Parallel Model Training via Serverless Clusters
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Solution Overview
Problem
Conventional methods for training computational models, such as machine learning and artificial intelligence models, are inefficient as they require serial processing, preventing the distribution of models across machines or networks due to technological constraints, leading to time-consuming and inaccurate results.
Innovation Solution
A parallel model training process is implemented using serverless computing services and cloud-computing resources, where model specifications are stored and processed across multiple serverless computing clusters, enabling simultaneous training and storage of trained models in an object storage service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computational models are trained individually in a serial process, then each model can be trained with dedicated resources, but the training process becomes time-consuming and inefficient
Solution Approach 1:
The patent segments the model training process into independent parallel tasks by dividing model specifications into separate training jobs that can be executed simultaneously across multiple computing clusters. Each cluster handles independent model training without interfering with others, enabling parallel processing while maintaining training quality.
Solution Approach 2:
The patent transitions from single-machine serial training to multi-machine parallel training by distributing model specifications across multiple serverless computing clusters. This dimensional expansion from one computing resource to many enables simultaneous training of multiple models, dramatically improving productivity while reducing total training time.
2Productivity
If models are distributed across multiple machines or networks, then training can be parallelized, but conventional technology lacks the capability to ensure accurate results and has technological constraints
Solution Approach 1:
The patent employs a universal object storage service that serves multiple functions: storing model specifications, storing training data, storing trained models, and coordinating distributed training across different computing clusters. This universal storage infrastructure enables reliable parallel training by providing a common data exchange platform that all clusters access consistently.
Solution Approach 2:
The patent introduces serverless computing services as intermediaries between the distributed computing clusters and the object storage service. These intermediary services manage the distribution of model specifications, coordinate training tasks, and ensure that results are accurately aggregated, overcoming the reliability concerns of distributed training.
3Loss of time
If multiple models are trained simultaneously across distributed clusters, then training efficiency improves, but system complexity increases due to coordination requirements
Solution Approach 1:
The patent implements self-service mechanisms where the serverless computing automatically manages the complexity of coordinating distributed training. The system autonomously distributes model specifications to appropriate clusters, monitors training progress, and aggregates results without requiring manual intervention, thereby reducing the perceived complexity for users while enabling parallel training.
Solution Approach 2:
The patent establishes feedback loops where trained models are stored back in the object storage service, making results immediately available for subsequent training tasks or deployment. This feedback mechanism automates the coordination complexity by creating a self-regulating system where completed training informs future training decisions, reducing manual coordination overhead.
Data Source
AI summary
Techniques and apparatus for an interactive element presentation process are described. In one embodiment, for example, an apparatus may include logic operative to store a plurality of model specifications for computational models, monitor the object storage service for at least one model event using a first serverless computing service, provide the plurality of model specifications associated with the at least one model event to one of a plurality of serverless computing clusters, generate model data for each of the plurality of model specifications, store the model data for each of the plurality of computational models in the object storage service, monitor the object storage service for at least one data event associated with the model data using a second serverless computing service, cause the plurality of instances to generate a plurality of trained model specifications based on training of the plurality of computational models. Other embodiments are described.