[2771326597] Upstream Reporter: Kim
Upstream issue status: Open
Upstream description:

Feature Overview:

• When users generate a lot of samples, they will have the option to run subset selection method to get a minimal set of samples representative of original dataset.
• Subset Selection algorithm as developed by research computes embeddings of the samples and then tries to iteratively find a minimal subset which maximizes the coverage of the dataset.

Goals (mandatory - Complete while in New status)

• Provide high-level goal statement, providing user context and expected user outcome(s) for this Feature
• End user with a large dataset, which might require higher compute, training time, other resources can reduce the size of the input dataset

Requirement:

• Given an input dataset, subset selection outputs a smaller set representative of the original dataset.

Done - Acceptance Criteria:

• Output of subset selection is representative of original dataset
• Smoke test to verify few use cases with subset selection (model is being trained efficiently)