MetaFaaS Serverless Architecture for Meta-Learning Training
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Solution Overview
Problem
Meta-learning architectures face challenges with high training times and increased costs due to their hierarchical nature and compute-intensive gradient-based algorithms, especially when using virtual machines that remain idle during irregular data arrival, leading to inefficient scalability and increased costs in serverless setups.
Innovation Solution
A method and system for meta-learning using distributed training on a serverless architecture, where a base model is initialized and spawned across multiple serverless instances, with tasks distributed uniformly, gradients computed and aggregated in cloud storage, and the model updated iteratively until convergence, optimizing compute efficiency and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual machines are continuously provisioned to handle irregular data arrival intervals, then scalability is improved, but resource utilization deteriorates as VMs remain idle during periods without incoming data
Solution Approach 1:
The system dynamically provisions and deprovisions virtual machine instances based on real-time data arrival patterns and processing queue status. When data arrives at irregular intervals, the system automatically scales the number of active VM instances up or down, ensuring that computational resources are actively utilized only when data is available for processing, thereby resolving the contradiction between scalability and resource utilization efficiency
Solution Approach 2:
The system implements periodic monitoring of data arrival patterns and processing loads, with scheduled adjustments to VM instance counts. This periodic action allows the system to adapt to irregular data intervals by systematically scaling resources in response to observed patterns, maintaining both scalability and efficient resource utilization without continuous idle provisioning
2Reliability
If gradient-based meta-learning algorithms are used to achieve hierarchical learning, then model generalization capability is improved, but training time deteriorates due to compute-intensive operations
Solution Approach 1:
The meta-learning training process is segmented into multiple independent worker instances that operate in parallel. Each worker instance handles a portion of the hierarchical learning computations, dividing the compute-intensive gradient-based operations across multiple processing units. This segmentation maintains the model's generalization capability while reducing overall training time through parallel execution of segmented training tasks
Solution Approach 2:
The system transitions from single-threaded sequential training to multi-dimensional parallel processing by spawning multiple worker instances across different computational threads and potentially different virtual machine instances. This dimensional expansion of the training architecture allows gradient-based meta-learning operations to execute simultaneously in multiple dimensions, significantly reducing training time while preserving the hierarchical learning effectiveness
3Productivity
If multiple worker instances are spawned in serverless architecture to accelerate distributed training, then training speed is improved, but system complexity deteriorates due to coordination and gradient aggregation overhead
Solution Approach 1:
A central coordinator service acts as an intermediary between multiple serverless worker instances and the training orchestration system. This intermediary manages the complexity of coordinating distributed workers, handling gradient aggregation from multiple instances, and managing model state synchronization. By centralizing these coordination functions, the system achieves high training speed through parallel worker execution while the intermediary absorbs and manages the system complexity, preventing it from propagating through the entire distributed architecture
Data Source
AI summary
Hardly any work in literature attempts employing Function-as-a-Service (FaaS) or serverless architecture to accelerate the training or re-training process of meta-learning architectures. Embodiments of the present disclosure provide a method and system for meta learning using distributed training on serverless architecture. The system, interchangeably referred to as MetaFaaS, is a meta-learning based scalable architecture using serverless distributed setup. Hierarchical nature of gradient based architectures is leveraged to facilitate distributed training on the serverless architecture. Further, a compute-efficient architecture, efficient Adaptive Learning of hyperparameters for Fast Adaptation (eALFA) for meta-learning is provided. The serverless architecture based training of models during meta learning enables unlimited scalability and reduction of training time by using optimal number of serverless instances. An analytical model for gradient based meta learning architectures that predicts training time required for the number of FaaS instances is provided which further enables estimating the cost incurred during training models in meta-learning.