset number.

[0110] Optionally, the computing resource includes the GPU, and creating a submodule includes:

[0111] a second creating unit, configured to create the target container according to the GPU and obtain second information of the GPU, where the second information includes the number and operating status of the GPU;

[0112] The second processing unit is configured to process the target task through the target container and obtain a start running time of the target container when the number of the GPUs is a second preset number and the running state is a target state.

[0113] Optionally, the usage information includes at least one of the following: the usage duration of the computing power resources, and the usage time period of the computing power resources.

[0114] Optionally, the usage information includes the usage duration of the computing power resources. The computing power resource metering device 200 of the intelligent computing center further includes:

[0115] A determination module, configured to determine whether the usage duration of the computing power resource is greater than the duration corresponding to the target time period;

[0116] The re-metering module is used to re-meter the usage information of the computing power resources corresponding to the target container within the target time period when it is determined that the usage time of the computing power resources is greater than the time corresponding to the target time period.

[0117] The computing power resource metering device 200 for the intelligent computing center provided by the present invention can execute each step in the computing power resource metering method of the above-mentioned intelligent computing center, and thus has the same beneficial technical effects as the computing power resource metering method of the above-mentioned intelligent computing center, and the details will not be repeated here.

[0118] Please refer to Figure 3 The present invention also provides an electronic device 30, including a processor 31, a memory 32, and a computer program stored in the memory 32 and executable on the processor 31. When the computer program is executed by the processor 31, the various processes shown in the computing power resource metering method of the above-mentioned intelligent computing center are implemented, and the same technical effects can be achieved. To avoid repetition, they will not be described here.

[0119] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements each process of the above-mentioned intelligent computing center computing resource metering method and achieves the same technical effect. To avoid repetition, the computer-readable storage medium is not described here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0120] The present invention also provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the computing power resource measurement method of the intelligent computing center shown in the figure can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0121] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0122] Through the description of the above embodiments, those skilled in the art can clearly understand that the method provided by the above invention can be implemented by means of software plus the necessary general hardware platform, or of course by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the various methods provided by the present invention.

[0123] The present invention is described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A method for measuring computing power resources of an intelligent computing center, characterized in that: include: Step S1: receiving a target request sent by a target user, wherein the target request is used to request processing of a target task; Step S2: creating a target container according to the target request, processing the target task through the target container, and obtaining the start running time of the target container; Step S3: When the target container stops running, the termination time of the target container is obtained, and the usage information of the computing resources corresponding to the target container in the target time period is measured, where the target time period is the time period between the start running time and the termination running time.

2. The method according to claim 1, characterized in that The step S2 comprises: Step S21: selecting a target processor according to the target request, where the target processor includes at least one of the following: a CPU and a GPU; Step S22: creating the target container according to the target processor, processing the target task through the target container, and obtaining the start running time of the target container.

3. The method according to claim 2, characterized in that The target processor includes the CPU, and step S22 includes: Step S221: Create the target container according to the CPU, and obtain first information of the CPU, where the first information includes the number of cores of the CPU; Step S222: When the first information of the CPU is a first preset number, the target task is processed through the target container, and a start running time of the target container is obtained.

4. The method according to claim 2, characterized in that The computing power resource includes the GPU, and step S22 includes: Step S223: creating the target container according to the GPU, and obtaining second information of the GPU, where the second information includes the number and operating status of the GPU; Step S224: When the number of the GPUs is a second preset number and the running state is a target state, the target task is processed through the target container, and a start running time of the target container is obtained.

5. The method according to any one of claims 1 to 4, characterized in that The usage information includes at least one of the following: the usage duration of the computing power resources and the usage time period of the computing power resources.

6. The method according to claim 5, characterized in that The usage information includes the usage time of the computing resources. After step S3, the following steps are also included: Step S4: Determine whether the usage time of the computing power resource is greater than the time corresponding to the target time period; Step S5: When it is determined that the usage duration of the computing resources is greater than the duration corresponding to the target time period, the usage information of the computing resources corresponding to the target container within the target time period is re-measured.

7. A computing resource metering device for an intelligent computing center, characterized in that: include: A receiving module is used to receive a target request sent by a target user, wherein the target request is used to request processing of a target task; a processing module, configured to create a target container according to the target request, process the target task through the target container, and obtain a start running time of the target container; A metering module is configured to obtain, when the target container terminates operation, the termination time of the target container and measure usage information of computing resources corresponding to the target container within a target time period, where the target time period is the time period between the start time and the termination time.

8. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the method for measuring computing power resources of an intelligent computing center as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the computing power resource metering method for an intelligent computing center according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the computing power resource metering method of an intelligent computing center as described in any one of claims 1 to 6.

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