1. The proposed method optimizes the training time of GRU networks by efficiently distributing computation across multiple nodes.

2. Version control integration ensures seamless tracking of model updates, enabling better collaboration and error handling in distributed environments.

3. The method achieves superior scalability and performance while maintaining model accuracy comparable to centralized training approaches.

Description:Title:
Method for Distributed Deep Learning Model Training Using GRU Networks with Version Control Integration

Field of the Invention

[0001] The present invention is related to the distributed systems in computer science and machine learning field.

Background

[0002] Distributed deep learning has gained significant attention due to the growing size and complexity of models, which exceed the capacity of single-node systems.
[0003] Gated Recurrent Unit (GRU) networks are highly efficient for sequence data tasks, offering lower computational complexity compared to LSTMs, making them suitable for distributed training setups.
[0004] Distributed training faces challenges such as synchronization overhead, data partitioning, gradient updates, and maintaining model consistency across nodes.
[0005] Integrating version control systems into distributed model training helps manage updates, rollback changes, and ensure reproducibility, enhancing collaboration and fault tolerance.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0007] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0008] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0009] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[0010] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

Objects of the Invention

[0011] Develop a scalable distributed training framework for GRU networks that minimizes training time while maintaining model accuracy.
[0012]. Integrate a robust version control system to manage training iterations, hyperparameter adjustments, and rollback functionalities.

Drawings

Figure 1

Brief Description of the Drawing

[0013] The figure 1 represents working model in the present invention with its prototype.

Detailed Description:

[0014] In figure 1, showing the input parameter; which is to be processed by the system 100.
[0015] Preprocess and partition the dataset into equal-sized chunks distributed across the nodes. Ensure balanced data allocation to minimize training skew.
[0016] Set up multiple nodes with synchronized configurations, including GPU allocation, GRU model initialization, and communication protocols.

[0017] Implement model parallelism to distribute different components of the GRU network across nodes for efficient training.
[0018] Use techniques like all-reduce to aggregate gradients from all nodes to maintain consistent model updates.
[0019] Integrate a version control system to track changes in model weights, configurations, and hyperparameters during training iterations.
[0020] Implement mechanisms to handle node failures by utilizing version control logs for recovery and retry strategies.
[0021] Optimize hyperparameters such as learning rate and batch size using a distributed search mechanism.
[0022] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[0023] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[0024] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or
steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
, Claims:We Claim:

1. The proposed method optimizes the training time of GRU networks by efficiently distributing computation across multiple nodes.
2. Version control integration ensures seamless tracking of model updates, enabling better collaboration and error handling in distributed environments.
3. The method achieves superior scalability and performance while maintaining model accuracy comparable to centralized training approaches.