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Tactic: Design for Memory Constraints

Tactic sort: Awesome Tactic

Type: Architectural Tactic

Category: green-ml-enabled-systems

Tags: machine-learning model-training

Title

Design for Memory Constraints

Description

Model training requires memory, and sometimes memory leaks and OOM (out of memory) errors may occur during that process. If that happens, the knowledge gained during the prior training process is lost. By considering memory availability constraints and addressing possible OOM exceptions, the system can be designed to operate within the available memory limits. It reduces the likelihood of errors and prevents unnecessary energy consumption (Shanbhag at al 2022).

Participant

Data Scientist

Related software artifact

Memory

Context

Machine Learning

Software feature

< unknown >

Tactic intent

Improve energy efficiency by considering memory constraints during training to prevent knowledge loss due to a premature termination, which would in turn require to restart the process from the beginning, therefore increasing energy consumption

Target quality attribute

Recoverability

Other related quality attributes

Energy Efficiency

Measured impact