Large-scale generative models have entered research and human-technology interaction, including software design, education, and creativity
This is typically indicated by the number of floating-point operations needed to train a model
They also present techniques to maximise ML Productivity Goodput, as well as an API that you can incorporate into your projects to measure and track Goodput
The three goodput metrics that make up ML Productivity Goodput are Scheduled Goodput, Runtime Goodput, and Programme Goodput
The fraction of time that all the resources needed to complete the training job are available is measured by scheduling goodput
The Runtime Goodput metric quantifies the amount of time required to advance when all training resources are available as a percentage of total time
The percentage of hardware performance that can be extracted by the training job is measured by Programme Goodput