ML Workload Orchestration With Adaptive Hyperparameters
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Solution Overview
Problem
Existing machine learning workload orchestration systems struggle with resource fragmentation and underutilization in heterogeneous compute environments, such as on-premises and edge devices, due to differing compute resources and asynchronous learning parameter exchanges.
Innovation Solution
A system that dynamically adjusts hyperparameters and schedules workloads based on the compute resources of each compute cluster, using a discovery subsystem to identify resources, a manifest subsystem to specify demands, a placement subsystem to allocate workloads, and an adaptive modeling subsystem to define hyperparameters accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If dedicated homogeneous compute nodes are used for machine learning workloads, then workload orchestration is simplified with constant hyperparameters, but compute resource utilization decreases in heterogeneous environments
Solution Approach 1:
The system dynamically adjusts hyperparameters based on the compute resources of each compute cluster. Instead of using constant hyperparameters across all nodes, the orchestrator adapts hyperparameters to match the heterogeneous capabilities of different clusters, enabling efficient workload execution while maintaining simplified orchestration through automated adaptation.
Solution Approach 2:
The patent changes the hyperparameters of machine learning workloads according to the specific compute resources of each target cluster. This parameter adaptation allows the same workload to be efficiently executed across heterogeneous clusters with different compute capabilities, resolving the contradiction between simplified orchestration and resource utilization.
2Device complexity
If constant hyperparameters are used across all distributed workloads, then workload allocation is simplified, but training time increases due to asynchronous learning parameter exchanges in heterogeneous clusters
Solution Approach 1:
The system implements dynamic hyperparameter adjustment that adapts to each compute cluster's capabilities. This dynamic approach synchronizes learning parameter exchanges by matching workload characteristics to cluster performance, reducing the asynchronous delays that occur when constant hyperparameters are used across heterogeneous clusters with different speeds.
Solution Approach 2:
By changing hyperparameters based on compute cluster characteristics, the system optimizes training efficiency across heterogeneous environments. This parameter adaptation ensures that workloads are appropriately scaled to each cluster's capabilities, minimizing idle time and synchronizing learning parameter exchanges without increasing allocation complexity.
3Productivity
If homogeneous compute clusters are assumed, then machine learning applications can be distributed efficiently, but adaptability to heterogeneous on-premises and edge devices is reduced
Solution Approach 1:
The workload orchestrator is designed to universally manage machine learning workloads across both homogeneous and heterogeneous compute environments. It performs multiple functions including resource discovery, hyperparameter adaptation, and workload allocation, enabling the same system to efficiently distribute workloads across diverse clusters from cloud data centers to edge devices with varying compute capabilities.
Solution Approach 2:
The system adapts hyperparameters based on the specific compute resources of each target cluster, whether homogeneous or heterogeneous. This parameter change capability enables universal workload distribution across diverse environments, maintaining efficiency while increasing adaptability to different hardware configurations including on-premises and edge devices.
Data Source
AI summary
Systems and methods are described herein to orchestrate the execution of an application, such as a machine learning or artificial intelligence application, using distributed compute clusters with heterogeneous compute resources. A discovery subsystem may identify the different compute resources of each compute cluster. The application is divided into a plurality of workloads with each workload associated with resource demands corresponding to the compute resources of one of the compute clusters. Adaptive modeling allows for hyperparameters to be defined for each workload based on the compute resources associated with the compute cluster to which each respective workload is assigned and the associated dataset.