A dataset-baked pod image, a safer launch script, and a cache removed entirely
Follow-on to the torch-native CWT work. This session baked the dataset into the GPU pod image, hardened the pod-launch script against a real orphaning bug, then went further than expected on caching — removing the CWT and dense-edge disk caches entirely rather than just tuning them. It ended by explicit request partway through a live investigation, so the most interesting question it raised is still open.
Dataset baked into the pod image
Rather than a network volume, the CHB-MIT data for the one subject this project currently trains on (~1.6GB, 42 recordings) now gets downloaded straight into the Docker image at build time, driven by the same public mirror's own file listing rather than a hardcoded filename list so it can't silently drift from what the pipeline expects. Build time grew from ~25 to ~43 minutes as a result; validated in-build that all 42 files actually landed.
A real orphaned-pod bug, found and fixed live
The new launch script is built to always tear a pod down, success or failure, via two independent layers — a local exit trap and a platform-side wall-clock cap. A live bad-machine draw (SSH never became reachable) exposed a real gap: the pod-creation call failing non-zero aborted the script before the pod's ID was ever parsed out of the response, so the cleanup trap had nothing to act on and the pod sat running, unbilled-for, until caught and stopped by hand. Fixed by parsing the pod ID out of either a success or a timeout-error response before deciding whether to bail, so a bad draw gets stopped going forward instead of orphaned.
Reopening the caching question — and removing it entirely
Having hit versions of "why is this still slow" across many prior sessions, the question got asked directly this time: if the CWT itself is now sub-millisecond, why cache it on disk at all? A first, narrower attempt — disabling only the CWT cache for the torch backend — measured no real improvement on a real pod (5m34s cached vs. 5m55s uncached, within noise). A more aggressive change, discovered mid-session as a concurrent local commit and pushed to make it usable, removed both the CWT and dense-edge disk caches outright rather than toggling them. The reasoning matched this session's own measurements: with raw CWT compute now measured at 0.16–0.23ms per call on real CUDA hardware, the caches' own hashing and disk I/O had become the dominant cost, not the computation they were protecting.
| config | epoch_time sum, 14 epochs |
|---|---|
| both caches enabled (baseline) | 145.5s |
| CWT cache only removed | 155.5s |
| both caches removed | 95.6s |
Removing both caches measured ~34% less training-loop time than baseline, with live GPU utilization at 74% — versus near-zero in every prior observation across this and past sessions. Correctness held throughout: identical hit rate and metrics across every config tested, confirming caching had always been a pure performance layer, never load-bearing for the actual output.
The open question this session didn't answer
A live profiling attempt to answer this directly was started and not finished — a stopped pod was restarted to investigate, which surfaced its own gotcha (a git-cloned repo doesn't survive a pod stop/start cycle, only image-baked content does), and the session was called off entirely before a phase-by-phase timing breakdown could be collected. No conclusion was reached about whether tonight's cache removal actually matters at real scale.
Open items
Getting a real CWT-vs-dense-edge-vs-model-step timing breakdown at smoke scale is the genuinely unfinished work here, before drawing further conclusions about the cache removal or comparing against the previous day's real-scale numbers. All pods were terminated and confirmed empty at session's end (~$0.62 total spend across the night's experiments). The launch script now defaults to stopping rather than deleting a pod on teardown, which means a stopped pod's disk billing can quietly continue unless checked periodically.