A hundred million parameter optimization platform

Abstract

The application discloses a kind of billion-level parameter optimization platform, comprising: parameter space component, parallel computing framework, crowd intelligence optimization algorithm unit, deep learning technology component and model evaluation and result analysis component, the parameter space component, parallel computing framework, crowd intelligence optimization algorithm unit, deep learning technology component and model evaluation and result analysis component are all connected with total control mechanism, utilize deep learning technology to model and predict parameter space, to further accelerate parameter search process, model evaluation and result analysis component.The application can quickly and efficiently find the best parameter, the platform uses a series of innovative technologies and algorithms, compared with existing parameter optimization method, with high efficiency, high precision and scalability.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and specifically relates to a platform for optimizing parameters at the level of hundreds of millions. Background Technology

[0002] Today, with the continuous development of deep learning and artificial intelligence technologies, optimizing ultra-large-scale parameters has become a crucial research problem in many fields. In numerous areas, such as natural language processing, computer vision, and speech recognition, it has been proven that using ultra-large-scale neural networks can achieve better performance. However, training these ultra-large-scale neural networks requires substantial computational resources and time, and necessitates optimizing a vast number of parameters, which is extremely challenging.

[0003] Traditional parameter optimization methods, such as stochastic gradient descent (SGD), often face many challenges when dealing with large-scale data and parameters. For example, they suffer from slow training speeds and a tendency to get trapped in local optima. Therefore, to address these issues, many improved algorithms have been proposed, such as batch normalization and adaptive gradients. However, these algorithms still have many problems.

[0004] High-dimensional problems refer to problems where the parameter space has a very high dimension, meaning there are a large number of parameters. For high-dimensional problems, the challenges of parameter optimization mainly lie in the following aspects: The curse of dimensionality: As the dimension of a parameter increases, the size of the parameter space grows exponentially, resulting in an extremely large search space and thus increasing the complexity of parameter search. This problem is commonly referred to as the curse of dimensionality.

[0005] Local Optimum: Numerous local optima exist in high-dimensional parameter spaces. While these optima offer good performance, they are not global optima. This makes finding the global optimum in high-dimensional parameter spaces significantly more difficult.

[0006] High computational resource requirements: Searching a high-dimensional parameter space requires a large amount of computational resources and time, especially when the parameter space is very large, which may exceed the capabilities of current computational resources.

[0007] Therefore, existing parameter optimization techniques still need to be improved. Summary of the Invention

[0008] Purpose of the invention: In order to overcome the above shortcomings, the purpose of this invention is to provide a platform for finding the best parameters at the level of hundreds of millions of parameters, which can quickly and efficiently find the optimal parameters. Compared with the prior art, it has high efficiency, high accuracy and good scalability.

[0009] Technical Solution: To achieve the above objectives, this invention provides a platform for optimizing parameters at the billion-level, comprising: The parameter space component is responsible for defining the range and values ​​of the parameter space, as well as the dependencies between different parameters; The parallel computing framework utilizes distributed computing and multi-process concurrency technology to decompose the parameter space into several subspaces, and the parallel computing distributes the parameter subspaces to different computing nodes for computation; the parallel computing framework includes a swarm optimization algorithm unit and a deep learning technology component. The swarm optimization algorithm unit uses a series of advanced swarm optimization algorithms to quickly search for the best parameters. These algorithms can avoid getting trapped in local optima and improve search accuracy and efficiency. Deep learning technology components utilize deep learning technology to model and predict the parameter space, thereby further accelerating the parameter search process; The model evaluation and results analysis component is responsible for evaluating the model performance corresponding to different parameter combinations, and summarizing and analyzing the results to help users better understand the search results. It can also provide auxiliary tools such as charts and data reports to help users better analyze and interpret the results. The parameter space component, parallel computing framework, swarm optimization algorithm unit, deep learning technology component, and model evaluation and result analysis component are all connected to the central control mechanism.

[0010] The parallel computing framework described in this invention needs to consider load balancing, performance optimization, program design, error handling, and fault recovery in order to ensure the efficiency and robustness of the parallel computing framework. The specific process of the parallel computing framework is as follows: 1): Decompose the problem: Decompose the large-scale task into multiple smaller tasks so that they can be processed in parallel. 2) Task allocation: Assign small tasks to different processors or computing units so that they can be processed simultaneously; 3) Executing computations: Each processor or computing unit independently executes its assigned tasks in order to process multiple tasks in parallel; 4) Merging calculation results: Merging the calculation results of each processor or computing unit to obtain the final calculation result; 5): Task completion: The entire computing task is completed when all processors or computing units have completed their tasks.

[0011] The parallel computing framework described in this invention divides the parameter space into multiple subspaces during the parameter optimization process, and then assigns different subspaces to different computing nodes for computation, which is an effective parallel computing method. This method can make full use of distributed computing and multi-process concurrency technology, fully utilize computing resources, and improve computing efficiency. The specific implementation method is as follows: a. Divide the subspace: The parameter space needs to be divided into multiple subspaces; the division method can be uniform division, random division, etc., and the specific method can be selected according to the specific situation; the subspaces after division should have the same size and be free of overlap and omission to ensure the accuracy of the calculation results; b. Allocating computing nodes: Different subspaces need to be allocated to different computing nodes for computation. Distributed computing frameworks, such as MPI and Hadoop, can be used to enable communication and collaboration between computing nodes. When allocating computing nodes, they should be allocated reasonably according to their computing capabilities and load conditions to make full use of computing resources and improve computing efficiency. c. Parallel computing: Parallel computing is performed on the subspace allocated to each computing node. Multi-process concurrency techniques, such as multithreading and coroutines, can be used to achieve parallel computing within the computing node. During parallel computing, attention should be paid to data synchronization and communication to ensure the correctness and consistency of the computing results. d. Summarize the calculation results: Summarize the results calculated by each computing node to obtain the optimal parameter combination. You can use the summarization functions provided by the distributed computing framework, such as the reduce function of MPI or the MapReduce framework of Hadoop, to summarize the calculation results. During the summarization process, you should take into account the issues of data transmission and computing efficiency to ensure the accuracy and efficiency of the summarized results.

[0012] The parameter space decomposition described in this invention is a systems engineering analysis method that can decompose the parameter space of a system into multiple subspaces according to certain rules, in order to conduct a more detailed and comprehensive study of the system. The specific decomposition rules and methods are as follows: a. Determine system parameters: The first step is to determine the system parameters, that is, to determine the parameters that have a decisive influence on the system behavior; b. Define the parameter range: For each parameter, it is necessary to determine its range, that is, the range of possible values ​​the parameter can take; c. Subspace partitioning: Dividing the parameter space into multiple subspaces, each containing a subset of parameter points. There are several methods for subspace partitioning, such as: - Uniform partitioning method: Divide the parameter range into several equal parts, each part corresponding to a subspace; - Hierarchical partitioning method: The parameter space is partitioned layer by layer from high dimension to low dimension, with each layer corresponding to a dimension, and each dimension is divided into subspaces according to certain rules; - Central partitioning method: The parameter space is divided into multiple spherical subspaces with the center point of the parameter range as the center; d. Define subspaces: Define each subspace, which can be done using natural language or mathematical formulas; e. Analyze subspaces: Analyze each subspace to obtain the behavioral characteristics of the system under that subspace; f. Comprehensive Analysis: By combining the analysis results of all subspaces, the overall behavioral characteristics of the system are obtained.

[0013] The specific computation process of the parallel computing described in this invention, which allocates the parameter subspace to different computing nodes for computation, is as follows: a: First, determine the partitioning method of the parameter space and parameter subspace. Usually, the parameter space is divided into several subspaces by uniform partitioning or random partitioning. b: Next, determine the number and allocation method of computing nodes. Based on the system's performance and task requirements, decide how many computing nodes to use and how to allocate these nodes to different computers or processors for computation. c: Determine the parameter subspace that each computing node needs to compute. After determining the parameter subspace that each node needs to process based on the number of computing nodes and the allocation method, allocate these subspaces to different computing nodes. d: Perform calculations. Each computing node performs calculations according to its assigned parameter subspace and transmits the calculation results to other nodes. Throughout the calculation process, different nodes need to communicate and synchronize to ensure the accuracy and consistency of the calculation results. e: Merge the calculation results. After all the calculation nodes have completed the calculation, the results calculated by each node need to be merged to obtain the final result of the entire parameter space. This step usually requires data reduction and aggregation operations.

[0014] The specific principles for dividing the parameter space into multiple subspaces in this invention can be determined according to specific application requirements, and typically include the following aspects: a. Characteristics of the objective function: If the objective function exhibits different characteristics in different parameter regions, such as unimodal, multimodal, or local optimum, the parameter space can be divided according to these characteristics; b. Constraints of the problem: In practical applications, there are often various constraints, such as feasibility constraints and resource limitations. The parameter space can be divided according to various constraints, and all parameter combinations that satisfy the constraints can be divided into the same subspace. The specific partitioning process can employ a divide-and-conquer approach, continuously dividing the parameter space into smaller subspaces until the number of parameter combinations in each subspace can be easily handled and solved. This can be achieved using binary search or the K-DTree method. It is important to note that the computational complexity and search efficiency of the algorithm must be considered during the partitioning of the parameter space and the selection of subspaces to avoid excessively subdividing the parameter space or performing too many searches, which could lead to excessively long computation times or low algorithm efficiency.

[0015] The algorithms used in the swarm optimization algorithm unit described in this invention are designed to quickly search for the optimal parameters. These include genetic algorithms, particle swarm optimization algorithms, model-based optimization algorithms, and distributed differential evolution algorithms. Other algorithms can also be selected according to the user's needs. The rotation of a specific algorithm should consider the following principles: 1) Problem characteristics: Different types of problems require different types of algorithms to solve them, so it is necessary to first understand the characteristics and requirements of the problem. For example, some problems require optimization of continuous functions, while others require optimization of discrete functions. 2) Algorithm complexity: Algorithm complexity is one of the important factors in evaluating the quality of an algorithm. Algorithms with high complexity will have a long computation time and may even be unable to handle large-scale problems. 3) Algorithm stability: Algorithm stability refers to the sensitivity of the algorithm to the initial solution and parameters. A highly stable algorithm can produce similar optimization results under different initial solutions and parameters. 4) Existing experience: Existing experience is one of the important bases for selecting an algorithm. If there is experience in solving similar problems, the previous experience can be used to select a suitable algorithm. 5) Algorithm Interpretability: Algorithm interpretability refers to the transparency and understandability of the algorithm's computation process. It enables better understanding and explanation of the algorithm's computation process and allows for effective adjustment and optimization. The deep learning technology component described in this invention utilizes deep learning technology to model and predict the parameter space, learning the interactions between parameters to better understand the characteristics and patterns of the parameter space and further optimize the parameter search process. In deep learning, the process of modeling the parameter space typically uses a neural network model. Specifically, each parameter in the parameter space is taken as input, and the neural network maps the input to the output, which is the corresponding prediction result. The training process of the neural network involves optimizing it using a large amount of data to minimize the error between the prediction and the actual result, thereby obtaining the optimal parameter values. Predicting unknown parameter spaces involves using existing data and models to predict the values ​​of unknown parameters. Specifically, this involves inputting unknown parameter values ​​and using a neural network to calculate the prediction results. The criteria for judging the prediction can be determined based on the specific application scenario. Error metrics such as mean squared error (MSE) or cross-entropy are commonly used to evaluate the model's predictive performance. If the model's prediction error is small, it is considered to have strong predictive ability and can predict unknown parameter values ​​well. The deep learning technology component is implemented as follows: 1) Data preparation: A set of labeled training data needs to be prepared, which includes model performance data corresponding to different parameter combinations. The training data should be representative and diverse to cover different regions and characteristics of the parameter space. At the same time, the training data needs to be preprocessed and normalized to ensure the reliability and accuracy of the training data. 2) Model Design: A deep learning model needs to be designed to learn the characteristics and rules of the parameter space. The deep learning model can adopt some common neural network structures, such as convolutional neural networks and recurrent neural networks, or it can customize some special network structures to adapt to different application scenarios. The deep learning model should include an input layer, a hidden layer and an output layer, where the input layer is the parameter space and the output layer is the model performance data.

[0016] 3) Training the model: The deep learning model needs to be trained using training data. Some optimization algorithms can be used in the training process, such as stochastic gradient descent and backpropagation, to minimize the prediction error. During the training process, attention should be paid to the allocation of training data and cross-validation to prevent overfitting and underfitting. 4) Parameter prediction: The trained deep learning model is used to model and predict the parameter space. The deep learning model can be used to predict the performance of the model with known parameter combinations, or it can be used to predict the parameter space that has not been explored. The prediction results can be used to guide the parameter search process and improve the search efficiency and accuracy.

[0017] The specific process of data preprocessing and normalization in step 1) of the implementation method of the deep learning technology component described in this invention is as follows: 1) Data cleaning: Remove duplicate, missing, and outlier data to ensure data quality and accuracy; 2) Feature selection: Select features that have an impact on the prediction results, reduce dimensionality, and improve model performance; 3) Feature scaling: Scaling the numerical range of each feature to a uniform range to avoid adverse effects on model training due to some feature values ​​being too large or too small. Common feature scaling methods include z-score normalization and min-max scaling. 4) Data normalization: scaling data to a uniform range between 0 and 1 to make the data comparable; common normalization methods include: min-max scaling and z-score normalization. 5) Dataset partitioning: Divide the dataset into training set, validation set, and test set for model training, tuning, and testing; 6) Category label conversion: Convert category labels into numerical values ​​to facilitate processing by machine learning algorithms; 7) Feature engineering: Further processing of features, such as combination, decomposition, and discretization, to improve model performance and prediction accuracy; 8) Data dimensionality reduction: Use algorithms such as PCA to reduce high-dimensional data to low-dimensional data, thereby reducing the complexity of model training.

[0018] The deep learning modeling process in the implementation of the deep learning technology component described in this invention is as follows: 1) Data preprocessing: Preprocessing the raw data, including data cleaning, data normalization, and data segmentation; 2) Model selection: Select an appropriate model based on the characteristics of the problem and the data structure, such as convolutional neural networks, recurrent neural networks, and deep autoencoders; 3) Model building: Constructing the model structure, including the input layer, hidden layers, output layer, and activation function; 4) Parameter initialization: Initialize the model parameters to make them easier to adjust during training; 5) Loss function selection: Choose an appropriate loss function, such as cross-entropy loss or mean squared error; 6) Training the model: Train the model using the training set and adjust the parameters using optimization algorithms, such as gradient descent and Adam. 7) Model evaluation: Evaluate the trained model, including metrics such as accuracy, recall, F1 score, and AUC.

[0019] 8) Model tuning: Based on the evaluation results, the model is tuned, including adjusting parameters, increasing the number of network layers, and using different optimization algorithms; 9) Model application: Applying the trained model to real-world problems, such as image classification and speech recognition; The training process of the deep learning model is as follows: 1) Data preparation: Preprocess the data, divide it into training and test sets, and ensure the quality and applicability of the data; 2) Model building: Based on task requirements and data characteristics, select appropriate model structure and parameters to build a deep learning model; 3) Loss function definition: Based on task requirements, select an appropriate loss function to measure the difference between the model's predicted results and the actual results.

[0020] 4) Model training: The model is trained using the training set, and the model parameters are continuously updated through optimization algorithms (such as gradient descent) to minimize the loss function.

[0021] 5) Model Evaluation: Use the test set to evaluate the model, calculate the model's loss value and accuracy on the test set, and other metrics to determine the model's performance and generalization ability.

[0022] 6) Parameter tuning: Based on the model evaluation results, adjust the model structure and parameters, optimize model performance, and verify the model's generalization ability through cross-validation of the training and test sets and other methods.

[0023] 7) Model saving and use: Save the optimal model parameters for subsequent prediction, classification, clustering or other tasks.

[0024] As can be seen from the above technical solution, the present invention has the following beneficial effects: 1. The parameter optimization platform for hundreds of millions of parameters described in this invention is an ultra-large-scale parameter optimization platform that can quickly and efficiently find the optimal parameters. The platform uses a series of innovative technologies and algorithms, including distributed computing, multi-process concurrency, swarm optimization, and deep learning. Compared with existing parameter optimization methods, the platform has the following advantages: Efficiency: The platform can utilize multiple computers or cloud servers for parallel computing, which greatly shortens the parameter search time.

[0025] High accuracy: The platform uses advanced parameter optimization algorithms, which can quickly search for the best parameters, improving the performance of the algorithm and model.

[0026] Scalability: The platform's architecture is highly scalable, capable of meeting parameter optimization tasks of varying scales and complexities.

[0027] 2. The parallel computing framework in this invention utilizes distributed computing and multi-process concurrency techniques to decompose the parameter space into several subspaces and assign different subspaces to different computing nodes for computation. This can significantly reduce computation time while improving the utilization rate of computing resources.

[0028] 3. The swarm optimization algorithm component employs a series of advanced swarm optimization algorithms, such as genetic algorithms and particle swarm optimization, to quickly search for optimal parameters. These algorithms can avoid getting trapped in local optima, improving search accuracy and efficiency.

[0029] 4. The deep learning technology component utilizes deep learning technology to model and predict the parameter space, further accelerating the parameter search process. This component can learn the interactions between parameters and predict unexplored parameter spaces, improving search efficiency. The process of deep learning technology modeling the parameter space is described below. Each parameter in the parameter space is taken as input, and a neural network maps the input to the output, which is the corresponding prediction result. The training process of the neural network involves optimizing it with a large amount of data to minimize the error between the prediction result and the actual result, thereby obtaining the optimal parameter values. It also predicts unknown parameter values ​​by using existing data and the model to predict unknown parameter values. This can be done by inputting unknown parameter values ​​and calculating them through the neural network to obtain the corresponding prediction result. If the model's prediction error is small, it is considered to have strong predictive ability and can predict unknown parameter values ​​well.

[0030] 5. The model evaluation and results analysis component is responsible for evaluating the model performance corresponding to different parameter combinations, and summarizing and analyzing the results to help users better understand the search results. This component can also provide auxiliary tools such as charts and data reports to help users better analyze and interpret the results. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the structure of the billion-parameter optimization platform described in this invention; Figure 2 This is a decomposed coordinate diagram of the multi-object space in this invention; Figure 3 This is a schematic coordinate diagram of the boundary intersection method in this invention; Figure 4 This is a schematic coordinate diagram of the boundary intersection method with penalty terms in this invention; Figure 5 This is a flowchart of the genetic algorithm in this invention; Figure 6 This is a flowchart of the particle swarm algorithm in this invention; Figure 7 This is a flowchart of the differential evolution algorithm in this invention; Figure 8 This is a schematic diagram of the module connections of the billion-level parameter optimization platform in this invention. Detailed Implementation

[0032] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0033] Example 1 1. A parameter optimization platform with hundreds of millions of parameters, comprising: The parameter space component is responsible for defining the range and values ​​of the parameter space, as well as the dependencies between different parameters; The parallel computing framework utilizes distributed computing and multi-process concurrency technology to decompose the parameter space into several subspaces, and the parallel computing distributes the parameter subspaces to different computing nodes for computation; the parallel computing framework includes a swarm optimization algorithm unit and a deep learning technology component. The swarm optimization algorithm unit uses a series of advanced swarm optimization algorithms to quickly search for the best parameters. These algorithms can avoid getting trapped in local optima and improve search accuracy and efficiency. Deep learning technology components utilize deep learning technology to model and predict the parameter space, thereby further accelerating the parameter search process; The model evaluation and results analysis component is responsible for evaluating the model performance corresponding to different parameter combinations, and summarizing and analyzing the results to help users better understand the search results. It can also provide auxiliary tools such as charts and data reports to help users better analyze and interpret the results. The parameter space component, parallel computing framework, swarm optimization algorithm unit, deep learning technology component, and model evaluation and result analysis component are all connected to the central control mechanism.

[0034] In this embodiment, this component is responsible for defining the range and values ​​of the parameter space, as well as the dependencies between different parameters. For example, for a deep learning model, the parameter space may include parameters such as learning rate, batch size, and weight decay. The specific range and values ​​of the parameter space will vary depending on the specific deep learning model and task. Generally, the learning rate can be between 0.001 and 0.1, the batch size can be between 16 and 512, and the weight decay can be between 0.0001 and 0.1.

[0035] There are certain relationships between different parameters. For example, the values ​​of learning rate and batch size affect the model's convergence speed and generalization ability. Generally speaking, a larger learning rate results in faster convergence but a higher risk of overfitting; a smaller learning rate results in slower convergence but better generalization. A larger batch size makes the model's update direction more accurate but increases computational complexity and memory consumption; a smaller batch size makes the model's update direction more random but reduces computational complexity and memory consumption. Weight decay can be used to prevent overfitting, but excessive weight decay can lead to underfitting. Therefore, adjustments need to be made based on specific circumstances to find the most suitable parameter combination.

[0036] In this embodiment, the parallel computing framework needs to consider load balancing, performance optimization, program design, error handling, and fault recovery to ensure the efficiency and robustness of the parallel computing framework. The specific process of the parallel computing framework is as follows: 1): Decompose the problem: Decompose the large-scale task into multiple smaller tasks so that they can be processed in parallel. 2) Task allocation: Assign small tasks to different processors or computing units so that they can be processed simultaneously; 3) Executing computations: Each processor or computing unit independently executes its assigned tasks in order to process multiple tasks in parallel; 4) Merging calculation results: Merging the calculation results of each processor or computing unit to obtain the final calculation result; 5): Task completion: The entire computing task is completed when all processors or computing units have completed their tasks.

[0037] In this embodiment, the parallel computing framework divides the parameter space into multiple subspaces during parameter optimization and then assigns different subspaces to different computing nodes for computation, which is an effective parallel computing method. This method can make full use of computing resources and improve computing efficiency by utilizing distributed computing and multi-process concurrency technology. The specific implementation method is as follows: a. Divide the subspace: The parameter space needs to be divided into multiple subspaces; the division method can be uniform division, random division, etc., and the specific method can be selected according to the specific situation; the subspaces after division should have the same size and be free of overlap and omission to ensure the accuracy of the calculation results; b. Allocating computing nodes: Different subspaces need to be allocated to different computing nodes for computation. Distributed computing frameworks, such as MPI and Hadoop, can be used to enable communication and collaboration between computing nodes. When allocating computing nodes, they should be allocated reasonably according to their computing capabilities and load conditions to make full use of computing resources and improve computing efficiency. c. Parallel computing: Parallel computing is performed on the subspace allocated to each computing node. Multi-process concurrency techniques, such as multithreading and coroutines, can be used to achieve parallel computing within the computing node. During parallel computing, attention should be paid to data synchronization and communication to ensure the correctness and consistency of the computing results. d. Summarize the calculation results: Summarize the results calculated by each computing node to obtain the optimal parameter combination. You can use the summarization functions provided by the distributed computing framework, such as the reduce function of MPI or the MapReduce framework of Hadoop, to summarize the calculation results. During the summarization process, you should take into account the issues of data transmission and computing efficiency to ensure the accuracy and efficiency of the summarized results.

[0038] It should be noted that distributed computing refers to the process of allocating and coordinating tasks among multiple computer nodes to complete large-scale computing tasks.

[0039] It divides the computational task and data into multiple parts, distributes them across different computers for computation, and finally aggregates the results. The advantages of distributed computing systems are that they can improve computing power and achieve high availability and fault tolerance.

[0040] Multiprocess concurrency refers to the simultaneous execution of multiple processes within the same program. Each process performs its tasks independently and can share resources. When multiple tasks need to be processed concurrently, multiprocess concurrency can improve program execution efficiency and reduce waiting time. Multiprocess concurrency requires consideration of coordination and synchronization between the processes to ensure the program's correctness and stability.

[0041] In summary, both distributed computing and multi-process concurrency aim to improve computational efficiency and program execution efficiency. Distributed computing emphasizes coordinating multiple nodes to complete a task, while multi-process concurrency emphasizes the coordination and sharing among multiple processes on the same computer. In practical applications, the two can be used in combination to achieve more efficient computation and processing.

[0042] The parameter space decomposition described in this embodiment is a systems engineering analysis method that can decompose the parameter space of a system into multiple subspaces according to certain rules, so as to conduct a more detailed and comprehensive study of the system. The specific decomposition rules and methods are as follows: a. Determine system parameters: The first step is to determine the system parameters, that is, to determine the parameters that have a decisive influence on the system behavior; b. Define the parameter range: For each parameter, it is necessary to determine its range, that is, the range of possible values ​​the parameter can take; c. Subspace partitioning: Dividing the parameter space into multiple subspaces, each containing a subset of parameter points. There are several methods for subspace partitioning, such as: - Uniform partitioning method: Divide the parameter range into several equal parts, each part corresponding to a subspace; - Hierarchical partitioning method: The parameter space is partitioned layer by layer from high dimension to low dimension, with each layer corresponding to a dimension, and each dimension is divided into subspaces according to certain rules; - Central partitioning method: The parameter space is divided into multiple spherical subspaces with the center point of the parameter range as the center; d. Define subspaces: Define each subspace, which can be done using natural language or mathematical formulas; e. Analyze subspaces: Analyze each subspace to obtain the behavioral characteristics of the system under that subspace; f. Comprehensive Analysis: By combining the analysis results of all subspaces, the overall behavioral characteristics of the system are obtained.

[0043] In this embodiment, the parallel computing process of allocating the parameter subspace to different computing nodes for computation is as follows: a: First, determine the division method of the parameter space and parameter subspace. Usually, the parameter space is divided into several subspaces by uniform division or random division. b: Next, determine the number and allocation method of computing nodes. Based on the system's performance and task requirements, decide how many computing nodes to use and how to allocate these nodes to different computers or processors for computation. c: Determine the parameter subspace that each computing node needs to compute. After determining the parameter subspace that each node needs to process based on the number of computing nodes and the allocation method, allocate these subspaces to different computing nodes. d: Perform calculations. Each computing node performs calculations according to its assigned parameter subspace and transmits the calculation results to other nodes. Throughout the calculation process, different nodes need to communicate and synchronize to ensure the accuracy and consistency of the calculation results. e: Merge the calculation results. After all the calculation nodes have completed the calculation, the results calculated by each node need to be merged to obtain the final result of the entire parameter space. This step usually requires data reduction and aggregation operations.

[0044] In this embodiment, the specific principles for dividing the parameter space into multiple subspaces can be determined based on specific application requirements, and typically include the following aspects: a. Characteristics of the objective function: If the objective function exhibits different characteristics in different parameter regions, such as unimodal, multimodal, or local optimum, the parameter space can be divided according to these characteristics; b. Constraints of the problem: In practical applications, there are often various constraints, such as feasibility constraints and resource limitations. The parameter space can be divided according to various constraints, and all parameter combinations that satisfy the constraints can be divided into the same subspace.

[0045] The specific partitioning process can employ a divide-and-conquer approach, continuously dividing the parameter space into smaller subspaces until the number of parameter combinations in each subspace can be easily handled and solved. This can be achieved using binary search or the K-DTree method. It is important to note that the computational complexity and search efficiency of the algorithm must be considered during the partitioning of the parameter space and the selection of subspaces to avoid excessively subdividing the parameter space or performing too many searches, which could lead to excessively long computation times or low algorithm efficiency.

[0046] The algorithms used in the swarm optimization algorithm unit described in this embodiment are designed to quickly search for the best parameters. These include genetic algorithms, particle swarm optimization algorithms, model-based optimization algorithms, and distributed differential evolution algorithms. Other algorithms can also be selected according to the user's needs. The rotation of a specific algorithm should consider the following principles: 1) Problem characteristics: Different types of problems require different types of algorithms to solve them, so it is necessary to first understand the characteristics and requirements of the problem. For example, some problems require optimization of continuous functions, while others require optimization of discrete functions. 2) Algorithm complexity: Algorithm complexity is one of the important factors in evaluating the quality of an algorithm. Algorithms with high complexity will have a long computation time and may even be unable to handle large-scale problems. 3) Algorithm stability: Algorithm stability refers to the sensitivity of the algorithm to the initial solution and parameters. A highly stable algorithm can produce similar optimization results under different initial solutions and parameters. 4) Existing experience: Existing experience is one of the important bases for selecting an algorithm. If there is experience in solving similar problems, the previous experience can be used to select a suitable algorithm. 5) Algorithm interpretability: Algorithm interpretability refers to the transparency and understandability of the algorithm's computation process, enabling better understanding and explanation of the algorithm's computation process, and allowing for effective adjustment and optimization of the algorithm.

[0047] Genetic Algorithm: A genetic algorithm is a heuristic search algorithm that can be applied to parameter optimization. In a distributed computing environment, the population can be divided into multiple subpopulations, and then the operations of the genetic algorithm, such as selection, crossover, and mutation, can be executed in parallel on different computing nodes to improve search efficiency.

[0048] Particle Swarm Optimization (PSO): PSO is an optimization algorithm based on swarm intelligence that can be applied to parameter optimization. In a distributed computing environment, the particle swarm can be divided into multiple sub-swarms and computed in parallel on different computing nodes to accelerate the optimization process.

[0049] Model-based optimization algorithms: Model-based optimization algorithms typically transform the parameter search problem into a function optimization problem, and then use some efficient optimization algorithms, such as Bayesian optimization algorithm and Gaussian process regression, to perform optimization calculations in a distributed computing environment.

[0050] Parallel hill-climbing algorithm: The parallel hill-climbing algorithm is an optimization algorithm based on local search that can be applied to parameter optimization. In a distributed computing environment, the search space can be divided into multiple subspaces, and then local search operations can be executed in parallel on different computing nodes to improve search efficiency.

[0051] Distributed Differential Evolutionary Algorithm: Distributed Differential Evolutionary Algorithm is a heuristic optimization algorithm that can be applied to parameter optimization. In a distributed computing environment, the population can be divided into multiple subpopulations, and the operations of the differential evolution algorithm, such as selection, crossover, and mutation, can be executed in parallel on different computing nodes to improve search efficiency.

[0052] The deep learning technology component described in this embodiment uses deep learning technology to model and predict the parameter space, and can learn the mutual influence between parameters, thereby better understanding the characteristics and laws of the parameter space and further optimizing the parameter search process; In deep learning, the process of modeling the parameter space typically uses a neural network model. Specifically, each parameter in the parameter space is taken as input, and the neural network maps the input to the output, which is the corresponding prediction result. The training process of the neural network involves optimizing it using a large amount of data to minimize the error between the prediction and the actual result, thereby obtaining the optimal parameter values. Predicting unknown parameter spaces involves using existing data and models to predict the values ​​of unknown parameters. Specifically, this involves inputting unknown parameter values ​​and using a neural network to calculate the prediction results. The criteria for judging the prediction can be determined based on the specific application scenario. Error metrics such as mean squared error (MSE) or cross-entropy are commonly used to evaluate the model's predictive performance. If the model's prediction error is small, it is considered to have strong predictive ability and can predict unknown parameter values ​​well. The deep learning technology component is implemented as follows: 1) Data preparation: A set of labeled training data needs to be prepared, which includes model performance data corresponding to different parameter combinations. The training data should be representative and diverse to cover different regions and characteristics of the parameter space. At the same time, the training data needs to be preprocessed and normalized to ensure the reliability and accuracy of the training data. 2) Model Design: A deep learning model needs to be designed to learn the characteristics and rules of the parameter space. The deep learning model can adopt some common neural network structures, such as convolutional neural networks and recurrent neural networks, or it can customize some special network structures to adapt to different application scenarios. The deep learning model should include an input layer, a hidden layer and an output layer, where the input layer is the parameter space and the output layer is the model performance data.

[0053] 3) Training the model: The deep learning model needs to be trained using training data. Some optimization algorithms can be used in the training process, such as stochastic gradient descent and backpropagation, to minimize the prediction error. During the training process, attention should be paid to the allocation of training data and cross-validation to prevent overfitting and underfitting. 4) Parameter prediction: The trained deep learning model is used to model and predict the parameter space. The deep learning model can be used to predict the performance of the model with known parameter combinations, or it can be used to predict the parameter space that has not been explored. The prediction results can be used to guide the parameter search process and improve the search efficiency and accuracy.

[0054] The specific process of data preprocessing and normalization in step 1) of the implementation method of the deep learning technology component in this embodiment is as follows: 1) Data cleaning: Remove duplicate, missing, and outlier data to ensure data quality and accuracy; 2) Feature selection: Select features that have an impact on the prediction results, reduce dimensionality, and improve model performance; 3) Feature scaling: Scaling the numerical range of each feature to a uniform range to avoid adverse effects on model training due to some feature values ​​being too large or too small. Common feature scaling methods include z-score normalization and min-max scaling. 4) Data normalization: scaling data to a uniform range between 0 and 1 to make the data comparable; common normalization methods include: min-max scaling and z-score normalization. 5) Dataset partitioning: Divide the dataset into training set, validation set, and test set for model training, tuning, and testing; 6) Category label conversion: Convert category labels into numerical values ​​to facilitate processing by machine learning algorithms; 7) Feature engineering: Further processing of features, such as combination, decomposition, and discretization, to improve model performance and prediction accuracy; 8) Data dimensionality reduction: Use algorithms such as PCA to reduce high-dimensional data to low-dimensional data, thereby reducing the complexity of model training.

[0055] The deep learning modeling process in the implementation of the deep learning technology component described in this embodiment is as follows: 1) Data preprocessing: Preprocessing the raw data, including data cleaning, data normalization, and data segmentation; 2) Model selection: Select an appropriate model based on the characteristics of the problem and the data structure, such as convolutional neural networks, recurrent neural networks, and deep autoencoders; 3) Model building: Constructing the model structure, including the input layer, hidden layers, output layer, and activation function; 4) Parameter initialization: Initialize the model parameters to make them easier to adjust during training; 5) Loss function selection: Choose an appropriate loss function, such as cross-entropy loss or mean squared error; 6) Training the model: Train the model using the training set and adjust the parameters using optimization algorithms, such as gradient descent and Adam. 7) Model evaluation: Evaluate the trained model, including metrics such as accuracy, recall, F1 score, and AUC.

[0056] 8) Model tuning: Based on the evaluation results, the model is tuned, including adjusting parameters, increasing the number of network layers, and using different optimization algorithms; 9) Model application: Applying the trained model to real-world problems, such as image classification and speech recognition; The training process of the deep learning model is as follows: 1) Data preparation: Preprocess the data, divide it into training and test sets, and ensure the quality and applicability of the data; 2) Model building: Based on task requirements and data characteristics, select appropriate model structure and parameters to build a deep learning model; 3) Loss function definition: Based on task requirements, select an appropriate loss function to measure the difference between the model's predicted results and the actual results.

[0057] 4) Model training: The model is trained using the training set, and the model parameters are continuously updated through optimization algorithms (such as gradient descent) to minimize the loss function.

[0058] 5) Model Evaluation: Use the test set to evaluate the model, calculate the model's loss value and accuracy on the test set, and other metrics to determine the model's performance and generalization ability.

[0059] 6) Parameter tuning: Based on the model evaluation results, adjust the model structure and parameters, optimize model performance, and verify the model's generalization ability through cross-validation of the training and test sets and other methods.

[0060] 8) Model saving and use: Save the optimal model parameters for subsequent prediction, classification, clustering or other tasks.

[0061] Example 2 The parameter optimization platform with hundreds of millions of parameters described in this embodiment has the same composition structure as that in Embodiment 1, with the following differences: The parameter optimization strategy in the parameter optimization platform described in this embodiment is explained as follows: Many real-world optimization problems require considering multiple conflicting optimization objectives simultaneously. For example, chip design must consider performance, power consumption, and cost at the same time, while financial activities require considering both returns and risks. Similar problems can be represented as multi-objective optimization problems in the following form.

[0062] (1) For a multi-objective optimization problem with d objective functions, assume the objective function vector... Let u and v be two solutions in the target space. If u and v satisfy (2), then the solutions u and v are said to have a relationship where u dominates v.

[0063] (2) If, in the entire parameter space, there is no point where the objective function vector dominates the objective function vector of a certain point, then that point is called a Pareto Optimal for the corresponding multi-objective optimization problem. The objective function vectors of the set of all Pareto Optimals constitute the Pareto Front for the corresponding multi-objective optimization problem.

[0064] Finding as many Pareto optimal solutions as possible with high efficiency and approaching the Pareto front is the standard for measuring the performance of multi-objective global optimization algorithms. Since multi-objective optimization problems do not have a single numerical standard (objective function value) to measure the quality of all parameters as in single-objective problems, it is necessary to compare the points with equation (2) to determine the dominance relationship. Therefore, solving multi-objective optimization problems is much more complicated.

[0065] 1. Multi-objective optimization algorithm based on objective space decomposition Currently, there are two main types of multi-objective optimization algorithms based on evolutionary computation: one is based on NSGA-ii, ... Algorithms based on non-dominated sorting techniques, such as NSGA-iii, determine the quality of individual solutions in a population by stratifying individuals according to their dominance in the target space, assigning a unique index to each solution, and thus retaining non-dominated and high-ranking individuals. However, non-dominated sorting is computationally expensive, with a complexity of O(n log n). N is the number of individuals, and M is the number of targets.

[0066] Another type of multi-objective optimization method based on objective decomposition is represented by MOEA / D. Its basic idea is not to directly calculate the dominance relationships between individuals, but to construct reference directions using reference points and a set of weight vectors uniformly distributed in the objective space. This transforms the multi-objective optimization problem into finding scalar quantum problems in each reference direction, solving multiple subproblems to approximate the Pareto front of the objective space. Taking a 2-objective problem as an example, the objective space decomposition method is as follows: Figure 2 As shown.

[0067] Boundary crossing method and boundary crossing method with penalty term are two commonly used goal space decomposition methods. The idea of ​​boundary crossing method is to find the intersection point of Pareto front and weight vector, and transform the multi-objective optimization problem into a subproblem of the form of Equation (3) using weight vector and reference point.

[0068] (3) The boundary crossing method with penalty terms removes the equality constraint in equation (3), and its subproblem form is shown in equation (4), which is easier to implement in practice.

[0069] (4) Optimizing multiple subproblems generated by both methods can approximate the intersection of the weight vector and the Pareto front. The two methods can be respectively used... Figure 3 and Figure 4 This is represented as follows. Ideally, using a reasonable target space reference point and a reference vector that can well cover the Pareto front, by solving the subproblems generated in the target space decomposition method, an approximation of the Pareto front can be obtained. This is the basic idea of ​​a decomposition-based multi-objective optimization algorithm. Assuming there are m optimization objectives, an integer H can be pre-set, and then each component in the m-dimensional weights can be derived from... Take values ​​sequentially and combine them to generate A uniformly distributed weight vector is used to calculate the reference direction vector. 2. Parallel computing training strategy. Initialize: Global parameter Number of working nodes K ， Global iteration number T ， Communication interval M ， Learning rate Differential evolution iteration number L for t = 0, 1, ..., T-1 do Read current global model parameters for m = 0, 1, ..., M-1 do Samples are randomly drawn from the training set S or obtained online (or in mini-batch). renew end for Synchronous communication obtains parameters from all nodes .

[0070] The specific details of each algorithm are as follows: Genetic algorithms are optimization algorithms based on the theory of biological evolution. They find the global optimum by simulating the process of heredity and evolution. Advantages: They have high search accuracy and scalability, and can handle large-scale, high-dimensional optimization problems. The computation process of a genetic algorithm generally includes the following steps: 1) Initialize the population: Randomly generate a set of initial solutions, i.e., the population. The population size is usually from tens to hundreds. 2) Fitness assessment: The fitness value of each individual is evaluated using a fitness function. The fitness value represents the degree to which an individual solves problems. 3) Selection operation: Select a certain number of individuals as the parents of the next generation of the population. Selection methods include roulette wheel selection, tournament selection, etc. 4) Crossover operation: Perform a crossover operation on the selected individuals to generate new individual solutions; 5) Mutation operation: Perform a mutation operation on the newly generated individuals to produce new solutions; 6) Replacement operation: Replace the original individuals with the newly generated individuals to generate a new population; 7) Determine the termination condition: If the termination condition (such as fitness value, number of iterations, etc.) is met, return the optimal solution; otherwise, repeat the above steps. The above is the general calculation process of genetic algorithms. The specific implementation needs to be adjusted and optimized according to different problems.

[0071] Particle Swarm Optimization (PSO) is a swarm intelligence-based optimization algorithm that searches for the global optimum by simulating the behavior of biological populations. Advantages: It boasts high convergence speed and search accuracy, and can overcome the problem of local optima. Applications: Suitable for high-dimensional optimization problems requiring a global optimum, such as those in machine learning and neural networks.

[0072] The calculation process of the particle swarm optimization algorithm is as follows: 1) Initialize the particle swarm A certain number of particles are randomly generated. Each particle has a certain position and velocity. The position represents a feasible solution, and the velocity represents the direction and distance of the particle's movement. 2) Assess the fitness of the particles. Substitute the position of each particle into the objective function to calculate the fitness score; 3) Update the global optimal solution and the individual optimal solution. The fitness of all particles is compared with the global optimal solution and the individual optimal solution, and the optimal solution is updated accordingly. 4) Update particle velocity and position Based on the global optimal solution and the individual optimal solution, the velocity and position of each particle are recalculated. 5) Repeat steps 2-4 until the termination condition is met. By setting conditions such as the maximum number of iterations or the target fitness value, steps 2)-4) are executed repeatedly until the termination condition is met; 6) Output the optimal solution. The optimal solution obtained by the particle swarm optimization algorithm is the solution obtained during the iteration process.

[0073] It should be noted that when performing steps 2) and 3), the fitness function may need to be optimized to improve the efficiency and accuracy of the algorithm.

[0074] Parallel hill-climbing is a parallel optimization algorithm based on hill-climbing. It improves search efficiency by using multiple subprocesses to search in parallel. Advantages: It has high search speed and accuracy, and can handle large-scale, high-dimensional optimization problems.

[0075] Application scenarios: Suitable for high-dimensional optimization problems that require fast search for local optima, such as optimization parameter tuning and logistics optimization.

[0076] The calculation process of the parallel hill-climbing method is as follows: 1) Initialize a set of initial solutions, assuming there are n solutions; 2) Evaluate each solution in parallel to obtain the objective function value for each solution; 3) Select the optimal solution based on the objective function value. If the objective function value of the optimal solution satisfies the termination condition, stop the calculation and output the optimal solution; otherwise, go to step 4). 4) Randomly select a set of solutions, make a small change to each solution, and obtain a new set of solutions. Assume there are m solutions. 5) Evaluate each new solution in parallel to obtain the objective function value for each new solution; 6) Select the optimal solution based on the objective function value. If the optimal solution is better than the previous optimal solution, update the optimal solution and return to step 3); otherwise, return to step 4). Distributed Differential Evolutionary Algorithm (DEA) is a distributed optimization algorithm based on Differential Evolutionary Algorithm. It effectively reduces computation time by solving the problem in parallel across multiple computing nodes. Advantages: High parallelism and scalability, capable of handling large-scale, high-dimensional optimization problems. Disadvantages: High computational resource requirements, necessitating a large number of computing nodes, and high algorithm complexity. Applications: Suitable for high-dimensional optimization problems requiring significant computational resources, such as neural network training, image processing, and data mining.

[0077] The computation process of the distributed differential evolution algorithm is as follows: 1) Initialize the population First, the population needs to be initialized. Based on the characteristics of the problem, the number of attributes for each individual needs to be determined, and initial individuals are randomly generated. The population size is generally preset, usually depending on the scale of the problem. Simultaneously, the number of generations and the mutation and crossover factors for each individual need to be set. 2) Mutation operation The mutation operation is the core operation of the differential evolution algorithm. Three distinct individuals are selected, denoted as r1, r2, and r3, where r1 and r2 are randomly selected, and r3 is the neighboring individual of the current individual. Then, the difference vector v = r1 - r2 is calculated, and the difference vector v is mutated with the current individual to obtain the mutated individual u = xi + F × v, where F is the mutation factor, xi is the current individual, and u is the mutated individual. 3) Cross operations Crossover is the process of generating a new individual from a mutated individual u and the current individual xi. The crossover factor CR (usually between [0,1]) is used to control the generation of the new individual, that is, some genes of the new individual are taken from u, while some genes retain the values ​​of the original individual xi. 4) Select Operation The selection operation stores newly generated individuals into the population and selects the best individual as the initial population for the next round of evolution. 5) Termination conditions: The algorithm stops iterating when a certain number of iterations is reached or the fitness value meets certain requirements. 6) Output results: Output the optimal solution and its corresponding fitness value.

[0078] Model-based optimization algorithms are optimization algorithms that rely on mathematical models. They construct a mathematical model of the objective function and then use the optimization algorithm to find the optimal solution. Advantages: They can efficiently search complex high-dimensional problems, exhibiting high convergence speed and search accuracy. Disadvantages: They require a high degree of model accuracy; inaccurate models can lead to significant deviations in the results. Application scenarios: They are suitable for problems requiring accurate modeling of the objective function, such as optimization problems in engineering, economics, and finance.

[0079] The computation process of model-based optimization algorithms can be divided into the following steps: 1) Determine the model: Select an appropriate model based on the actual problem, such as a linear model or a nonlinear model; 2) Establish the objective function: Determine the objective function based on the actual problem, such as minimizing error or maximizing profit; 3) Determine the optimization algorithm: Based on the model and objective function, select a suitable optimization algorithm, such as gradient descent, genetic algorithm, etc. 4) Set initial parameters: Based on the model and objective function, set initial parameter values, such as regression coefficients in linear regression; 5) Iterative solution: By continuously iterating and optimizing the algorithm, the solution is gradually approached. 6) Determine convergence: Determine whether the optimization algorithm has converged. If it has not converged, continue iterating. 7) Output results: After obtaining the optimal solution, output the results, such as regression coefficients, minimized error, maximized profit, etc. In general, the computation process of model-based optimization algorithms involves establishing a model and objective function based on the actual problem, iteratively solving the optimization algorithm to obtain the optimal solution and output the result.

[0080] The parameter optimization platform described in this embodiment is not only applicable to ultra-large-scale neural networks, but also addresses a key issue in training many models in machine learning and artificial intelligence. In practical applications, researchers and engineers frequently need to optimize parameters in various models, including Support Vector Machines (SVM), decision trees, logistic regression, and random forests. In these models, parameter optimization aims to optimize the model's parameters to best fit the given training data while maintaining its generalization ability to new data. The optimization objective can be to minimize training error, maximize prediction accuracy, or other metrics. Parameter optimization is typically achieved using mathematical optimization techniques such as gradient descent and Newton's method.

[0081] Here are some common application scenarios: 1. Machine Learning Machine learning is an important artificial intelligence technology that can be applied to fields such as data mining, image recognition, and natural language processing. In machine learning, model parameters need to be optimized to achieve better prediction and classification results. Large-scale parameter optimization techniques can help accelerate the training and optimization of machine learning models, improving their accuracy and efficiency.

[0082] The specific application process is as follows: 1) Data preprocessing: First, data preprocessing is required, including data cleaning, data normalization, and feature extraction.

[0083] 2) Model building: Select an appropriate model to build, such as deep neural networks, support vector machines, decision trees, etc., and define the loss function and optimizer.

[0084] 3) Large-scale parameter optimization: Select an appropriate optimization algorithm for parameter optimization. Commonly used optimization algorithms include stochastic gradient descent, momentum-based stochastic gradient descent, and adaptive learning rate stochastic gradient descent.

[0085] 4) Model evaluation: The model is evaluated using a validation set or a test set. The main metrics include accuracy, recall, and F1 score.

[0086] 5) Parameter tuning: Improve model performance by adjusting hyperparameters, such as learning rate, batch size, regularization term, etc.

[0087] 6) Deploy the model: Finally, deploy the optimized model to the production environment.

[0088] It is important to note that in practice, large-scale parameter optimization is a time-consuming and computationally resource-intensive process, requiring the use of distributed computing, GPU acceleration, and other technologies to improve efficiency.

[0089] 2. Computer Vision Computer vision is an important field of computer science with applications in image recognition, object tracking, and face recognition. In computer vision, image features need to be extracted and classified to achieve functions such as image recognition and object tracking. Large-scale parameter optimization techniques can help accelerate image feature extraction and classification, improving the accuracy and efficiency of computer vision systems.

[0090] The application process of ultra-large-scale parameter optimization in the field of computer vision is as follows: 1) Data preparation: First, it is necessary to prepare large-scale image and labeled data, which usually includes the pixel values ​​of the images and the corresponding labels, such as labels for tasks like classification, detection, and segmentation.

[0091] 2) Model design: Design the corresponding model structure according to the specific task and data characteristics, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).

[0092] 3) Parameter initialization: Initialize the model parameters, usually using random initialization methods.

[0093] 4) Training the model: The model is trained using large-scale labeled data, and the parameters are usually optimized using optimization algorithms such as stochastic gradient descent (SGD).

[0094] 5) Hyperparameter tuning: For some parameters that need to be set manually, such as learning rate and regularization coefficient, adjustments are required to obtain better results.

[0095] 6) Model evaluation: Use the validation set or test set to evaluate the trained model and calculate metrics such as precision and recall.

[0096] 7) Deployment and application: Deploy the trained model to real-world application scenarios, such as image classification, object detection, and face recognition.

[0097] During application, model fine-tuning or retraining may be necessary to meet practical requirements. Additionally, the model's interpretability and robustness must be considered.

[0098] 3. Natural Language Processing Natural Language Processing (NLP) is a technology that translates human language into computer language, applicable to fields such as text classification, sentiment analysis, machine translation, question-answering systems, and speech recognition. NLP requires training and optimizing language models to achieve better language understanding and generation capabilities. Large-scale parameter optimization techniques can help accelerate the training and optimization of language models, improving the accuracy and efficiency of NLP systems.

[0099] The following is an application process of using ultra-large-scale parameter optimization in the field of natural language processing: 1) Data collection and preprocessing: First, sufficient datasets need to be collected and preprocessed, such as data cleaning, word segmentation, stemming, and feature extraction.

[0100] 2) Model selection: Select a model suitable for the task and train it according to the dataset, such as deep neural networks, logistic regression, support vector machines and Naive Bayes.

[0101] 3) Hyperparameter selection: Hyperparameters are parameters that need to be manually set in the model, such as learning rate, number of iterations, batch size, and regularization coefficient. Optimal hyperparameters are usually found through methods such as cross-validation.

[0102] 4) Model training: Train the model using the training set, and monitor and adjust the training process.

[0103] 5) Model evaluation: Use the test set to evaluate the trained model, such as by metrics like accuracy, recall, and F1 score.

[0104] 6) Model optimization: Further improve model performance by adjusting model parameters and architecture, and repeat steps 3-5 until optimal performance is achieved.

[0105] 7) Deployment: Deploy the trained model to the production environment, for example, by building an API interface for other applications to call.

[0106] In summary, large-scale parameter optimization is one of the key steps in achieving the optimal natural language processing model, and model performance can be continuously optimized through repeated iterations.

[0107] 4. Genomics Genomics is the science that studies biological genes and genomes, and it can be applied to disease diagnosis, drug development, gene editing, and other fields. Genomics requires the analysis and classification of large amounts of genetic data to achieve better bioinformatics analysis and prediction capabilities. Large-scale parameter optimization techniques can help accelerate the analysis and classification of genetic data, improving the accuracy and efficiency of genomics analysis and prediction.

[0108] Large-scale parameter optimization techniques can solve many problems in genomics, such as gene prediction, genome assembly, and gene expression analysis. The following is an example of how large-scale parameter optimization is applied in genomics: 1) Data Preprocessing: First, useful information needs to be extracted from the genomic data. For example, gene prediction requires converting DNA sequences into protein-coding information, genome assembly requires splicing fragmented DNA sequences into continuous sequences, and gene expression analysis requires comparing gene expression levels with genome annotation information. These preprocessing steps can be accomplished using various genomics tools.

[0109] 2) Feature Selection: Large-scale parameter optimization requires selecting features for training the model. In genomics, features can include gene sequences, expression levels, tissue specificity, etc. Feature selection can utilize statistical methods and machine learning algorithms.

[0110] 3) Model Training: Based on the selected features, train the model using a large-scale parameter optimization algorithm. Typically, problems in genomics are multi-class classification problems, therefore algorithms suitable for multi-class classification, such as logistic regression, decision trees, and support vector machines, are required.

[0111] 4) Model Evaluation: Evaluate the performance of the trained model using a test dataset. In genomics, cross-validation is commonly used to evaluate model performance.

[0112] 5) Model Applications: Trained models can be applied to various genomics problems. For example, gene prediction models can be used to predict new gene sequences, and gene expression analysis models can be used to interpret differential expression in tissues or diseases.

[0113] In summary, large-scale parameter optimization has wide applications in genomics, which can accelerate the progress of genomics research and provide useful information and insights.

[0114] It's worth noting that parameter optimization differs across models. For example, deep neural networks typically handle a very large number of parameters and involve many non-convex problems. These problems usually require specialized algorithms, such as stochastic gradient descent and adaptive gradient descent. In other models, parameter optimization may be easier to handle due to smaller parameter sizes or simpler optimization problems.

[0115] In summary, parameter optimization is a crucial problem in machine learning and artificial intelligence, applicable not only to large-scale neural networks but also to other models. Optimizing parameters helps models better fit data and improve prediction accuracy, thereby enhancing model performance and application value.

[0116] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements can be made without departing from the principle of the present invention, and these improvements should also be considered within the scope of protection of the present invention.

Claims

1. A trillion parameter optimization platform, characterized in that: include: The parameter space component is responsible for defining the range and values ​​of parameters including learning rate, batch size, and weight decay, as well as the dependencies between different parameters; The parallel computing framework utilizes distributed computing and multi-process concurrency technology to decompose the parameter space into several parameter subspaces, and the parallel computing distributes the parameter subspaces to different computing nodes for computation; the parallel computing framework includes a swarm optimization algorithm unit and a deep learning technology component; The swarm optimization algorithm unit initializes the parameters of the parameter subspace, the deep learning technology component limits the range of the parameter search space of the parameter subspace, and then uses the swarm optimization algorithm to search and solve for the optimal parameter combination in each subspace; the optimal parameter combination output by all parameter subspaces is integrated, and the overall performance evaluation and optimization result comprehensive analysis of the model are completed. The swarm optimization algorithm unit uses a series of advanced swarm optimization algorithms to quickly search for optimal parameters. The swarm optimization algorithm unit includes genetic algorithms, particle swarm optimization algorithms, and distributed differential evolution algorithms. The selection of specific algorithms takes into account the following principles: characteristics of the problem, complexity of the algorithm, stability of the algorithm, existing experience, and interpretability of the algorithm. The deep learning technology component utilizes deep learning techniques to model and predict the parameter space, thereby accelerating the parameter search process. The deep learning technology component uses neural network models, including convolutional neural networks and recurrent neural networks, to adapt to different application scenarios. The model evaluation and results analysis component is responsible for evaluating the model performance corresponding to different parameter combinations, and summarizing and analyzing the results; it also provides charts, data reports or other auxiliary tools. The parameter space component, parallel computing framework, swarm optimization algorithm unit, deep learning technology component, and model evaluation and result analysis component are all connected to the central control mechanism. The parameter space is decomposed into multiple subspaces according to certain rules, the specific decomposition rules and methods are as follows: a. Determine system parameters: First, determine the system parameters, that is, determine the parameters that have a decisive influence on the system behavior; b. Define the parameter range: For each parameter, determine its range, that is, the range of possible values ​​the parameter can take; c. Divide the parameter space into multiple subspaces, each containing a subset of parameter points; d. Define subspaces: Define each subspace using natural language and mathematical formulas; e. Analyze subspaces: Analyze each subspace to obtain the behavioral characteristics of the system under that subspace; f. Comprehensive Analysis: By combining the analysis results of all subspaces, the overall behavioral characteristics of the system are obtained; The parallel computing framework considers load balancing, performance optimization, program design, error handling, and fault recovery, and the specific process is as follows: 1) Decompose the problem: Break down a large-scale task into multiple smaller tasks for parallel processing; 2) Task allocation: Assign small tasks to different processors or computing units so that they can be processed simultaneously; 3) Executing computations: Each processor or computing unit independently executes its assigned tasks in order to process multiple tasks in parallel; 4) Merging calculation results: Merging the calculation results of each processor or computing unit to obtain the final calculation result; 5): Task completion: The entire computing task is completed when all processors or computing units have completed their tasks.

2. The platform for optimizing parameters at the billion-level according to claim 1, characterized in that: During parameter optimization, the parallel computing framework divides the parameter space into multiple subspaces and then assigns different subspaces to different computing nodes for computation. The specific implementation method is as follows: a. Divide the parameter space into multiple subspaces; the subspaces after division have the same size and are neither duplicated nor omitted. b. Allocate computing nodes: Different subspaces are assigned to different computing nodes for computation. A distributed computing framework is used to realize communication and collaboration between computing nodes. When allocating computing nodes, reasonable allocation is made based on the computing capacity and load of the computing nodes. c. Parallel computing: Parallel computing is performed on the allocated subspace on each computing node, using multi-process concurrency technology to achieve parallel computing within the computing node; during parallel computing, attention should be paid to data synchronization and communication; d. Summarize the calculation results: Summarize the results calculated by each computing node to obtain the optimal parameter combination. Use the summarization function provided by the distributed computing framework to summarize the calculation results.

3. The platform for optimizing parameters at the billion-level according to claim 2, characterized in that: The specific computation process of assigning the parameter subspace to different computing nodes for computation in parallel computing is as follows: a: First, determine the method of dividing the parameter space and parameter subspace. Usually, the parameter space is divided into several subspaces by uniform or random partitioning. b: Next, determine the number and allocation method of computing nodes. Based on the system's performance and task requirements, decide how many computing nodes to use and how to allocate these nodes to different computers or processors for computation. c: Determine the parameter subspace to be computed by each computing node. After determining the parameter subspace to be processed by each node based on the number of computing nodes and the allocation method, allocate these subspaces to different computing nodes. d: Perform calculations. Each computing node performs calculations according to its assigned parameter subspace and transmits the calculation results to other nodes. Throughout the calculation process, different nodes communicate and synchronize with each other. e: Merge the calculation results. After all the calculation nodes have completed their calculations, merge the results obtained from each node to obtain the final result of the entire parameter space.

4. The platform for optimizing parameters at the billion-level according to claim 3, characterized in that: The specific principles for dividing the parameter space into multiple subspaces are determined based on the specific application requirements, and typically include the following aspects: a. Characteristics of the objective function: If the objective function exhibits different characteristics in different parameter regions, the parameter space is divided according to these characteristics; b. Constraints of the problem: In practical applications, there are often various constraints. The parameter space is divided according to various constraints, and all parameter combinations that satisfy the constraints are divided into the same subspace.

5. The platform for optimizing parameters at the billion-level according to claim 1, characterized in that: The deep learning technology component utilizes deep learning technology to model and predict the parameter space, learn the mutual influence between parameters, thereby better understanding the characteristics and laws of the parameter space and further optimizing the parameter search process; The process of deep learning technology modeling the parameter space is as follows: each parameter in the parameter space is taken as input, and the input is mapped to the output through a neural network. The output is the corresponding prediction result. The training process of a neural network involves optimizing it with a large amount of data to minimize the error between the predicted and actual results, thereby obtaining the optimal parameter values. Predicting an unexplored parameter space refers to predicting unknown parameter values ​​using existing data and models. Specifically, it involves inputting unknown parameter values ​​and performing calculations through a neural network to obtain the corresponding prediction results. The criteria for prediction are determined based on the specific application scenario. Error indicators are usually used. If the prediction error of the model is small, the model is considered to have strong prediction ability and thus better predict the unknown parameter values. The deep learning technology component is implemented as follows: S1): Data preparation: A set of labeled training data needs to be prepared, which includes model performance data corresponding to different parameter combinations. The training data should be representative and diverse to cover different regions and characteristics of the parameter space. At the same time, the training data needs to be preprocessed and normalized. S2): Model Design: A deep learning model needs to be designed to learn the characteristics and rules of the parameter space. The deep learning model adopts some common neural network structures to adapt to different application scenarios. The deep learning model includes an input layer, a hidden layer and an output layer, where the input layer is the parameter space and the output layer is the model performance data. S3): Training the model: The deep learning model needs to be trained using training data. Some optimization algorithms are used in the training process to minimize the prediction error. During the training process, attention should be paid to the allocation of training data and cross-validation. S4): Parameter prediction: Model and predict the parameter space using a trained deep learning model, predict the performance of a model with known parameter combinations using a deep learning model, or predict an unexplored parameter space using a deep learning model. The prediction results are used to guide the parameter search process.

6. The platform for optimizing parameters at the billion-level according to claim 1, characterized in that: The specific process of data preprocessing and normalization in S1) of the implementation method of the deep learning technology component is as follows: 11) Data cleaning: Remove duplicate, missing, and outlier data; 12) Feature selection: Select features that have an impact on the prediction results; 13) Feature scaling: Scaling the numerical range of each feature to the same range to avoid adverse effects on model training due to some feature values ​​being too large or too small; 14) Data normalization: scaling data uniformly to a range between 0 and 1; 15) Dataset partitioning: Divide the dataset into training set, validation set, and test set; 16) Category label conversion: Convert category labels to numeric types; 17) Feature engineering: Further processing of features; 18) Data dimensionality reduction: Use the PCA algorithm to reduce high-dimensional data to low-dimensional data.

7. The platform for optimizing parameters at the billion-level according to claim 1, characterized in that: The modeling process of the deep learning model in S2) of the implementation method of the deep learning technology component is as follows: 21) Data preprocessing: Preprocessing the raw data, including data cleaning, data normalization, and data segmentation; 22) Model selection: Select an appropriate model based on the characteristics of the problem and the data structure; 23) Model building: Constructing the model structure, including the input layer, hidden layers, output layer, and activation function; 24) Parameter initialization: Initialize the model parameters to make them easier to adjust during training; 25) Loss function selection: Choose an appropriate loss function; 26) Training the model: The model is trained using the training set, and the parameters are adjusted using optimization algorithms; 27) Model evaluation: Evaluate the trained model, including accuracy, recall, F1 score, and AUC. 28) Model tuning: Based on the evaluation results, the model is tuned, including adjusting parameters, increasing the number of network layers, and using different optimization algorithms; 29) Model application: Applying the trained model to real-world problems; The training process of the deep learning model is as follows: 1) Data preparation: Preprocess the data, divide it into training and test sets, and ensure the quality and applicability of the data; 2) Model building: Based on task requirements and data characteristics, select appropriate model structure and parameters to build a deep learning model; 3) Loss function definition: Based on task requirements, select an appropriate loss function to measure the difference between the model's predicted results and the actual results; 4) Model training: The model is trained using the training set, and the model parameters are continuously updated through optimization algorithms to minimize the loss function; 5) Model Evaluation: Use the test set to evaluate the model, calculate the model's loss value and accuracy on the test set, and other metrics to determine the model's performance and generalization ability; 6) Parameter tuning: Based on the model evaluation results, adjust the model structure and parameters, optimize model performance, and perform cross-validation on the training and test sets; Model saving and use: Save the optimal model parameters for subsequent prediction, classification, clustering or other tasks.

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