Model Orchestration for Secure Multi-Cluster ML Training
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Solution Overview
Problem
Existing machine learning systems lack efficient methods for automatically orchestrating large-scale model training across multiple machines, requiring manual model modifications, result monitoring, and active cloud computing management, while also failing to preserve user privacy and security, especially with proprietary data.
Innovation Solution
A system and method for automated model orchestration that provisions, orchestrates, and manages computing resources to train user-specified models, enabling seamless scaling from small to large-scale training with reduced user overhead, while preserving privacy and security by keeping data within the user's cloud system and using a modular hierarchical architecture to control heterogeneous computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual model modifications and active cloud computing management are used, then model training can be performed, but user overhead and operational complexity increase
Solution Approach 1:
The system enables self-service through automated orchestration that provisions, schedules, monitors, and manages computing resources without requiring manual user intervention. The orchestration system automatically handles model training workflows, resource allocation, and result aggregation, allowing users to initiate training with minimal input and receive results without active management.
2Reliability
If data is kept within user's cloud system for privacy, then security is improved, but access to computing resources is restricted
Solution Approach 1:
The orchestration system acts as an intermediary between the user's secure cloud environment and external computing resources. It enables secure access by mediating data transfers, managing authentication, and coordinating training workflows without requiring users to directly expose their proprietary data, thus maintaining security while enabling resource access.
3Productivity
If large-scale model training is performed across multiple machines, then training efficiency improves, but system complexity increases
Solution Approach 1:
The system segments the large-scale training task into manageable components distributed across multiple machines. The orchestration system divides model training into separate workflows that can be executed independently on different computing resources, then aggregates the results. This segmentation enables parallel processing and improves training efficiency while keeping individual task complexity manageable.
Solution Approach 2:
The orchestration system provides multi-functionality by handling provisioning, scheduling, monitoring, and result aggregation across diverse computing resources. It creates a universal interface that manages heterogeneous machines and cloud environments through standardized workflows, reducing the complexity users would otherwise face when coordinating multiple machines for training.
4Adaptability or versatility
If heterogeneous computing resources are utilized, then resource flexibility improves, but management complexity increases
Solution Approach 1:
The orchestration system implements universality by creating a unified management layer that handles heterogeneous computing resources through standardized interfaces. It provisions and manages diverse machines, cloud environments, and computing architectures using consistent workflows and protocols, enabling resource flexibility while abstracting away the underlying management complexity from users.
Data Source
AI summary
A system for large-scale machine learning experiment execution, including: a platform configured to determine an experiment set from a run specification and schedule a run to one or more clusters; and a set of agents configured to receive the experiment set from the platform and facilitate individual experiment execution through a cluster orchestrator.