chore: update embedding run IDs and centralize them per model#17
Conversation
Repoint Virchow2 and ProvGigaPath embedding configs at the new embedding runs
and remove the storage disparity between them.
Previously the Virchow2 run ID lived in dataset.yaml while the ProvGigaPath ID
was a literal duplicated across three experiment configs. Replace both with a
single per-model map in dataset.yaml:
mlflow_artifacts.embedding_run_ids:
virchow2: 262d680f52374614baaf19f1e4d05940
provgigapath: 5099ce57e886411dababfc1234a88b23
All embedding configs now interpolate from this map, so each run ID has one
source of truth. No consumer code changes: nothing in Python reads
mlflow_artifacts.embedding_run_id; resolution is pure Hydra interpolation.
Also expand the README with repository structure, setup, and usage sections.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Code Review
This pull request updates the README.md with comprehensive documentation regarding the repository structure, setup instructions using uv, and usage guidelines. Additionally, it refactors the configuration files to support multiple embedding models by replacing the single embedding_run_id with a dictionary of embedding_run_ids (specifically for virchow2 and provgigapath) and updates the corresponding experiment and task configurations to reference these new keys. There are no review comments, so I have no feedback to provide.
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3 participants
Summary
Repoint the embedding configs at the newly generated embedding runs from the master branch and remove the storage disparity between the two models.
Notes