| # Team handoff |
|
|
| ## Completed |
|
|
| - Teacher-only trace census: TMax 267 original attempts, DeepSWE 2,000. |
| - Exception-separated, fixed-duration admission replay with shared TP8 service accounting. |
| - Peak and reservation-hours comparisons, then resource-specific search subject to FIFO completion deadline. |
| - Independent final schedule validation and plots showing complete schedules by color. |
| - Portable CPU-only reproduction from compact HF data; raw archives retained for deeper extraction. |
|
|
| The latest canonical result is `reference-results/RESULT.json` in the pinned HF release, mirrored by `tests/reference_result.json`. The 100-attempt DeepSWE pilot and earlier shortest-time/lower-peak schedules are historical, not the latest objective-specific result. |
|
|
| ## First steps for a teammate |
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|
| 1. Clone the GitHub repository and obtain access to the HF dataset. |
| 2. Follow README quick start and run the published-metric check. |
| 3. Read METHODS, especially reservation-vs-utilization, memory offload and fixed-latency assumptions. |
| 4. Choose an experiment, write output to a new `runs/` directory, and preserve input HF commit, git commit, seed and parameters. |
| 5. Compare complete schedules (`joint_schedules.csv`), not an unattainable combination of independently selected minima. |
|
|
| ## Next experiments worth running |
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|
| - Add strong reactive baselines (shortest job, aging, fair sharing, image affinity) and noisy duration predictions. Do not compare a learned policy only with weak FIFO. |
| - Model nonzero RAM offload/restore and image reload time, bandwidth, cache capacity and destination storage. Report how savings degrade. |
| - Measure real CPU utilization/RSS/GPU occupancy; current 89.4% DeepSWE CPU/RAM reduction is a quota-hours estimate, not an observed utilization reduction. |
| - Add an inference response-time model: reordered requests can change queueing/batching, so fixed durations need online validation. |
| - Optimize multiple resources jointly with explicit weights or a Pareto frontier and fixed quality/deadline; retain all full-schedule metrics. |
| - Collect additional workloads and real suspend/resume measurements only after validating the offline reproduction. |
|
|
| ## Where to change code |
|
|
| `search.py` owns candidate priorities, admission constraints, objective evaluation and lower bounds. `refine_storage.py` explores grouping/simultaneous starts for repeated tasks. `validate.py` is deliberately separate from vectorized search accounting; keep this independent check when adding scheduling policies. `plot_schedules.py` uses validation output so each plotted color is a physically consistent replay schedule. |
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| The current search retains fixed scientific assumptions and original deterministic RNG ordering. Refactor only with a frozen result comparison. Never turn unknown/infrastructure results into zero task scores, remove valid model mistakes, or rerun model calls merely to improve timing/accuracy statistics. |
|
|
| ## Access and scope |
|
|
| The repositories are public by owner authorization. Grant GitHub write access to teammates who will commit changes; never share account tokens. The migration contains no conversation export. Research rationale is in PROJECT_SEER.md; the experiment state and caveats are captured here. |
| |
| Notion report: https://app.notion.com/p/3ff419577f77809aa274eea270a5b461 |
| |
