Intelligent service platform implementation method based on large model optimization and efficient deployment

Abstract

The invention discloses an intelligent service platform implementation method based on large model optimization and efficient deployment, and relates to the technical field of artificial intelligence application. An intelligent service platform is created for large model optimization and efficient deployment, a distributed training architecture is adopted through the intelligent service platform, a training task of a large model is decomposed to a plurality of computing nodes to be executed in parallel, and the computing pressure of a single node is reduced; an automatic hyper-parameter optimization algorithm is integrated through the intelligent service platform, a model structure search function is provided at the same time, and a better model structure is automatically explored through neural architecture search (NAS); the intelligent service platform adopts a model compression and quantification method to perform compression and quantification processing on a large model, the intelligent service platform uses a deployment tool to seamlessly deploy the optimized model to various hardware platforms, and a reasoning acceleration method is adopted to perform deep optimization on a model reasoning process.

Description

Technical Field

[0001] This invention discloses a method for implementing an intelligent service platform based on large model optimization and efficient deployment, which relates to the field of artificial intelligence application technology. Background Technology

[0002] Large models typically have billions or even hundreds of billions of parameters, enabling them to learn complex patterns and knowledge, thus performing exceptionally well on various tasks. However, in practical applications, large models face a series of pressing problems that need to be addressed, such as: 1. High cost of computing resources: Training and inference of large models require a lot of computing resources, usually relying on high-performance GPU clusters, which makes it difficult for many enterprises and individuals to afford the high cost.

[0003] 2. High difficulty in model optimization: Optimizing large models involves complex hyperparameter tuning, model structure improvement, and training strategy optimization, which requires professional knowledge and a large number of experiments, making it difficult for ordinary users.

[0004] 3. Low deployment efficiency: The deployment process of large models is complex, involving multiple steps such as model conversion, optimization, and deployment to specific hardware devices. Furthermore, the poor compatibility between different hardware platforms leads to low deployment efficiency.

[0005] 4. High requirements for hardware devices: Existing large models usually require high-performance hardware devices to run, which limits their application in some resource-constrained scenarios, such as mobile devices and edge computing devices.

[0006] 5. Lack of unified platform support: Currently, there is a lack of a unified platform in the market that can integrate the training, optimization, deployment and application of large models. Users need to switch between different tools and frameworks, which increases the difficulty of use and development costs. Summary of the Invention

[0007] This invention provides a method for implementing an intelligent service platform based on large model optimization and efficient deployment. By constructing a unified platform architecture, it integrates the entire process of training, optimizing, deploying, and applying large models, solves the pain points in existing large model applications, lowers the threshold for use, and improves the practicality and scalability of large models, enabling them to be more widely applied in various real-world scenarios and providing users with an efficient and intelligent service experience.

[0008] The specific solution proposed in this invention is as follows: This invention provides a method for implementing an intelligent service platform based on large-scale model optimization and efficient deployment, creating an intelligent service platform for large-scale model optimization and efficient deployment. The intelligent service platform adopts a distributed training architecture, which decomposes the training task of a large model into multiple computing nodes for parallel execution, thereby reducing the computing pressure on a single node. The intelligent service platform integrates automatic hyperparameter optimization algorithms, including Bayesian optimization algorithms and genetic algorithms, to automatically search for the optimal hyperparameter combination, thereby improving model performance. It also provides a model structure search function, which automatically explores better model structures through Neural Architecture Search (NAS). The intelligent service platform employs model compression and quantization methods to compress and quantize large models, reducing the number of model parameters and storage space, making the models more suitable for running on resource-constrained devices. The intelligent service platform uses deployment tools to seamlessly deploy optimized models to various hardware platforms, including but not limited to GPUs, CPUs, FPGAs, ASICs, and mobile devices. It employs inference acceleration methods to deeply optimize the model inference process, while supporting dynamic quantization and mixed-precision inference. It dynamically adjusts the model's accuracy and speed according to actual needs, monitors performance indicators during the model inference process in real time, and automatically optimizes and adjusts the inference engine based on monitoring data.

[0009] Furthermore, in the intelligent service platform implementation method based on large model optimization and efficient deployment described in claim 1, the intelligent service platform provides a unified user interface, providing a full-process operation entry point for model training, optimization, deployment and application, enabling users to upload data, configure training tasks, monitor training progress, view model performance indicators, deploy models and call models for inference operations through the interface.

[0010] Furthermore, in the intelligent service platform implementation method based on large model optimization and efficient deployment described in claim 1, the intelligent service platform provides a multi-tenant management mode, provides independent resource space and permission management for different users, ensures data isolation and resource security between users, and integrates intelligent resource scheduling algorithms to automatically allocate and schedule resources according to users' task requirements and resource usage, thereby improving resource utilization and reducing operating costs.

[0011] Furthermore, in the intelligent service platform implementation method based on large model optimization and efficient deployment described in claim 1, model management and version control are performed through the intelligent service platform: a model management system is established to manage the entire lifecycle of model training, optimization, deployment and update, and to perform model version control, backtrack and compare the performance of different versions of the model to ensure the stability and traceability of the model. At the same time, model sharing and collaboration functions are provided to facilitate model communication and cooperation among users.

[0012] Furthermore, the intelligent service platform described in claim 1, which is based on large model optimization and efficient deployment, adopts a plug-in architecture design, allowing the integration of various functional plug-ins, including data augmentation plug-ins, model interpretation plug-ins, and security detection plug-ins, to expand the platform's functions and application scenarios.

[0013] The present invention also provides an intelligent service platform based on large model optimization and efficient deployment. The intelligent service platform adopts a distributed training architecture, which decomposes the training task of the large model into multiple computing nodes for parallel execution, thereby reducing the computing pressure on a single node. The intelligent service platform integrates automatic hyperparameter optimization algorithms, including Bayesian optimization algorithms and genetic algorithms. It uses automatic hyperparameter optimization algorithms to automatically search for the optimal hyperparameter combination to improve model performance. At the same time, it provides a model structure search function, which automatically explores better model structures through neural architecture search (NAS). The intelligent service platform employs model compression and quantization methods to compress and quantize large models, reducing the number of model parameters and storage space, making the models more suitable for running on resource-constrained devices. The intelligent service platform uses deployment tools to seamlessly deploy the optimized model to various hardware platforms, including but not limited to GPUs, CPUs, FPGAs, ASICs, and mobile devices. It employs inference acceleration methods to deeply optimize the model inference process, while supporting dynamic quantization and mixed-precision inference. It dynamically adjusts the model's accuracy and speed according to actual needs, monitors performance indicators during the model inference process in real time, and automatically optimizes and adjusts the inference engine based on monitoring data.

[0014] Furthermore, the intelligent service platform based on large model optimization and efficient deployment described in claim 1 provides a unified user interface, offering a full-process operation entry point for model training, optimization, deployment, and application, enabling users to upload data, configure training tasks, monitor training progress, view model performance indicators, deploy models, and call models for inference operations through the interface.

[0015] Furthermore, the intelligent service platform based on large model optimization and efficient deployment described in claim 1 provides a multi-tenant management mode, providing independent resource space and permission management for different users, ensuring data isolation and resource security between users. At the same time, it integrates intelligent resource scheduling algorithms to automatically allocate and schedule resources according to users' task requirements and resource usage, thereby improving resource utilization and reducing operating costs.

[0016] Furthermore, the intelligent service platform based on large model optimization and efficient deployment described in claim 1 performs model management and version control: establishing a model management system to manage the entire lifecycle of model training, optimization, deployment and update, and to control model versions, retrospectively review and compare the performance of different versions of the model to ensure the stability and traceability of the model. At the same time, it provides model sharing and collaboration functions to facilitate model communication and cooperation among users.

[0017] Furthermore, the intelligent service platform based on large model optimization and efficient deployment described in claim 1 adopts a plug-in architecture design, which allows the integration of various functional plug-ins, including data augmentation plug-ins, model interpretation plug-ins, and security detection plug-ins, to expand the platform's functions and application scenarios.

[0018] The advantages of this invention are: 1. Lowering the barrier to entry: By providing a unified platform architecture and a simple user interface, the entire process of training, optimizing, deploying and applying large models is integrated, enabling ordinary users to easily use large models without having to delve into complex underlying technical details, thus lowering the barrier to entry for using large models.

[0019] 2. Improve development efficiency: The distributed training and optimization framework can significantly improve the training efficiency of large models. The automatic optimization module and model compression and quantization technology can quickly improve model performance and reduce model size. The efficient deployment and inference engine can quickly deploy the model to multiple hardware platforms and achieve efficient inference, thereby greatly improving the development and application efficiency of large models.

[0020] 3. Improve model performance: Automatic hyperparameter optimization algorithm and model structure search function can automatically explore the optimal model configuration. Model compression and quantization technology can improve the inference speed and energy efficiency of the model without significantly reducing performance. Inference acceleration technology can further optimize the inference process and ensure that the model can achieve the best performance on different hardware platforms.

[0021] 4. Enhance platform flexibility and scalability: Multi-tenant management and resource scheduling functions can meet the needs of different users and improve resource utilization; model management and version control functions can ensure the stability and traceability of models; plug-in function extension mechanism can facilitate users to carry out customized development according to actual needs, enhancing the platform's flexibility and scalability.

[0022] 5. Promote the widespread application of large-scale models: By addressing the pain points in the application of large-scale models, this invention enables large-scale models to be more widely used in various practical scenarios, such as intelligent customer service, intelligent writing, intelligent security, and intelligent healthcare, bringing more efficient and intelligent service experiences to various industries and promoting the development and application of artificial intelligence technology. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the intelligent platform architecture.

[0024] Figure 2 This is a flowchart of distributed training and optimization.

[0025] Figure 3 It is a flowchart for efficient deployment and inference. Detailed Implementation

[0026] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention. Example

[0027] This invention provides a method for implementing an intelligent service platform based on large-scale model optimization and efficient deployment, creating an intelligent service platform for large-scale model optimization and efficient deployment. The intelligent service platform adopts a distributed training architecture, which decomposes the training task of a large model into multiple computing nodes for parallel execution, thereby reducing the computing pressure on a single node. The intelligent service platform integrates automatic hyperparameter optimization algorithms, including Bayesian optimization algorithms and genetic algorithms, to automatically search for the optimal hyperparameter combination, thereby improving model performance. It also provides a model structure search function, which automatically explores better model structures through Neural Architecture Search (NAS). The intelligent service platform employs model compression and quantization methods to compress and quantize large models, reducing the number of model parameters and storage space, making the models more suitable for running on resource-constrained devices. The intelligent service platform uses deployment tools to seamlessly deploy optimized models to various hardware platforms, including but not limited to GPUs, CPUs, FPGAs, ASICs, and mobile devices. It employs inference acceleration methods to deeply optimize the model inference process, while supporting dynamic quantization and mixed-precision inference. It dynamically adjusts the model's accuracy and speed according to actual needs, monitors performance indicators during the model inference process in real time, and automatically optimizes and adjusts the inference engine based on monitoring data.

[0028] The intelligent service platform also provides a unified user interface, offering a complete workflow entry point for model training, optimization, deployment, and application. Users can upload data, configure training tasks, monitor training progress, view model performance metrics, deploy models, and call models for inference operations through the interface.

[0029] Furthermore, the intelligent service platform provides a multi-tenant management mode, offering independent resource space and access control for different users, ensuring data isolation and resource security among users. Simultaneously, it integrates intelligent resource scheduling algorithms to automatically allocate and schedule resources based on user task requirements and resource usage, thereby improving resource utilization and reducing operating costs.

[0030] Meanwhile, model management and version control are carried out through the intelligent service platform: a model management system is established to manage the entire lifecycle of model training, optimization, deployment and update, and to control model versions, backtrack and compare the performance of different versions of the model to ensure the stability and traceability of the model. At the same time, model sharing and collaboration functions are provided to facilitate model communication and cooperation among users.

[0031] Furthermore, the intelligent service platform can also adopt a plug-in architecture design, allowing the integration of various functional plug-ins, including data enhancement plug-ins, model interpretation plug-ins, and security detection plug-ins, to expand the platform's functionality and application scenarios. Example

[0032] The present invention also provides an intelligent service platform based on large model optimization and efficient deployment. The intelligent service platform adopts a distributed training architecture, which decomposes the training task of the large model into multiple computing nodes for parallel execution, thereby reducing the computing pressure on a single node. The intelligent service platform integrates automatic hyperparameter optimization algorithms, including Bayesian optimization algorithms and genetic algorithms. It uses automatic hyperparameter optimization algorithms to automatically search for the optimal hyperparameter combination to improve model performance. At the same time, it provides a model structure search function, which automatically explores better model structures through neural architecture search (NAS). The intelligent service platform employs model compression and quantization methods to compress and quantize large models, reducing the number of model parameters and storage space, making the models more suitable for running on resource-constrained devices. The intelligent service platform uses deployment tools to seamlessly deploy the optimized model to various hardware platforms, including but not limited to GPUs, CPUs, FPGAs, ASICs, and mobile devices. It employs inference acceleration methods to deeply optimize the model inference process, while supporting dynamic quantization and mixed-precision inference. It dynamically adjusts the model's accuracy and speed according to actual needs, monitors performance indicators during the model inference process in real time, and automatically optimizes and adjusts the inference engine based on monitoring data.

[0033] The information interaction and execution process of the aforementioned intelligent service platform are based on the same concept as the method embodiments of the present invention, and the specific details can be found in the descriptions in the method embodiments of the present invention, and will not be repeated here.

[0034] Similarly, the advantages of the intelligent service platform of the present invention are: 1. Lowering the barrier to entry: By providing a unified platform architecture and a simple user interface, the entire process of training, optimizing, deploying and applying large models is integrated, enabling ordinary users to easily use large models without having to delve into complex underlying technical details, thus lowering the barrier to entry for using large models.

[0035] 2. Improve development efficiency: The distributed training and optimization framework can significantly improve the training efficiency of large models. The automatic optimization module and model compression and quantization technology can quickly improve model performance and reduce model size. The efficient deployment and inference engine can quickly deploy the model to multiple hardware platforms and achieve efficient inference, thereby greatly improving the development and application efficiency of large models.

[0036] 3. Improve model performance: Automatic hyperparameter optimization algorithm and model structure search function can automatically explore the optimal model configuration. Model compression and quantization technology can improve the inference speed and energy efficiency of the model without significantly reducing performance. Inference acceleration technology can further optimize the inference process and ensure that the model can achieve the best performance on different hardware platforms.

[0037] 4. Enhance platform flexibility and scalability: Multi-tenant management and resource scheduling functions can meet the needs of different users and improve resource utilization; model management and version control functions can ensure the stability and traceability of models; plug-in function extension mechanism can facilitate users to carry out customized development according to actual needs, enhancing the platform's flexibility and scalability.

[0038] 5. Promote the widespread application of large-scale models: By addressing the pain points in the application of large-scale models, this invention enables large-scale models to be more widely used in various practical scenarios, such as intelligent customer service, intelligent writing, intelligent security, and intelligent healthcare, bringing more efficient and intelligent service experiences to various industries and promoting the development and application of artificial intelligence technology.

[0039] It should be noted that not all steps and modules in the above processes and platform structures are mandatory; some steps or modules can be omitted as needed. The execution order of each step is not fixed and can be adjusted as required. The platform structure described in the above embodiments can be a physical structure or a logical structure. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be jointly implemented by certain components in multiple independent devices.

[0040] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A method for implementing an intelligent service platform based on large-scale model optimization and efficient deployment, characterized by: Create an intelligent service platform for large-scale model optimization and efficient deployment. The intelligent service platform adopts a distributed training architecture, which decomposes the training task of a large model into multiple computing nodes for parallel execution, thereby reducing the computing pressure on a single node. The intelligent service platform integrates automatic hyperparameter optimization algorithms, including Bayesian optimization algorithms and genetic algorithms, to automatically search for the optimal hyperparameter combination, thereby improving model performance. It also provides a model structure search function, which automatically explores better model structures through Neural Architecture Search (NAS). The intelligent service platform employs model compression and quantization methods to compress and quantize large models, reducing the number of model parameters and storage space, making the models more suitable for running on resource-constrained devices. The intelligent service platform uses deployment tools to seamlessly deploy optimized models to various hardware platforms, including but not limited to GPUs, CPUs, FPGAs, ASICs, and mobile devices. It employs inference acceleration methods to deeply optimize the model inference process, while supporting dynamic quantization and mixed-precision inference. It dynamically adjusts the model's accuracy and speed according to actual needs, monitors performance indicators during the model inference process in real time, and automatically optimizes and adjusts the inference engine based on monitoring data.

2. The method for implementing an intelligent service platform based on large model optimization and efficient deployment according to claim 1, characterized in that: The intelligent service platform provides a unified user interface, offering a complete workflow for model training, optimization, deployment, and application. Users can upload data, configure training tasks, monitor training progress, view model performance metrics, deploy models, and call models for inference operations through the interface.

3. The method for implementing an intelligent service platform based on large-scale model optimization and efficient deployment according to claim 1, characterized in that: The intelligent service platform provides a multi-tenant management mode, offering independent resource space and access control for different users, ensuring data isolation and resource security among users. At the same time, it integrates intelligent resource scheduling algorithms to automatically allocate and schedule resources based on users' task requirements and resource usage, thereby improving resource utilization and reducing operating costs.

4. The method for implementing an intelligent service platform based on large-scale model optimization and efficient deployment according to claim 1, characterized in that: The intelligent service platform enables model management and version control: a model management system is established to manage the entire lifecycle of model training, optimization, deployment, and updates, and to control model versions, retrospectively compare the performance of different versions of the model, ensure model stability and traceability, and provide model sharing and collaboration functions to facilitate model exchange and cooperation among users.

5. The method for implementing an intelligent service platform based on large-scale model optimization and efficient deployment according to claim 1, characterized in that: The intelligent service platform adopts a plug-in architecture design, which allows the integration of various functional plug-ins, including data enhancement plug-ins, model interpretation plug-ins, and security detection plug-ins, to expand the platform's functions and application scenarios.

6. An intelligent service platform based on large-scale model optimization and efficient deployment, characterized by: The intelligent service platform adopts a distributed training architecture, which decomposes the training task of a large model into multiple computing nodes for parallel execution, reducing the computing pressure on a single node. The intelligent service platform integrates automatic hyperparameter optimization algorithms, including Bayesian optimization algorithms and genetic algorithms. It uses automatic hyperparameter optimization algorithms to automatically search for the optimal hyperparameter combination to improve model performance. At the same time, it provides a model structure search function, which automatically explores better model structures through neural architecture search (NAS). The intelligent service platform employs model compression and quantization methods to compress and quantize large models, reducing the number of model parameters and storage space, making the models more suitable for running on resource-constrained devices. The intelligent service platform uses deployment tools to seamlessly deploy the optimized model to various hardware platforms, including but not limited to GPUs, CPUs, FPGAs, ASICs, and mobile devices. It employs inference acceleration methods to deeply optimize the model inference process, while supporting dynamic quantization and mixed-precision inference. It dynamically adjusts the model's accuracy and speed according to actual needs, monitors performance indicators during the model inference process in real time, and automatically optimizes and adjusts the inference engine based on monitoring data.

7. The intelligent service platform based on large model optimization and efficient deployment according to claim 6, characterized in that: The intelligent service platform provides a unified user interface, offering a complete workflow entry point for model training, optimization, deployment, and application. Users can upload data, configure training tasks, monitor training progress, view model performance metrics, deploy models, and call models for inference operations through the interface.

8. The intelligent service platform based on large model optimization and efficient deployment according to claim 6, characterized in that: The intelligent service platform provides a multi-tenant management mode, offering independent resource space and access control for different users, ensuring data isolation and resource security among users. At the same time, it integrates intelligent resource scheduling algorithms to automatically allocate and schedule resources based on users' task requirements and resource usage, thereby improving resource utilization and reducing operating costs.

9. The intelligent service platform based on large model optimization and efficient deployment according to claim 6, characterized in that: The intelligent service platform manages and controls models: it establishes a model management system to manage the entire lifecycle of model training, optimization, deployment, and updates, and controls model versions, allowing for retrospective analysis and comparison of the performance of different versions to ensure model stability and traceability. It also provides model sharing and collaboration functions to facilitate model exchange and cooperation among users.

10. The intelligent service platform based on large model optimization and efficient deployment according to claim 6, characterized in that: The intelligent service platform adopts a plug-in architecture design, which allows the integration of various functional plug-ins, including data enhancement plug-ins, model interpretation plug-ins, and security detection plug-ins, to expand the platform's functions and application scenarios.

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