Method and device for monitoring execution state of computing power operation task of intelligent computing center in real time

Abstract

The invention provides a real-time monitoring method and device for the execution state of a computing power operation task of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures. Comprising the steps that S1, under the condition that a target request used for requesting deployment of a target model is received, the target model is deployed according to the target request, and the target model is used for executing a target operation task; s2, acquiring parameter information of the target model; and S3, determining a model deployment state of the target model and an execution state of the target operation task according to the parameter information. Therefore, a mode for monitoring the execution state of the computing power operation task of the intelligent computing center in real time is provided, namely, the monitoring effect on the execution state of the computing power operation task of the intelligent computing center is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical fields of intelligent computing centers, intelligent computing centers, and computing power infrastructure technologies, and particularly to a method and device for real-time monitoring of the execution status of computing power operation tasks in an intelligent computing center. Background Art

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

[0003] An "intelligent computing center" refers to a facility that uses large-scale heterogeneous computing power resources, including general computing power and intelligent computing power, and mainly provides the required computing power, data, and algorithms for artificial intelligence applications (such as scenarios of artificial intelligence deep learning model development, model training, and model inference, etc.). An intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from underlying computing power to top-level application enabling.

[0004] The "intelligent computing center" includes, but is not limited to, the "intelligent computing center".

[0005] An "intelligent computing center", that is, an artificial intelligence computing center, is a type of computing power infrastructure that is based on artificial intelligence theory, adopts an 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 centers" and "intelligent computing centers", and is the ability of computer devices or computing / data centers to process information. It is the ability of computer hardware and software to cooperate to jointly execute a certain computing requirement. It is the computing ability to achieve the output of the target result by processing information data. It is a new type of productive force that integrates information computing power, network carrying capacity, and data storage capacity, and mainly provides services to society through computing power infrastructure.

[0007] Currently, models can be deployed in an intelligent computing center to process related tasks, but there is currently a lack of a method for real-time monitoring of the execution status of computing power operation tasks in an intelligent computing center. It can be seen that since the emergence of intelligent computing centers, the lack of real-time monitoring of the execution status of computing power operation tasks is an urgent problem to be solved. Summary of the Invention

[0008] The present invention provides a method and device for real-time monitoring of the execution status of computing power operation tasks in an intelligent computing center, which is used to solve the problem of the lack of real-time monitoring of the execution status of computing power operation tasks.

[0009] To solve the above technical problems, the present invention is implemented as follows:

[0010] In a first aspect, the present invention provides a method for real-time monitoring of the execution status of computing power operation tasks in an intelligent computing center, including:

[0011] Step S1: When receiving a target request for requesting to deploy a target model, deploy the target model according to the target request, where the target model is used to execute a target running task;

[0012] Step S2: Obtain the parameter information of the target model;

[0013] Step S3: Determine the model deployment status and the target running task execution status of the target model according to the parameter information.

[0014] Optionally, the model deployment status includes: model not deployed status, model being deployed status, model deployment failed status, model deployment successful status or unavailable status.

[0015] Optionally, the model deployment successful status includes:

[0016] Model deployment is successful and not online status; or,

[0017] Model deployment is successful and online successful status; or,

[0018] Model deployment is successful and cancelled deployment status; or,

[0019] After model deployment is successful and online is successful, offline status.

[0020] Optionally, the model deployment status includes: model deployment failed status. After step S3, the method further includes:

[0021] Step S4: Obtain the deployment log of the target model;

[0022] Step S5: Determine the error information that causes the target model deployment to fail according to the deployment log.

[0023] Optionally, the error information is the information in the target deployment file, and the target deployment file is the information carried by the target request. After step S5, the method further includes:

[0024] Step S6: Correct the error information in the target deployment file;

[0025] Step S7: Redeploy the target model according to the target deployment file after correcting the error information.

[0026] Optionally, step S5 includes:

[0027] Step S51: Determine the error information that causes the target model deployment to fail according to the deployment log;

[0028] Step S52: Display the deployment log, and highlight the target information in the deployment log, where the target information includes at least one of the following: the storage location, name, and byte length of the error information in the target deployment file;

[0029] Among them, the highlighting method includes at least one of the following: highlighting with a preset font, highlighting with a preset size, highlighting with a floating window, and highlighting with a display box.

[0030] In a second aspect, the present invention provides a real-time monitoring device for the execution status of computing power operation tasks in an intelligent computing center, including:

[0031] A deployment module, configured to deploy the target model according to the target request when receiving a target request for requesting to deploy the target model, where the target model is used to execute a target operation task;

[0032] A first acquisition module, configured to acquire parameter information of the target model;

[0033] A first determination module, configured to determine the deployment status of the target model and the execution status of the target operation task according to the parameter information.

[0034] In a third aspect, the present invention provides an electronic device, including: a processor, a memory, and a program stored on the memory and executable on the processor, where when the program is executed by the processor, the steps of the real-time monitoring method for the execution status of computing power operation tasks in the intelligent computing center as described in the first aspect above are implemented.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the real-time monitoring method for the execution status of computing power operation tasks in the intelligent computing center as described in the first aspect above are implemented.

[0036] In a fifth aspect, the present invention provides a computer program product, including computer instructions, and when the computer instructions are executed by a processor, the steps of the real-time monitoring method for the execution status of computing power operation tasks in the intelligent computing center as described in the first aspect above are implemented.

[0037] In the present invention, when receiving a target request for requesting to deploy a target model, the target model is deployed according to the target request, where the target model is used to execute a target operation task; parameter information of the target model is acquired; and the model deployment status of the target model is determined according to the parameter information.

[0038] In this way, determining the model deployment status and the target operation task execution status of the target model according to the obtained parameter information of the target model provides a way to monitor the execution status of the computing power operation task in the intelligent computing center in real time, that is, it enhances the monitoring effect of the execution status of the computing power operation task in the intelligent computing center. Brief Description of the Drawings

[0039] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0040] Figure 1 It is a schematic flowchart of a method for real-time monitoring of the execution status of the computing power operation task in the intelligent computing center provided by the present invention;

[0041] Figure 2 It is a schematic structural diagram of a device for real-time monitoring of the execution status of the computing power operation task in the intelligent computing center provided by the present invention;

[0042] Figure 3 It is a schematic structural diagram of an electronic device provided by the present invention. Detailed Embodiments

[0043] Next, the technical solutions in the present invention will be clearly and completely described in conjunction with the drawings in the present invention. Obviously, the described content is part of the present invention, rather than all of the content. Based on the content in the present invention, all other content obtained by those of ordinary skill in the art without creative efforts belongs to the scope of protection of the present invention. The "computing power" described in the present invention refers to: the ability of a computer device or a computing / data center to process information, the ability of computer hardware and software to cooperate to jointly execute a certain computing requirement, the computing ability to output a target result by processing information data, a new type of productive force integrating information computing power, network carrying capacity, and data storage capacity, and mainly providing services to society through computing power infrastructure.

[0044] The "Computational Power (CP)" as described in the present invention refers to: the ability of a data center server to process data and output results, which is a comprehensive indicator for measuring the computing power of a data center and includes general computing power, supercomputing power, and intelligent computing power. The commonly used measurement unit is the number of floating-point operations per second (FLOPS, 1 EFLOPS = 10^18 FLOPS). The larger the value, the stronger the comprehensive computing power. It is estimated that 1 EFLOPS is approximately the computing power output of 5 Tianhe-2A or 500,000 mainstream server CPUs or 2 million mainstream laptops. The calculation formula is: CP = CP 通用 + CP 智能 + CP 超级 。

[0045] The "Network Power (NP)" as described in the present invention refers to: the manifestation of the data transmission ability of computing power facilities, which is a comprehensive ability including network architecture, network bandwidth, transmission delay, intelligent management and scheduling, etc., and involves network transmission within and between data centers, and is a comprehensive indicator for measuring network transmission scheduling ability.

[0046] The "Storage Power (SP)" as described in the present invention refers to: the comprehensive ability of a data center in four aspects: data storage capacity, performance, security and reliability, and green and low-carbon, which is a comprehensive indicator for measuring the data storage ability of a data center and includes external storage devices such as storage arrays and server internal storage devices. The commonly used measurement unit for storage capacity is exabyte (EB, 1 EB = 2^60 bytes), the commonly used measurement unit for performance is the number of read and write operations per second per unit capacity (IOPS / TB, Input / Output Operations Per Second / TB), and the disaster recovery ratio is an important manifestation of security and reliability.

[0047] The "computing power infrastructure" as described in the present invention refers to: a new type of information infrastructure that integrates information computing power, network carrying power, and data storage power, and can realize the centralized computing, storage, transmission, and application of information.

[0048] The "new type of information infrastructure" as described in the present invention mainly includes network infrastructures such as 5G networks, fiber broadband networks, backbone networks, international communication networks, and satellite Internet, computing power infrastructures such as data centers, general computing power centers, intelligent computing centers, and supercomputing centers, and new technology infrastructures such as artificial intelligence, blockchain, and quantum computing.

[0049] The "computing power" as described in the present invention includes: general computing power, intelligent computing power, and supercomputing power.

[0050] The "general computing power" described 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.

[0051] The "intelligent computing power" described in the present invention refers to: for various artificial intelligence innovation applications, a computing platform deployed on a large scale based on dedicated chips such as GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), and ASIC (Application Specific Integrated Circuit), such as natural language processing, machine vision, and so on.

[0052] The "super computing power" described in the present invention refers to: mainly 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 processes extremely complex or data-intensive problems through a dedicated operating system, mainly used for computing in cutting-edge scientific fields, such as planetary simulation, drug molecule design, gene analysis, etc.

[0053] The "intelligent computing center" described in the present invention refers to: a facility that provides the required computing power, data, and algorithms mainly for artificial intelligence applications (such as scenarios like artificial intelligence deep learning model development, model training, and model inference) by using large-scale heterogeneous computing power resources, including general computing power (CPU) and intelligent computing power (GPU, FPGA, ASIC, etc.). The intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from underlying computing power to top-level application enabling.

[0054] The "intelligent computing center" described in the present invention includes but is not limited to the "intelligent computing center".

[0055] The "intelligent computing center" described in the present invention, that is, the artificial intelligence computing center, is a type of computing power infrastructure based on artificial intelligence theory, adopting an artificial intelligence computing architecture, and providing computing power services, data services, and algorithm services required for artificial intelligence applications.

[0056] The "computing power center" described in the present invention refers to: a facility mainly composed of infrastructure such as wind, fire, water, and electricity and IT software and hardware devices, with computing power, transportation power, and storage power, including general data centers, intelligent computing centers, supercomputing centers, etc.

[0057] The "supercomputing center" described in the present invention refers to: namely, a supercomputing data center, which is a data center based on supercomputers or large-scale computing clusters and can provide functions such as large-scale computing, storage, and network services, and is widely used in application scenarios such as aerospace, national defense, oil exploration, climate modeling, and genome sequencing.

[0058] The "computing power resources" described in the present invention refers to: technologies and facilities with information computing, transmission, storage, and application capabilities required for the development of the 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 support and guarantee resources such as wind, fire, water, and electricity.

[0059] The "models" described in the present invention include but are not limited to "large language models" and "multimodal large models".

[0060] The "large language model" described in the present invention refers to a large language model (LLM), which is a language model with a relatively large number of parameters, aiming to understand and generate human language, trained with a large amount of text data, and can perform a wide range of tasks including text summarization, translation, sentiment analysis, etc.

[0061] The "multimodal large model" (Multimodal Large Models) described in the present invention refers to: a model trained by jointly combining multimodal information such as text, images, videos, and audio, including but not limited to multimodal large language models.

[0062] The "computing power operation task" described in the present invention refers to: a specific workload or job that is executed on computing power resources and requires a certain amount of computing power support, usually involving scenarios such as complex data processing, numerical calculation, model training, or simulation.

[0063] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for real-time monitoring of the execution status of computing power operation tasks in an intelligent computing center provided by the present invention. As Figure 1 shown, it includes the following steps:

[0064] Step S1: When receiving a target request for requesting the deployment of a target model, deploy the target model according to the target request, and the target model is used to execute a target operation task;

[0065] Among them, the present invention can be applied to electronic devices in an intelligent computing center, and the specific types of the above-mentioned electronic devices are not limited here. Optionally, the above-mentioned electronic device can be a server in the intelligent computing center, and this server can also be referred to as a computing power platform.

[0066] Among them, the specific target running task is not limited here. Optionally, the target running task can be model inference or model calculation. The above-mentioned model inference can refer to inputting existing data into the model, and the model makes inferences based on the existing data to output an inference result. For example, the specific application scenario of the above-mentioned model inference can be to output the demand for a graphics processing unit in a future period of time; the above-mentioned model calculation can refer to inputting existing data into the model, and the model makes inferences based on the existing data to output a calculation result. For example, the specific application scenario of the above-mentioned model calculation can be to calculate the power consumption in a certain area during a certain period of time.

[0067] Step S2: Obtain the parameter information of the target model;

[0068] Among them, the specific types of the parameter information of the target model are not limited here. Optionally, the parameter information of the target model can be information used to represent the current state of the target model. For example, the above-mentioned parameter information can include at least one of the following: the utilization rate of the computing power resources of the target model, the scheduling information of the computing power resources of the target model, the deployment log information of the target model, etc.

[0069] Step S3: Determine the model deployment state of the target model and the execution state of the target running task according to the parameter information.

[0070] Among them, when the parameter information includes the utilization rate of the computing power resources of the target model, determining the model deployment state of the target model according to the parameter information can be understood as:

[0071] When the utilization rate of the computing power resources of the target model is greater than the preset utilization rate, it indicates that the utilization rate of the computing power resources of the target model is relatively high at this time, that is, the target model is more likely to schedule the computing power resources to execute the target running task. Therefore, it can be determined that the target model is in the model deployment state or the model deployment success state;

[0072] When the utilization rate of the computing power resources of the target model is less than or equal to the preset utilization rate, it indicates that the utilization rate of the computing power resources of the target model is relatively low at this time, that is, the target model is less likely to schedule the computing power resources to execute the target running task. Therefore, it can be determined that the target model is in the model deployment failure state, the model not deployed state or the unavailable state.

[0073] Optionally, when the utilization rate of the computing power resources of the target model is greater than the preset utilization rate, it is also possible to monitor the change in the utilization rate of the computing power resources of the target model within the target time period. If the utilization rate of the computing power resources of the target model gradually increases within the target time period, it indicates that the demand for the computing power resources of the target model is increasing. Then, the possibility that the target model is executing the target running task is relatively high at this time, and it can be determined that the target model is in the model deployment success state. If the utilization rate of the computing power resources of the target model gradually decreases within the target time period, it indicates that the deployment of the target model is approaching completion, but the target running task has not been executed yet. Then, it can be determined that the target model is in the model deployment in-progress state.

[0074] It should be noted that when the above parameter information includes the scheduling information of the computing power resources of the target model, the utilization rate of the computing power resources of the target model can be calculated first according to the scheduling information of the computing power resources of the target model, and then the model deployment state and the target running task execution state of the target model can be determined according to the utilization rate of the computing power resources of the target model. For specific expressions, reference can be made to the above content.

[0075] It should be noted that when the above parameter information includes the deployment log information of the target model, since the specific state of the target model and the target running task execution state at different time points can be recorded in the deployment log information, the model deployment state and the target running task execution state of the target model can also be accurately determined.

[0076] In the present invention, through steps S1 to S3, the model deployment state of the target model is determined according to the obtained parameter information of the target model, and the execution state of the monitored target running task is provided, that is, a method for real-time monitoring of the execution state of the computing power running task in the intelligent computing center is provided, which enhances the monitoring effect of the execution state of the computing power running task in the intelligent computing center.

[0077] It should be noted that the specific type of the model deployment state is not limited here. Optionally, the model deployment state may include the model deployment success state or the model deployment failure state.

[0078] Optionally again, the model deployment state includes: the model not deployed state, the model deployment in-progress state, the model deployment failure state, the model deployment success state, or the unavailable state. In this way, the diversity of the model deployment state is increased, thereby further refining the specific states included in the model deployment state, and further increasing the accuracy of the model deployment state monitoring result.

[0079] Among them, the model not deployed state can be understood as the state where the target model has not been started for deployment. The model being deployed state can be understood as the state where the target model is in the process of deployment but has not been deployed successfully yet. The model deployment failure state can be understood as the state where the target model deployment fails. The model deployment success state can be understood as the state where the target model is deployed successfully. The unavailable state can be understood as the state where the target model is unavailable.

[0080] Optionally, the model deployment success state includes:

[0081] The model is deployed successfully and not yet online; or,

[0082] The model is deployed successfully and is online successfully; or,

[0083] The model is deployed successfully and the deployment is cancelled; or,

[0084] After the model is deployed successfully and is online successfully, it is taken offline again.

[0085] Among them, the model is deployed successfully and not yet online can be understood as the target model is deployed successfully but has not been online yet, so the target running task cannot be executed. The model is deployed successfully and is online successfully can be understood as the target model is deployed successfully and has been online successfully, and at this time, the target running task can be started. The model is deployed successfully and the deployment is cancelled can be understood as: after the model is deployed successfully, it is in the state of being cancelled during deployment, and at this time, the target running task cannot be executed either. After the model is deployed successfully and is online successfully, it is taken offline again can be understood as after the model is deployed successfully, it is first online successfully and then taken offline again, and at this time, the target running task cannot be executed either.

[0086] It should be noted that when determining the specific states included in the above model deployment success state, it can be specifically determined in combination with the deployment log information corresponding to the target model. The deployment log information can record the specific states of the target model at different time points. In this way, the specific states included in the model deployment success state and the execution state of the target running task can be accurately determined.

[0087] In the present invention, the diversity of the model deployment success state is increased, thereby further refining the specific states included in the model deployment success state, and further increasing the accuracy of the monitoring result of the model deployment success state.

[0088] It should be noted that after the target model is in the model deployment success state, the service state of the target model can also be displayed, that is, the real-time state of the target model executing the target running task. In this way, the execution state of the target running task can be obtained timely and accurately. Optionally, the real-time state of the above target running task can be displayed in real time.

[0089] Optionally, the model deployment status includes: a model deployment failure status. After step S3, the method further includes:

[0090] Step S4: Obtain the deployment log of the target model;

[0091] Step S5: Determine the error information that causes the deployment failure of the target model according to the deployment log.

[0092] Among them, the above deployment log can be understood as the above deployment log information, and the deployment log can be used to record the specific status of the target model at different time points during the deployment process of the target model.

[0093] In the present invention, the error information that causes the deployment failure of the target model can be determined according to the deployment log, so that the above error information can be accurately obtained to achieve the purpose of prompting the user.

[0094] It should be noted that after determining the above error information, a target operation can also be performed according to the error information. The target operation can include analyzing and correcting the error information, etc.

[0095] Optionally, the error information is the information in the target deployment file, and the target deployment file is the information carried by the target request. After step S5, the method further includes:

[0096] Step S6: Correct the error information in the target deployment file;

[0097] Step S7: Redeploy the target model according to the target deployment file after correcting the error information.

[0098] Among them, the specific method of correcting the error information in the target deployment file is not limited herein. Optionally, the target case information with a matching degree higher than the preset matching degree with the error information can be found from the database, and the above error information can be corrected according to the correction method corresponding to the target case information. In this way, the correction efficiency of the error information can be improved; alternatively, the input of the user can also be received, and the above error information can be corrected according to the input of the user. In this way, the correction accuracy of the above error information can be improved.

[0099] In the present invention, the error information in the target deployment file can be corrected first, and then the target model can be redeployed according to the target deployment file after correcting the error information, so that the successful deployment of the target model can be ensured, and the deployment effect of the target model is enhanced.

[0100] Optionally, step S5 includes:

[0101] Step S51: Determine the error information that causes the deployment failure of the target model according to the deployment log;

[0102] Step S52: Display the deployment log, and highlight the target information in the deployment log, where the target information includes at least one of the following: the storage location, name, and byte length of the error information in the target deployment file;

[0103] Among them, the highlighting method includes at least one of the following: highlighting with a preset font, highlighting with a preset size, highlighting with a floating window, and highlighting with a display box.

[0104] Among them, the target information includes at least one of the following: the storage location, name, and byte length of the error information in the target deployment file. In this way, the positioning efficiency and accuracy of the above error information can be improved through the above target information.

[0105] Among them, highlighting with a preset font can be understood as: the error information can be displayed in a preset font, while the information other than the error information in the target deployment file can be displayed in other fonts, and the other fonts are different from the preset font. The above preset font can include at least one of the following: red font, blue font, bold font, etc.

[0106] Among them, highlighting with a preset size can be understood as: the error information can be displayed in a preset size, while the information other than the error information in the target deployment file can be displayed in other sizes, and the other sizes are different from the preset size. For example, the preset size can be larger than the other sizes, that is, the above error information can be enlarged and displayed.

[0107] Among them, highlighting with a floating window can be understood as: the error information can be displayed in the form of a floating window, while the information other than the error information in the target deployment file can be displayed in a non-floating window form. When the floating window is displayed, the error information can achieve the effect of floating above the information other than the error information in the target deployment file.

[0108] Among them, highlighting with a display box can be understood as: the display box can enclose the error information, that is, the error information is displayed inside the display box, while the information other than the error information in the target deployment file can be displayed outside the error information.

[0109] In the present invention, when displaying the deployment log, the target information in the deployment log can be highlighted. In this way, the effect of prompting the user of the above error information can be achieved, so that the above error information can be corrected in time, and the efficiency of redeploying the target model can be improved.

[0110] See Figure 2 , Figure 2 which is a schematic structural diagram of a real-time monitoring device for the execution status of computing power operation tasks in an intelligent computing center provided by the present invention, as Figure 2As shown, the real-time monitoring device 200 for the computing power operation task execution status of the intelligent computing center includes:

[0111] A deployment module 201, configured to deploy the target model according to the target request when receiving the target request for requesting to deploy the target model, where the target model is used to execute the target operation task;

[0112] A first acquisition module 202, configured to acquire the parameter information of the target model;

[0113] A first determination module 203, configured to determine the model deployment status and the target operation task execution status of the target model according to the parameter information.

[0114] Optionally, the model deployment status includes: model not deployed status, model deploying status, model deployment failure status, model deployment success status or unavailable status.

[0115] Optionally, the model deployment success status includes:

[0116] Model deployed successfully and not yet online status; or,

[0117] Model deployed successfully and online successfully status; or,

[0118] Model deployed successfully and cancelled deployment status; or,

[0119] After the model is deployed successfully and online successfully, the offline status.

[0120] Optionally, the model deployment status includes: model deployment failure status. The real-time monitoring device 200 for the computing power operation task execution status of the intelligent computing center further includes:

[0121] A second acquisition module, configured to acquire the deployment log of the target model;

[0122] A second determination module, configured to determine the error information that causes the deployment failure of the target model according to the deployment log.

[0123] Optionally, the error information is the information in the target deployment file, and the target deployment file is the information carried by the target request. The real-time monitoring device 200 for the computing power operation task execution status of the intelligent computing center further includes:

[0124] A correction module, configured to correct the error information in the target deployment file;

[0125] A redeployment module, configured to redeploy the target model according to the target deployment file after correcting the error information.

[0126] Optionally, the second determination module includes:

[0127] A determination sub-module, configured to determine error information that causes the deployment of the target model to fail according to the deployment log;

[0128] A display sub-module, configured to display the deployment log and highlight target information in the deployment log, where the target information includes at least one of the following: the storage location, name, and byte length of the error information in the target deployment file;

[0129] Wherein, the highlighting method includes at least one of the following: highlighting with a preset font, highlighting with a preset size, highlighting with a floating window, and highlighting with a display box.

[0130] The real-time monitoring device 200 for the execution status of the computing power operation task in the intelligent computing center provided by the present invention can execute each step in the above-mentioned real-time monitoring method for the execution status of the computing power operation task in the intelligent computing center, and thus has the same beneficial technical effects as the above-mentioned real-time monitoring method for the execution status of the computing power operation task in the intelligent computing center, which will not be elaborated herein for the sake of brevity.

[0131] 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 on the memory 32 and executable on the processor 31. When the computer program is executed by the processor 31, it realizes each process shown in the above-mentioned real-time monitoring method for the execution status of the computing power operation task in the intelligent computing center, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0132] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes each process of the above-mentioned real-time monitoring method for the execution status of the computing power operation task in the intelligent computing center, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here. Among them, the computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0133] The present application also provides a computer program product, including computer instructions, which when executed by a processor, realize each process of the above-mentioned Figure 1 shown real-time monitoring method for the execution status of the computing power operation task in the intelligent computing center, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0134] It should be noted that in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including such element.

[0135] From 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 a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence 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 several instructions for causing 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.

[0136] The present invention has been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims, and all of them belong to the protection scope of the present invention.

Claims

1. A real-time monitoring method for the execution status of computing power operation tasks in an intelligent computing center, characterized in that, Including: Step S1: When receiving a target request for requesting to deploy a target model, deploy the target model according to the target request, where the target model is used to execute a target running task; Step S2: Obtain parameter information of the target model; Step S3: Determine the model deployment status of the target model and the execution status of the target running task according to the parameter information.

2. The method according to claim 1, characterized in that, The model deployment status includes: model not deployed status, model deploying status, model deployment failed status, model deployment successful status or unavailable status.

3. The method according to claim 2, wherein The model deployment successful status includes: Model deployment is successful and not online status; or, Model deployment is successful and online successful status; or, Model deployment is successful and cancelled deployment status; or, After model deployment is successful and online is successful, offline status.

4. The method according to any one of claims 1 to 3, characterized in that, The model deployment status includes: model deployment failed status. After step S3, the method further includes: Step S4: Obtain the deployment log of the target model; Step S5: Determine error information that causes the target model deployment to fail according to the deployment log.

5. The method according to claim 4, characterized in that, The error information is information in a target deployment file, and the target deployment file is information carried by the target request. After step S5, the method further includes: Step S6: Correct the error information in the target deployment file; Step S7: Redeploy the target model according to the target deployment file after correcting the error information.

6. The method according to claim 5, wherein Step S5 includes: Step S51: Determine error information that causes the target model deployment to fail according to the deployment log; Step S52: Display the deployment log and highlight target information in the deployment log, where the target information includes at least one of the following: the storage location, name, and byte length of the error information in the target deployment file; Wherein, the highlighting method includes at least one of the following: highlighting with a preset font, highlighting with a preset size, highlighting with a floating window, highlighting with a display box.

7. A real-time monitoring device for the execution status of computing power operation tasks in an intelligent computing center, characterized in that, Including: A deployment module, configured to deploy a target model according to a target request when receiving a target request for requesting to deploy the target model, where the target model is used to execute a target running task; A first obtaining module, configured to obtain parameter information of the target model; A first determining module, configured to determine the model deployment status of the target model and the execution status of the target running task according to the parameter information.

8. An electronic device, characterized in that, Including: A processor, a memory, and a program stored on the memory and executable on the processor. When the program is executed by the processor, the steps of the method for real-time monitoring of the execution status of the computing power running task of the intelligent computing center as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps of the method for real-time monitoring of the execution status of the computing power running task of the intelligent computing center as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that, Including computer instructions, when the computer instructions are executed by a processor, the steps of the method for real-time monitoring of the execution status of the computing power operation task of the intelligent computing center as described in any one of claims 1 to 6 are implemented.

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