The Problem
The Fitzpatrick skin tone bias audit (issue #531 ) is blocked. All datasets and code are ready, but no GPU is available to run Florence-2 inference:
Local machine: no CUDA GPU
Kaggle API: cannot push kernels (403 Forbidden on SaveKernel)
Kaggle web UI: accelerators deactivated on this account
What We Have
Asset
Location
Florence-2-base model
Kaggle Dataset: mosesos/florence-2-base
SCIN image shards 0-4
Kaggle Dataset: mosesos/trij-scin-data
MSKCC images + metadata
Kaggle Dataset: mosesos/trij-mskcc-data
Offline inference notebook
docs/bias-audit/scripts/run_on_kaggle.ipynb
Audit pipeline script
docs/bias-audit/scripts/run_comprehensive_audit.py
Resolution Paths (priority order)
Option A: Google Colab Pay As You Go (~$10)
Subscribe to Colab Pro/Pay As You Go (~$10/month)
Upload run_on_kaggle.ipynb (or adapt for Colab)
Runtime → Change runtime type → T4 GPU
Run inference on the 599 MSKCC images
Expected runtime: ~2-4 hours
Download inference_results.csv → run run_comprehensive_audit.py --csv /path/to/inference_results.csv
Pro: Cheapest, fastest path
Con: Need to re-download datasets to Colab
Option B: Borrow a GPU Machine
Any CUDA-capable machine
Check out repo, run run_on_kaggle.ipynb locally
Pro: Full control
Con: Need to find hardware
Option C: Kaggle API Fix
Try switching API token to owner-level token or different Kaggle account with GPU access
Re-attempt kernel push
Pro: No cost
Con: May not work — 403 may be account-level, not token-level
What Happens Next
Get GPU access → run Florence-2 on all 599 MSKCC images
Run run_comprehensive_audit.py on results
Generate performance_gap_report.md
Update issue Bias Audit: Run Real Inference on GPU & Package Results #531 with real (not simulated) bias metrics
Use report in Horizon 1000 outreach as proof of equity readiness
Blockers
References
The Problem
The Fitzpatrick skin tone bias audit (issue #531) is blocked. All datasets and code are ready, but no GPU is available to run Florence-2 inference:
What We Have
mosesos/florence-2-basemosesos/trij-scin-datamosesos/trij-mskcc-datadocs/bias-audit/scripts/run_on_kaggle.ipynbdocs/bias-audit/scripts/run_comprehensive_audit.pyResolution Paths (priority order)
Option A: Google Colab Pay As You Go (~$10)
run_on_kaggle.ipynb(or adapt for Colab)inference_results.csv→ runrun_comprehensive_audit.py --csv /path/to/inference_results.csvOption B: Borrow a GPU Machine
run_on_kaggle.ipynblocallyOption C: Kaggle API Fix
What Happens Next
run_comprehensive_audit.pyon resultsperformance_gap_report.mdBlockers
References