Artificial intelligence platform, capacity expansion updating method and device and electronic equipment

Abstract

The invention discloses an artificial intelligence platform, a capacity expansion updating method and device and electronic equipment, and relates to the technical field of artificial intelligence. Various expansion files, namely a plurality of replacement files, of a target processor and processor files related to the target processor are copied into a fixed directory through an updating module, and then under the condition that various expansion files of the target processor exist in the fixed directory, plug-in services corresponding to the target processor can be started. Therefore, after the target processor is expanded in the artificial intelligence platform, the function corresponding to the target processor can be normally operated, the expansion of the target processor is completed, the expansion process of the node of the GPU which does not support the model can be quickly updated under the condition of not leaving the field and being compatible, and the expansion efficiency of the GPU is improved. And the technical effect of expanding the capacity of the nodes of the GPU which does not support the model can be ensured.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to an artificial intelligence platform and a method, device, and electronic device for expansion and updating. Background Art

[0002] Nowadays, both AI training and reasoning tasks require the participation of graphics processing units (GPUs). Therefore, in computer clusters, computers with graphics processing units are a necessary condition. When the GPU of a user's computer cluster changes, it is necessary to expand the capacity of the AI ​​platform through expansion technology to support the training and reasoning tasks of the computer cluster.

[0003] However, when the user's computer cluster adopts a new GPU, the new GPU may not be in the support list when the artificial intelligence platform is installed. The existing expansion technology cannot expand the nodes of unsupported GPU models and cannot be applied to scenarios where the on-site node environment changes, resulting in a poor user experience. Summary of the invention

[0004] The present application provides an artificial intelligence platform and a capacity expansion and update method, device, and electronic device to at least solve the problem that the existing capacity expansion technology in the related technology cannot expand the nodes of unsupported GPU models and cannot be applied to the scenario of on-site node environment changes, resulting in a poor user experience.

[0005] This application provides an artificial intelligence platform, including: a management node, The management node includes update modules, package modules, and databases; The update module is used to obtain the processor file of the target processor from the software package module and obtain multiple replacement files corresponding to the target processor from the database; The update module is also used to copy the multiple replacement files and the processor files to the fixed directories corresponding to the multiple replacement files and the processor files respectively; wherein the management node also includes multiple fixed directories; The update module is also used to start the plug-in service of the target processor by executing the plug-in startup file when there are multiple corresponding replacement files and processor files in multiple fixed directories; wherein the processor file at least includes the plug-in startup file; The management node is used to expand the target processor to the artificial intelligence platform based on the plug-in service after startup.

[0006] The present application provides a capacity expansion and updating method, which is applied to the above-mentioned artificial intelligence platform and includes: Obtaining a processor file of a target processor and a plurality of replacement files corresponding to the target processor; wherein the plurality of replacement files are pre-acquired files for expanding the capacity of the target processor; Copying the multiple replacement files and processor files corresponding to the target processor to the fixed directories corresponding to the multiple replacement files and the processor files, respectively; wherein the fixed directory is a pre-set directory for storing files; In the case where there are multiple corresponding replacement files and processor files in multiple fixed directories, the plug-in service of the target processor is started by executing the plug-in startup file; wherein the processor file at least includes the plug-in startup file; Based on the plug-in service after startup, the target processor is expanded to the artificial intelligence platform.

[0007] The present application also provides a capacity expansion and updating device, comprising: An acquisition unit, used to acquire a processor file of a target processor and a plurality of replacement files corresponding to the target processor; wherein the plurality of replacement files are pre-acquired files used to expand the capacity of the target processor; A copying unit, used to copy a plurality of replacement files and a processor file corresponding to a target processor to fixed directories corresponding to the plurality of replacement files and the processor file, respectively; wherein the fixed directory is a pre-set directory for storing files; A startup unit, configured to start the plug-in service of the target processor by executing the plug-in startup file when there are multiple corresponding replacement files and processor files in multiple fixed directories; wherein the processor file at least includes the plug-in startup file; The expansion unit is used to expand the target processor to the artificial intelligence platform based on the plug-in service after startup.

[0008] The present application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing the steps of any of the above-mentioned expansion and update methods when executing the computer program.

[0009] The present application also provides a computer-readable storage medium, in which a computer program is stored, wherein when the computer program is executed by a processor, the steps of any of the above-mentioned expansion and update methods are implemented.

[0010] The present application also provides a computer program product, including a computer program, which implements the steps of any of the above-mentioned expansion and update methods when the computer program is executed by a processor.

[0011] Through an artificial intelligence platform and expansion and updating method, device, and electronic device of the present application, the expansion process is updated in the artificial intelligence platform. Various expansion files of the target processor, i.e., multiple replacement files and processor files related to the target processor are copied to a fixed directory through an update module. After that, various expansion files of the target processor exist in the fixed directory, and the plug-in service corresponding to the target processor can be started to ensure that after the target processor is expanded in the artificial intelligence platform, the functions corresponding to the target processor can be run normally to complete the expansion of the target processor. Therefore, the technical problem that the existing expansion technology cannot expand the nodes of unsupported GPU models and cannot be applied to the scenario of field node environment changes, resulting in poor user experience, can be solved, and the expansion process of the nodes of unsupported GPU models can be quickly updated without field compatibility, so as to ensure that the nodes of unsupported GPU models can be expanded. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0013] Figure 1 A schematic diagram of the structure of an artificial intelligence platform provided in an embodiment of the present application; Figure 2 A schematic diagram of modules in a management node provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of another artificial intelligence platform provided in an embodiment of the present application; Figure 4 A flowchart of a capacity expansion and updating method provided in an embodiment of the present application; Figure 5 A schematic diagram of the structure of a capacity expansion and updating device provided in an embodiment of the present application; Figure 6 A schematic diagram of the structure of another capacity expansion and updating device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] It should be noted that, in the description of this application, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence.

[0016] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0017] In conjunction with the specific application environment architecture or specific hardware architecture on which the execution of the expansion and update method depends, the specific application environment architecture or specific hardware architecture is described herein.

[0018] Figure 1 This is a structural diagram of an artificial intelligence platform provided in this application, and the execution of the expansion and update method depends on the artificial intelligence platform.

[0019] like Figure 1 As shown, the artificial intelligence platform includes: a management node 11, The management node 11 includes an update module 111, a software package module 112 and a database 113; The update module 111 is used to obtain the processor file of the target processor from the software package module 112, and to obtain multiple replacement files corresponding to the target processor from the database 113; The update module 111 is also used to copy the multiple replacement files and the processor files to the fixed directories corresponding to the multiple replacement files and the processor files respectively; wherein the management node 11 also includes multiple fixed directories; The update module 111 is also used to start the plug-in service of the target processor by executing the plug-in startup file when there are multiple corresponding replacement files and processor files in multiple fixed directories; wherein the processor file includes the plug-in startup file; The management node 11 is used to expand the target processor to the artificial intelligence platform based on the plug-in service after startup.

[0020] Among them, the artificial intelligence platform is an artificial intelligence training platform, which is used to realize the artificial intelligence training and reasoning tasks of computer clusters. The artificial intelligence platform is deployed in a computer cluster composed of a large number of computers (referred to as nodes), generally including one or an odd number (3 or 5) management nodes 11, and the rest are computing nodes 12. The management node 11 deploys resource management services, databases, cluster monitoring, user management services and image warehouses (harbor) (pre-set image file database), etc. The computing node 12 deploys node services, etc. The computing node 12 usually runs a development environment, which is the infrastructure provided to users for artificial intelligence training. The main functions of the node service include but are not limited to: collecting node information, reporting cluster monitoring, resource management and other services. Users can perform artificial intelligence training tasks on the artificial intelligence platform. Specifically, for the relationship between the update module 111 and the software package module 112, please refer to Figure 2 , Figure 2 This is a schematic diagram of modules in a management node provided by the present application, wherein an update module 111 and a software package module 112 are directly stored in the management node 11 .

[0021] An AI platform is a service platform that provides tools, resources, and environments to build, train, and deploy AI models. The platform typically includes the following key functions and features: Data cleaning: helps remove errors and duplicate information from data; Data conversion: converts data into a format suitable for model training; Data enhancement: increases sample diversity and improves model generalization capabilities by transforming data.

[0022] Model building: Provides various machine learning and deep learning frameworks, such as TensorFlow, PyTorch (PythonTorch), Keras and other common tools in the field of deep learning; Programming environment: Provides online code editors, Jupyter notebooks (an interactive computing platform that allows users to mix code, text, mathematical formulas, images, videos and other elements in the same document) or other integrated development environments (IDE). Version control: Supports version management and collaboration of models; Model training: Computing resources: Provide powerful GPU or tensor processing unit (TPU) computing resources to accelerate model training; Automatic parameter adjustment: Use automated tools (such as Hyperparameter tuning) to optimize model parameters; Training monitoring: Real-time monitoring of the training process, including loss function, accuracy and other indicators.

[0023] Evaluation indicators: Provides various evaluation indicators, such as accuracy, recall, F1 score, etc. Cross-validation: Supports different cross-validation methods to evaluate the generalization ability of the model; Model deployment: Supports deploying the model as an Application Programming Interface (API) service, or integrating it into existing applications; Management and monitoring: Provides management and monitoring tools during model runtime to ensure stable operation of the model; Team collaboration: Supports multi-person collaboration, sharing of data and models; Community sharing: Allows users to share models and experiences to promote knowledge exchange.

[0024] It should be noted that the fixed directory is a directory pre-set in the management node for storing files. The update module 111 copies the pre-acquired replacement files, driver data and plug-in files to the pre-set fixed directory. The replacement files are used to expand or upgrade the target processor. The setting of the fixed directory helps to standardize the file storage location for easy management and access.

[0025] The image plug-in is the k8s device plug-in image (deviceplugin, dp) of the target processor, which can be obtained directly from the manufacturer of the target processor. The image plug-in is an extension of Kubernetes for the target processor, which helps Kubernetes manage and schedule special hardware resources in the cluster, namely the target processor. The image plug-in ensures that resources can be used by Pods (the most basic operating unit in Kubernetes) and provides a mechanism for resource isolation and allocation. Among them, Kubernetes (k8s) is an open source for managing containerized applications on multiple hosts in the cloud platform. The goal of Kubernetes is to make the deployment of containerized applications simple and efficient. Kubernetes provides a mechanism for application deployment, planning, updating, and maintenance.

[0026] Through an artificial intelligence platform of the present application, the expansion process is updated in the artificial intelligence platform. Various expansion files of the target processor, i.e., multiple replacement files and processor files related to the target processor are copied to a fixed directory through an update module. After that, various expansion files of the target processor exist in the fixed directory, and the plug-in service corresponding to the target processor can be started to ensure that after the target processor is expanded in the artificial intelligence platform, the functions corresponding to the target processor can be run normally to complete the expansion of the target processor. Therefore, the technical problems that the existing expansion technology cannot expand the nodes of unsupported GPU models and cannot be applied to the scenario of field node environment changes, resulting in poor user experience, can be solved, and the expansion process of the nodes of unsupported GPU models can be quickly updated without field compatibility, ensuring that the nodes of unsupported GPU models can be expanded.

[0027] In an achievable embodiment of the present application, the updating module 111 is further configured to: The plug-in image in the processor file is subjected to label modification processing to obtain a modified plug-in image, and the modified plug-in image is pushed to a preset image file database; wherein the management node also includes a preset image file database, and the processor file includes at least a plug-in image, driver data, and a plug-in file.

[0028] In the embodiment of the present application, the plug-in image includes but is not limited to metadata, wherein a tag is a string used to identify the plug-in image, including but not limited to version number, build date, environment identifier, etc. Tag modification processing refers to editing the metadata of the plug-in image to update or change the identification information of the image.

[0029] The preset image file database is a database dedicated to storing and managing plug-in images, such as the image repository (harbor).

[0030] In an achievable embodiment of the present application, the updating module 111 is further configured to: Copy the plug-in deployment serialization file, the plug-in startup file and the plug-in stop file to the fixed directory corresponding to the plug-in file according to the preset control instruction; wherein the plug-in file at least includes the plug-in deployment serialization file, the plug-in startup file and the plug-in stop file; The driver data is copied to a fixed directory corresponding to the driver data according to the preset control instructions; wherein the driver data is used to add the driver of the target processor in the preset server after the artificial intelligence platform is installed in the preset server.

[0031] Among them, the main function of the update module 111 is to execute the update of the entire expansion process. The update module 111 stores preset control instructions and various replacement files. The preset control instructions are some custom-written instructions, for example: command statements written according to bash (Bourne Again Shell, whose syntax can be called bash script syntax or bash command syntax) and ansible (an automation tool for configuration management and application deployment) syntax, etc. Specifically, this application does not impose any restrictions on the writing of preset control instructions.

[0032] The preset control instructions store a variety of instructions, such as instructions for copying plug-in deployment serialization files to a corresponding fixed directory, instructions for copying driver data to a corresponding fixed directory, etc. Specifically, this application does not impose any restrictions on the content of the preset control instructions.

[0033] The plug-in deployment serialization file is the configuration file of the plug-in, including but not limited to: the parameters and setting information required by the plug-in, wherein the plug-in deployment serialization file is saved in a serialized format, and serialization refers to the process of converting a data structure or object state into a storable or transmittable format. Regarding the plug-in deployment serialization file, for example: the plug-in deployment yaml file (yaml file generally refers to a file written in the yaml (YAML Ain't Markup Language) data serialization format for configuring and managing applications), etc. Specifically, this application does not impose any restrictions on the plug-in deployment serialization file.

[0034] The plug-in startup file contains instructions or scripts for starting the plug-in, which is used to run the plug-in of the target processor when the artificial intelligence platform is started or under specific conditions. The plug-in stop file contains instructions or scripts for stopping the plug-in, which is used to stop the plug-in of the target processor when the artificial intelligence platform is shut down or under specific conditions. Driver data refers to the driver program files required to install and configure the target processor (such as GPU, Field-Programmable Gate Array (FPGA) etc.) in the preset server after the artificial intelligence platform is installed on the preset server.

[0035] Specifically, regarding the implementation process of the embodiments of the present application, it can be carried out through but not limited to the following methods: the update module 111 executes the command statement in the preset control instruction: the plug-in deployment yaml file and the start and stop files of this plug-in are copied to the fixed directory where the platform's management node stores such files.

[0036] Copy the plug-in deployment serialization files and key files of the driver data to a specific, predefined directory location in the artificial intelligence platform, ensuring that the artificial intelligence platform can automatically load and use the plug-ins and drivers of the target processor when it is started and running without manual intervention, which helps to improve the stability and manageability of the artificial intelligence platform.

[0037] In an achievable embodiment of the present application, the updating module 111 is further configured to: Copying the expansion replacement file of the target processor to a fixed directory corresponding to the expansion replacement file according to a preset control instruction; wherein the multiple replacement files include the expansion replacement file, and the expansion replacement file is used to add the first expansion information of the target processor in the artificial intelligence platform; Copy the module start / stop replacement files of all modules in the artificial intelligence platform to a fixed directory corresponding to the module start / stop replacement files according to the preset control instructions; wherein the multiple replacement files include the module start / stop replacement files, and the module start / stop replacement files are used to control the start / stop of all modules during the process of expanding the target processor of the artificial intelligence platform; According to the preset control instruction, the expansion template replacement file of the target processor is copied to the fixed directory corresponding to the expansion template replacement file; wherein the multiple replacement files include the expansion template replacement file, and the expansion template replacement file is used to add the second expansion information of the target processor in the artificial intelligence platform, and the first expansion information is different from the second expansion information.

[0038] Among them, the preset control instructions also include at least instructions for copying the expansion replacement file to the corresponding fixed directory, instructions for copying the module start and stop replacement file to the corresponding fixed directory, instructions for copying the expansion template replacement file to the corresponding fixed directory, etc.

[0039] The expansion replacement file deployment of the target processor is a file used to add the first expansion information of the target processor in the artificial intelligence platform. The first expansion information includes but is not limited to: the hardware configuration of the target processor, resource allocation strategy or parameter settings related to expansion. The preset control instruction will instruct to copy the expansion replacement file to the fixed directory corresponding to the expansion replacement file. The fixed directory corresponding to the expansion replacement file is specifically used to store the expansion replacement file to ensure that the platform can access and use this file when needed.

[0040] The module start / stop replacement file deployment for all modules includes the instructions or configurations for controlling the start and stop of all modules in the artificial intelligence platform. Since some modules need to be reconfigured or restarted in the artificial intelligence platform to adapt to new hardware resources during the process of expanding the target processor, the module start / stop replacement files for all modules in the artificial intelligence platform need to be copied to the corresponding fixed directory to ensure that the platform can access and use this file when needed.

[0041] The expansion template replacement file of the target processor contains the second expansion information for adding the target processor in the artificial intelligence platform. The second expansion information is different from the first expansion information and includes configuration parameters different from the first expansion information or settings for different expansion stages. Copying the expansion template replacement file to the corresponding fixed directory provides more detailed expansion control for the platform, allowing the platform to expand hardware resources to different degrees according to different needs.

[0042] Specifically, the implementation process of the embodiment of the present application can be implemented in the following manner, but not limited to: the update module 111 executes the command statement in the preset control instruction: copy the expansion replacement file of the target processor to the fixed directory where the management node of the platform stores such files. Copy the replacement file for starting and stopping all modules managed by the platform to the fixed directory where the management node of the platform stores such files. Copy the template replacement file used for expansion to the fixed directory where the management node of the platform stores such files.

[0043] Copy specific replacement files to the corresponding fixed directory in the artificial intelligence platform so that new configurations and start and stop instructions can be automatically applied when the platform expands the target processor. By using preset control instructions, file copying can be automated, reducing the complexity and error probability of manual operations. Expansion replacement files, module start and stop replacement files, and expansion template replacement files jointly ensure the stable operation of the platform and effective management of resources during the expansion process.

[0044] In an achievable embodiment of the present application, the updating module 111 is further configured to: According to the preset control instruction, the label addition and replacement file of the target processor is copied to the fixed directory corresponding to the label addition and replacement file; wherein the multiple replacement files include the label addition and replacement file, and the label addition and replacement file is used to add the label information of the target processor in the artificial intelligence platform; According to the preset control instructions, the node addition and replacement file of the target processor is copied to the fixed directory corresponding to the node addition and replacement file; wherein the multiple replacement files include the node addition and replacement file, and the node addition and replacement file is used to add the node information of the target processor in the artificial intelligence platform.

[0045] The tag addition replacement file is a file containing the tag information of the target processor. The tag information is usually used to identify and classify processor resources to facilitate resource management and scheduling in the artificial intelligence platform. The preset control instruction copies the tag addition replacement file to the corresponding fixed directory to ensure that the platform can allocate and manage resources according to the tag information of the tag addition replacement file.

[0046] The node addition and replacement file is a file containing the target processor node information. Node information usually refers to the location and role information of the processor in the network or computing cluster, which is crucial for the resource scheduling and task allocation of the platform. The preset control instruction copies the node addition and replacement file to the corresponding fixed directory. The platform can optimize resource allocation based on the node information to ensure that the task can be executed on the correct processor.

[0047] Furthermore, by adding label information through label addition replacement files, the platform can quickly identify and process specific processor resources through labels, which is of great significance for multi-dimensional resource management. By adding node information through node addition replacement files, the platform can understand the specific location and role of the target processor in the network or cluster through node information, which is crucial for task distribution and execution.

[0048] Specifically, the implementation process of the embodiment of the present application can be implemented in the following manner, but not limited to: the update module 111 executes the command statement in the preset control instruction: copy the tag replacement file for capacity expansion to the fixed directory where the management node of the platform stores such files. Copy the node replacement file for capacity expansion to the fixed directory where the management node of the platform stores such files.

[0049] By automatically deploying tags to add and replace files and nodes to add and replace files, the artificial intelligence platform can more efficiently manage and utilize the processor resources of the target processor, improving the overall computing efficiency and scalability of the platform.

[0050] In an achievable embodiment of the present application, the updating module 111 is further configured to: Copying the cleanup node replacement file of the target processor to a fixed directory corresponding to the cleanup node replacement file according to the preset control instruction; wherein the multiple replacement files include the cleanup node replacement file, and the cleanup node replacement file is used to clean up the data of the target processor in the preset server after the artificial intelligence platform is uninstalled from the preset server; Copying the detection template replacement file of the target processor to a fixed directory corresponding to the detection template replacement file according to the preset control instruction; wherein the multiple replacement files include the detection template replacement file, and the detection template replacement file is used to monitor the running state of the target processor through the first monitoring method after the artificial intelligence platform is installed on the preset server; According to the preset control instructions, the detection replacement file of the target processor is copied to a fixed directory corresponding to the detection replacement file; wherein the multiple replacement files include the detection replacement file, and the detection replacement file is used to monitor the operating status of the target processor through a second monitoring method after the artificial intelligence platform is installed on the preset server.

[0051] Among them, the cleanup node replacement file of the target processor is a file used to clean up the target processor related data in the preset server after the artificial intelligence platform is uninstalled on the preset server, including but not limited to: deleting configuration files, log files, temporary files, etc., to ensure that no residual data is left on the preset server after the platform is uninstalled, and to keep the preset server environment clean. The preset control instruction copies the cleanup node replacement file to the corresponding fixed directory, and can perform data cleaning operations when needed.

[0052] The detection template replacement file of the target processor is a file used to monitor the operating status of the target processor through a first monitoring method after the artificial intelligence platform is installed on the preset server. The first monitoring method includes but is not limited to: a specified monitoring strategy or template for collecting and processing the performance data, error log, etc. of the target processor. The preset control instruction copies the detection template replacement file to the corresponding fixed directory, so that the platform can immediately monitor the status of the target processor after installation to ensure its normal operation.

[0053] The detection replacement file of the target processor is a file used to monitor the operating status of the target processor through a second monitoring method after the artificial intelligence platform is installed on the preset server. The second monitoring method is different from the first monitoring method and provides another monitoring perspective or strategy to enhance the comprehensiveness and reliability of monitoring. The preset control instruction copies the detection replacement file to the corresponding fixed directory, which enables the artificial intelligence platform to use a variety of monitoring methods to ensure the stability and performance of the target processor.

[0054] Furthermore, by cleaning up the node replacement files for data cleaning, it can be ensured that the preset server environment is cleaned up after the AI ​​platform is uninstalled, avoiding data residue and security risks. By detecting template replacement files and detecting replacement file operation status monitoring, the processor operation status can be monitored in real time through different monitoring templates and strategies, so as to timely discover and solve problems and ensure the stable operation of the AI ​​platform.

[0055] Specifically, the implementation process of the embodiment of the present application can be implemented in the following manner, but not limited to: the update module 111 executes the command statement in the preset control instruction: the cleanup node replacement file used for shrinking is copied to the fixed directory where the management node of the platform stores such files. The detection template replacement file used to monitor the GPU is copied to the fixed directory where the management node of the platform stores such files. The detection replacement file used to monitor the GPU is copied to the fixed directory where the existing node of the platform stores such files.

[0056] Through automated deployment of cleanup node replacement files, detection template replacement files, and detection replacement files, the artificial intelligence platform can manage and monitor resources more efficiently, improving the maintainability and reliability of the system.

[0057] In an achievable embodiment of the present application, the artificial intelligence platform further includes: a computing node 12, The computing node 12 is used to deploy node services; wherein the node services at least include collecting node information, reporting cluster monitoring, and resource management services; The computing node 12 is also used to run the development environment corresponding to the artificial intelligence training to provide the infrastructure for artificial intelligence training.

[0058] For details about the specific structure and function of the management node 11 and the computing node 12 in the artificial intelligence platform, please refer to Figure 3 , Figure 3 A schematic diagram of the structure of another artificial intelligence platform provided for this application, wherein the computing node 12 is a key component of the artificial intelligence platform, which is usually an entity with computing capabilities, and can be a physical server, a virtual machine or a container, etc. The computing node 12 is responsible for executing computing tasks in the platform, especially in the artificial intelligence training process.

[0059] One of the main functions of computing node 12 is to deploy node services, which include but are not limited to the following: Collecting node information: computing node 12 will collect its own configuration information, resource usage (such as CPU, memory, storage and network usage), operating status, etc., so that the artificial intelligence platform can understand the specific situation of each node. Reporting cluster monitoring: computing node 12 will regularly report the collected information to the cluster monitoring system, and the cluster manager can monitor the health and performance of the entire cluster. Resource management services: computing node 12 provides resource management services, including but not limited to resource allocation, scheduling and optimization, to ensure that resources are effectively utilized and meet the needs of artificial intelligence training tasks.

[0060] The computing node 12 can also run the development environment required for artificial intelligence training. The development environment includes but is not limited to: operating system, programming language interpreter or compiler, artificial intelligence framework (such as: TensorFlow, PyTorch, etc.), related libraries and dependencies, data processing and model training tools, etc.

[0061] Computing node 12 provides the necessary environment and resources for AI training tasks, and can develop, train and test models on the AI ​​platform. Computing node 12 is not only responsible for deploying services in the AI ​​platform to manage and monitor its own status, but also provides a complete development environment required to run AI training tasks. Users of the AI ​​platform can focus on model design and optimization without having to worry about the configuration and maintenance of the underlying infrastructure.

[0062] In an achievable embodiment of the present application, the management node 11 at least further includes: a cluster monitor 114; The management node 11 is also used to deploy resource management services and user management services.

[0063] The management node 11 is another key component of the artificial intelligence platform, which is responsible for managing and coordinating the work of the entire platform. The management node 11 usually does not directly participate in computing tasks, but focuses on providing support services to ensure the efficient and stable operation of the platform.

[0064] Please continue reading Figure 3 ,The components of the management node include but are not limited to the database 113. The management node 11 includes a database for storing various data of the platform, including but not limited to user information, training task records, resource allocation, monitoring data, etc. The database 113 is a key component for data persistence of the platform.

[0065] The cluster monitoring 114 provides a cluster monitoring service for real-time monitoring of the status of the entire cluster, including but not limited to the health status of the computing nodes 12, resource usage, task execution progress, etc. This helps to timely discover and solve problems and ensure the stable operation of the cluster.

[0066] Regarding the service deployment of management node 11: The management node deploys resource management services, which are responsible for the resource allocation and scheduling of the entire artificial intelligence platform. Specific functions include but are not limited to: receiving resource usage reports from computing nodes, dynamically allocating and recycling computing resources according to the requirements of training tasks, scheduling training tasks to appropriate computing nodes for execution, optimizing resource usage, and improving resource utilization.

[0067] The management node 11 also deploys a user management service, which is used to process user-related operations, including but not limited to: user identity authentication and authorization; creation, update and deletion of user accounts; management of user resource quotas; and recording and analysis of user operation logs.

[0068] In one possible embodiment of the present application, the management node 11 is pre-deployed in the artificial intelligence platform; Computing node 12 is added to the artificial intelligence platform by means of capacity expansion.

[0069] Among them, the computing nodes 12 are all equipped with GPU devices, and the management nodes 11 may or may not be equipped with GPU devices. In the face of unsupported GPU models, i.e., target processors, the methods adopted include but are not limited to: when starting to deploy the platform, only deploy the management node 11 (select a node without GPU as the management node 11), and then add the computing nodes 12 to the cluster by expanding the capacity.

[0070] The management node 11 is a core component of the artificial intelligence platform. It has been pre-deployed during the initialization phase of the artificial intelligence platform, which means that at the beginning of the construction of the artificial intelligence platform, all services and functions of the management node 11 have been properly configured and ready to start managing the entire platform. The pre-deployed management node 11 ensures that the training platform has a stable control center that can be responsible for the startup, configuration, monitoring and maintenance of the training platform.

[0071] The computing node 12 is not deployed all at once when the artificial intelligence platform is initialized, but is dynamically added to the artificial intelligence platform through capacity expansion as needed. Capacity expansion refers to adding more computing resources to improve the overall computing power of the platform according to changes in the processing needs of the artificial intelligence platform.

[0072] In the artificial intelligence platform, the management node is the permanent control center of the platform, which is pre-deployed and always running to ensure that the basic management and monitoring functions of the platform are always available. The computing nodes can be dynamically added to the platform according to actual needs, so that the platform can flexibly respond to different computing loads, thereby improving resource utilization and platform scalability.

[0073] An embodiment of the present application provides a capacity expansion and updating method, and the method is described in detail in conjunction with the execution flow of the capacity expansion and updating method.

[0074] like Figure 4 As shown, Figure 4 A flow chart of a capacity expansion and updating method provided in this application, the capacity expansion and updating method is applied to Figures 1 to 3 The AI ​​platform described includes: Step 401 , obtaining a processor file of a target processor and a plurality of replacement files corresponding to the target processor; wherein the plurality of replacement files are pre-acquired files for expanding the capacity of the target processor.

[0075] In the embodiment of the present application, the processor file is a file related to the target processor, including at least a plug-in image, driver data and a plug-in file. The multiple replacement files are pre-acquired files specifically used for performing capacity expansion operations on the target processor.

[0076] Among them, driver data refers to the software components used to establish effective communication between the operating system and the target processor, usually including hardware drivers, so that the operating system can identify and correctly interact with the hardware components of the target processor (such as the central processing unit (CPU), GPU, network interface, etc.). The purpose of obtaining driver data is to ensure that the artificial intelligence platform can correctly install and configure the target processor on the preset server so that it can perform computing tasks efficiently.

[0077] A plug-in image usually refers to a software package that contains specific functions or services, which can extend the functionality of the AI ​​platform and can be quickly deployed and used in the platform. The purpose of obtaining a plug-in image is to integrate additional functions into the AI ​​platform, that is, the functions corresponding to the target processor.

[0078] Plugin files refer to configuration files, scripts, or other auxiliary files related to the plugin of the target processor, which work together with the plugin image to ensure that the plugin can run correctly in the artificial intelligence platform. Plugin files may include but are not limited to startup scripts, stop scripts, configuration files, serialized data, etc., which are used to control the behavior of the plugin and configure its runtime environment.

[0079] Step 402, copy multiple replacement files and processor files corresponding to the target processor to fixed directories corresponding to the multiple replacement files and processor files respectively; the fixed directory is a pre-set directory for storing files.

[0080] In an embodiment of the present application, the plurality of replacement files are pre-acquired files specifically used to perform capacity expansion operations on the target processor. Capacity expansion includes but is not limited to: increasing processing power, optimizing configuration, updating system parameters, etc. The types of the plurality of replacement files include but are not limited to: scripts, configuration files, or binary programs, etc.

[0081] Among them, the multiple replacement files include but are not limited to: expansion replacement files of the target processor, module start and stop replacement files of all modules in the artificial intelligence platform, expansion template replacement files of the target processor, label addition replacement files of the target processor, node addition replacement files of the target processor, cleanup node replacement files of the target processor, detection template replacement files of the target processor and detection replacement files of the target processor, etc. Specifically, this application does not impose any restrictions on multiple replacement files.

[0082] Fixed directories are pre-set directories used to store files of a specific type. Each type of file has its corresponding fixed directory, which helps with system management and maintenance.

[0083] Step 403: when there are multiple corresponding replacement files and processor files in multiple fixed directories, start the plug-in service of the target processor by executing the plug-in startup file; wherein the processor file at least includes the plug-in startup file.

[0084] In an embodiment of the present application, in an artificial intelligence platform, multiple replacement files, driver data in processor files, and plug-in files need to be copied to their respective corresponding fixed directories, and all necessary files need to be placed in corresponding locations to prepare for starting and processing the plug-in service of the target processor.

[0085] The plug-in startup file is a part of the plug-in file in the processor file, which contains the instructions and configuration information for starting the plug-in service. Regarding the plug-in startup file type, for example: script or executable program, it is used to initialize the plug-in environment, load the necessary resources, and start the plug-in operation.

[0086] By executing the plug-in startup file, the artificial intelligence platform will start the plug-in service of the target processor. The process of executing the plug-in startup file includes but is not limited to the following steps: Load the plug-in's dependent libraries and resources. Configure the environment variables and parameters required for the plug-in to run. Initialize the state and objects inside the plug-in. Start the main process or service of the plug-in to start executing the predetermined function.

[0087] After ensuring that all necessary replacement files, driver data in the processor files, and plug-in files are located in the correct fixed directory, the AI ​​platform starts the plug-in service of the target processor by executing the plug-in startup file. This enables the AI ​​platform to utilize the additional functions and services provided by the plug-in to enhance the performance and functionality of the target processor. The AI ​​platform can dynamically expand its capabilities according to the needs of the training task, thereby providing a more flexible and efficient AI training environment.

[0088] Step 404, based on the activated plug-in service, expand the target processor to the artificial intelligence platform.

[0089] In an embodiment of the present application, after the plug-in service is successfully started, the artificial intelligence platform can provide additional functions and support, at which time the target processor can be expanded in the artificial intelligence platform.

[0090] Regarding the process of expanding the target processor to the artificial intelligence platform, it can be done through but not limited to the following methods: Identification and registration: The artificial intelligence platform identifies the newly added target processor and registers it with the platform's resource management system. Resource allocation: The platform allocates resources to the newly added processor (target processor), such as: CPU core, memory, storage space, and network bandwidth. Service synchronization: Ensure that the plug-in service of the target processor is synchronized with other parts of the platform, including: monitoring, logging, and task scheduling. Status monitoring: Start status monitoring of the target processor to ensure that it runs stably and can respond to the platform's scheduling. Specifically, regarding the process of expanding the target processor to the artificial intelligence platform, it can be determined based on actual conditions, and this application does not impose any restrictions.

[0091] By obtaining the necessary files and data and ultimately expanding the target processor to the AI ​​platform, the stability and scalability of the platform can be ensured, allowing the AI ​​platform to flexibly adjust resources to adapt to changing workloads and performance requirements.

[0092] In an achievable embodiment of the present application, when copying multiple replacement files, driver data and plug-in files corresponding to the target processor to a fixed directory, it is necessary to perform it through pre-set control instructions. Regarding the setting of control instructions, it can be implemented by but not limited to the following methods: obtain the control instructions generated by the preset command statements to obtain the preset control instructions.

[0093] In an embodiment of the present application, the preset command statement is a custom-selected command statement, such as a bash statement, ansible statement, etc. Specifically, the present application does not impose any limitation on the preset command statement.

[0094] The preset control instructions store a variety of instructions, including but not limited to: control instructions corresponding to a plurality of replacement files, driver data and plug-in files corresponding to the target processor, etc., so as to copy the files to a fixed directory.

[0095] Obtaining control instructions generated by preset command statements ensures that the artificial intelligence platform can automatically perform tasks according to predefined operating procedures, reduces manual intervention, and improves the accuracy and efficiency of operations. By using preset command statements to generate control instructions, the artificial intelligence platform can maintain a high degree of flexibility and maintainability, thereby better adapting to different operating environments and user needs.

[0096] In an achievable embodiment of the present application, when copying multiple replacement files, driver data and plug-in files corresponding to the target processor to a fixed directory, it can be implemented by but not limited to the following methods: performing label modification processing on the plug-in image to obtain a modified plug-in image, and pushing the modified plug-in image to a preset image file database; wherein the preset image file database is a pre-set database for storing plug-in images; by executing preset control instructions, the multiple replacement files, driver data and plug-in files corresponding to the target processor are respectively copied to the fixed directories corresponding to the multiple replacement files, driver data and plug-in files.

[0097] In the embodiment of the present application, the plug-in image includes but is not limited to metadata, wherein a tag is a string used to identify the plug-in image, including but not limited to version number, build date, environment identifier, etc. Tag modification processing refers to editing the metadata of the plug-in image to update or change the identification information of the image.

[0098] The preset image file database is a database dedicated to storing and managing plug-in images. The preset image file database can be private or public, depending on the security requirements and deployment strategy of the artificial intelligence platform. Pushing the modified plug-in image to the preset image file database is to save the modified plug-in image so that it can be used by other components or services in the artificial intelligence platform.

[0099] Regarding file copying, it can be achieved through but not limited to the following methods: Execute command statement 1: Get the plug-in image of the target processor from the software package module, load the image, modify the image tag, and finally push the image to the harbor warehouse (preset image file database).

[0100] Execute command statement 2: Copy the plug-in deployment YAML file (plug-in deployment serialization file) and the plug-in start and stop files (plug-in startup file and plug-in stop) to the fixed directory where the management node of the artificial intelligence platform stores such files.

[0101] Execute command statement 3: Copy the expansion replacement file of the unsupported GPU (expansion replacement file of the target processor) to the fixed directory where the management node of the artificial intelligence platform stores such files.

[0102] Execute command statement 4: Copy the replacement files for starting and stopping all modules managed by the platform (module start and stop replacement files) to the fixed directory where such files are stored on the management node of the artificial intelligence platform.

[0103] Execute command statement 5: Copy the template replacement file used for capacity expansion (capacity expansion template replacement file of the target processor) to the fixed directory where the management node of the artificial intelligence platform stores such files.

[0104] Execute command statement 6: Copy the label replacement file for capacity expansion (label replacement file for the target processor) to the fixed directory where the management node of the artificial intelligence platform stores such files.

[0105] Execute command statement 7: Copy the node addition replacement file used for capacity expansion (node ​​addition replacement file of the target processor) to the fixed directory where the management node of the artificial intelligence platform stores such files.

[0106] Execute command statement 8: Copy the cleanup node replacement file used for shrinking (the cleanup node replacement file of the target processor) to the fixed directory where the management node of the artificial intelligence platform stores such files.

[0107] Execute command statement 9: Copy the GPU detection template replacement file used for monitoring (the detection template replacement file of the target processor) to the fixed directory where the management node of the artificial intelligence platform stores such files.

[0108] Execute command statement 10: Copy the GPU detection replacement file used for monitoring (detection replacement file of the target processor) to the fixed directory where the existing nodes of the artificial intelligence platform store such files.

[0109] Execute command statement 11: Copy the driver (driver data) provided by the GPU manufacturer stored in the software package module to the fixed directory where such files are stored on the artificial intelligence platform management node.

[0110] Execute command statement 12: Execute the plug-in startup file on the management node to start the plug-in.

[0111] Among them, command statements 1-12 are instructions included in the preset control instructions.

[0112] Execute preset control instructions to copy the replacement files, driver data and plug-in files of the target processor to their corresponding fixed directories, ensuring that all necessary files are correctly placed in the expected location of the system to facilitate subsequent service startup and platform expansion operations.

[0113] In summary, this application can achieve the following technical effects: The present application updates the expansion process in the artificial intelligence platform, and copies various expansion files of the target processor, namely multiple replacement files, driver data and plug-in files, to a fixed directory through an update module. After that, the various expansion files of the target processor exist in the fixed directory, and the plug-in service corresponding to the target processor can be started to ensure that after the target processor is expanded in the artificial intelligence platform, the corresponding functions of the target processor can be run normally to complete the expansion of the target processor. Therefore, it can solve the technical problems that the existing expansion technology cannot expand the nodes of unsupported GPU models, cannot be applied to the scenario of on-site node environment changes, and leads to poor user experience, and achieves the technical effect of quickly updating the expansion process of the nodes of unsupported GPU models without field compatibility, ensuring that the nodes of unsupported GPU models can be expanded.

[0114] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method.

[0115] The embodiment of the present application also provides a capacity expansion and updating device, Figure 5 A schematic diagram of the structure of a capacity expansion and updating device provided in this application, such as Figure 5 As shown, including: An acquisition unit 51 is used to acquire a processor file of a target processor and a plurality of replacement files corresponding to the target processor; wherein the plurality of replacement files are pre-acquired files used to expand the capacity of the target processor; A copying unit 52, used to copy the multiple replacement files and the processor file corresponding to the target processor to the fixed directories corresponding to the multiple replacement files and the processor file respectively; wherein the fixed directory is a pre-set directory for storing files; A startup unit 53 is used to start the plug-in service of the target processor by executing the plug-in startup file when there are multiple corresponding replacement files and processor files in multiple fixed directories; wherein the processor file at least includes the plug-in startup file; The expansion unit 54 is used to expand the target processor to the artificial intelligence platform based on the plug-in service after startup.

[0116] In one embodiment of the present application, Figure 6 As shown, the expansion and updating device also includes: The generating unit 55 is used to obtain the control instruction generated by the preset command statement to obtain the preset control instruction.

[0117] In one embodiment of the present application, the copy unit 52 is further configured to: Perform tag modification processing on the plug-in image to obtain a modified plug-in image, and push the modified plug-in image to a preset image file database; wherein the preset image file database is a pre-set database for storing plug-in images; By executing preset control instructions, a plurality of replacement files, driver data and plug-in files corresponding to the target processor are copied to fixed directories corresponding to the plurality of replacement files, driver data and plug-in files respectively.

[0118] For the description of the features in the embodiment corresponding to the capacity expansion and updating device, reference may be made to the relevant description of the embodiment corresponding to the capacity expansion and updating method, which will not be described in detail here.

[0119] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any of the above-mentioned expansion and update method embodiments.

[0120] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above-mentioned expansion and update method embodiments when running.

[0121] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0122] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any of the above-mentioned expansion and update method embodiments are implemented.

[0123] An embodiment of the present application also provides another computer program product, including a non-volatile computer-readable storage medium, the non-volatile computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in any of the above-mentioned expansion and update method embodiments are implemented.

[0124] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0125] The above is a detailed introduction to an artificial intelligence platform and expansion and updating method, device, and electronic device provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. An artificial intelligence platform, characterized in that: include: A management node, the management node including an update module, a software package module and a database; The update module is used to obtain a processor file of a target processor from the software package module, and to obtain a plurality of replacement files corresponding to the target processor from the database; The update module is further used to copy the multiple replacement files and the processor file to the fixed directories corresponding to the multiple replacement files and the processor file respectively; wherein the management node also includes a plurality of the fixed directories; The update module is further configured to, when the plurality of replacement files and the processor files corresponding to each other exist in the plurality of fixed directories, start the plug-in service of the target processor by executing a plug-in startup file; wherein the processor file includes the plug-in startup file; The management node is used to expand the target processor to the artificial intelligence platform based on the plug-in service after startup.

2. The artificial intelligence platform according to claim 1, characterized in that: The update module is also used for: The plug-in image in the processor file is subjected to label modification processing to obtain a modified plug-in image, and the modified plug-in image is pushed to a preset image file database; wherein the management node also includes the preset image file database, and the processor file includes at least the plug-in image, driver data, and a plug-in file.

3. The artificial intelligence platform according to claim 2, characterized in that: The update module is also used for: Copy the plug-in deployment serialization file, the plug-in startup file and the plug-in stop file to the fixed directory corresponding to the plug-in file according to the preset control instruction; wherein the plug-in file at least includes the plug-in deployment serialization file, the plug-in startup file and the plug-in stop file; The driving data is copied to the fixed directory corresponding to the driving data according to the preset control instruction; wherein the driving data is used to add the driver of the target processor in the preset server after the artificial intelligence platform is installed in the preset server.

4. The artificial intelligence platform according to claim 3, characterized in that: The update module is also used for: According to the preset control instruction, copy the expansion replacement file of the target processor to the fixed directory corresponding to the expansion replacement file; wherein the multiple replacement files include the expansion replacement file, and the expansion replacement file is used to add the first expansion information of the target processor in the artificial intelligence platform; According to the preset control instruction, the module start-stop replacement files of all modules in the artificial intelligence platform are copied to the fixed directory corresponding to the module start-stop replacement files; wherein the multiple replacement files include the module start-stop replacement file, and the module start-stop replacement file is used to control the start and stop of all modules in the process of expanding the target processor of the artificial intelligence platform; According to the preset control instruction, the expansion template replacement file of the target processor is copied to the fixed directory corresponding to the expansion template replacement file; wherein the multiple replacement files include the expansion template replacement file, and the expansion template replacement file is used to add the second expansion information of the target processor in the artificial intelligence platform, and the first expansion information is different from the second expansion information.

5. The artificial intelligence platform according to claim 4, characterized in that: The update module is also used for: According to the preset control instruction, copy the label addition replacement file of the target processor to the fixed directory corresponding to the label addition replacement file; wherein the multiple replacement files include the label addition replacement file, and the label addition replacement file is used to add the label information of the target processor in the artificial intelligence platform; According to the preset control instruction, the node addition and replacement file of the target processor is copied to the fixed directory corresponding to the node addition and replacement file; wherein the multiple replacement files include the node addition and replacement file, and the node addition and replacement file is used to add the node information of the target processor in the artificial intelligence platform.

6. The artificial intelligence platform according to claim 5, characterized in that: The update module is also used for: According to the preset control instruction, the cleanup node replacement file of the target processor is copied to the fixed directory corresponding to the cleanup node replacement file; wherein the multiple replacement files include the cleanup node replacement file, and the cleanup node replacement file is used to clean up the data of the target processor in the preset server after the artificial intelligence platform is uninstalled from the preset server; Copying the detection template replacement file of the target processor to the fixed directory corresponding to the detection template replacement file according to the preset control instruction; wherein the multiple replacement files include the detection template replacement file, and the detection template replacement file is used to monitor the running state of the target processor through a first monitoring method after the artificial intelligence platform is installed on the preset server; According to the preset control instruction, the detection replacement file of the target processor is copied to the fixed directory corresponding to the detection replacement file; wherein the multiple replacement files include the detection replacement file, and the detection replacement file is used to monitor the operating status of the target processor through a second monitoring method after the artificial intelligence platform is installed on the preset server.

7. The artificial intelligence platform according to claim 1, characterized in that: The artificial intelligence platform also includes: computing nodes, The computing node is used to deploy node services; wherein the node services at least include collecting node information, reporting cluster monitoring, and resource management services; The computing node is also used to run the development environment corresponding to the artificial intelligence training to provide an infrastructure for artificial intelligence training.

8. The artificial intelligence platform according to claim 1, characterized in that: The management node also includes at least: cluster monitoring; The management node is also used to deploy resource management services and user management services.

9. The artificial intelligence platform according to claim 7, characterized in that: The management node is pre-deployed in the artificial intelligence platform; The computing nodes are added to the artificial intelligence platform by means of capacity expansion.

10. A capacity expansion and updating method, characterized in that: The capacity expansion and updating method is applied to the artificial intelligence platform according to any one of claims 1 to 9, comprising: Acquire a processor file of a target processor and a plurality of replacement files corresponding to the target processor; wherein the plurality of replacement files are pre-acquired files for expanding the capacity of the target processor; Copying the multiple replacement files corresponding to the target processor and the processor file to the fixed directories corresponding to the multiple replacement files and the processor file, respectively; wherein the fixed directory is a pre-set directory for storing files; In the case that the plurality of replacement files and the processor files corresponding to each other exist in the plurality of fixed directories, the plug-in service of the target processor is started by executing the plug-in startup file; wherein the processor file at least includes the plug-in startup file; Based on the started plug-in service, the target processor is expanded to the artificial intelligence platform.

11. The capacity expansion and updating method according to claim 10, characterized in that: Before obtaining the driver data, plug-in image and plug-in file of the target processor, the method further includes: Acquire the control instruction generated by the preset command statement to obtain the preset control instruction.

12. The capacity expansion and updating method according to claim 11, characterized in that: The step of copying the plurality of replacement files corresponding to the target processor and the processor file to fixed directories corresponding to the plurality of replacement files and the processor file respectively comprises: Performing label modification processing on the plug-in image in the processor file to obtain a modified plug-in image, and pushing the modified plug-in image to a preset image file database; wherein the preset image file database is a pre-set database for storing the plug-in image; By executing the preset control instruction, the multiple replacement files and the processor file corresponding to the target processor are respectively copied to the fixed directories corresponding to the multiple replacement files and the processor file.

13. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the capacity expansion and updating method as claimed in any one of claims 10 to 12 when executing the computer program.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the capacity expansion and update method according to any one of claims 10 to 12.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the capacity expansion and updating method as claimed in any one of claims 10 to 12 are implemented.

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