Checkpoint mountains
Every training run keeps 'just one more' checkpoint. Times hundreds.
macOS
Datasets, conda environments, model checkpoints, and notebooks with embedded outputs. Data work fills disks with files that all look important — until you measure them.
Every training run keeps 'just one more' checkpoint. Times hundreds.
Conda and venv environments duplicate gigabytes per project.
A 200 MB .ipynb is 199 MB of base64 plots.
Oldest-plus-largest is the delete shortlist. Statistics views help here.
Keep the best per experiment; the collector holds the rest for a final review.
Re-run beats storage. Clear outputs before archiving notebooks.
Allocated size can surprise — verify before removing environments.
NullDisk first — native map, staged collector, zero telemetry. Then, depending on taste:
Age-plus-size statistics (JDiskReport's specialty) or a treemap sorted by size. Numbers beat vibes for data hoards.
Derived artifacts (checkpoints, caches, outputs) yes; raw data almost never. When in doubt, archive to external storage first.