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Add distributed server implementation and protocol definitions

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- Introduced `protocol.py` for shared data models used in server/client communication, including request and response schemas for registration, job submission, and status updates.
- Implemented `server.py` to manage a SQLite job queue and client registry, handling job polling, status updates, and job completion.
- Created a cheat sheet for server usage, detailing commands for starting the server, submitting jobs, and monitoring clients.
- Added several experiment configuration files for various training setups, including geometry vector injections and baseline ensembles.
This commit is contained in:
rpotter6298
2026-04-28 08:24:25 +02:00
parent 4dea45df78
commit 512ebd13b2
42 changed files with 4468 additions and 612 deletions
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v4/scripts/experiments/geom_gt_sweep.json
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[
{
"run_name": "experiments/ensemble_fused/geom_gt"
}
]
v4/scripts/experiments/geometry_vec_gt_sweep.json
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[
{
"_note": "Geometry vector injection, GT source, all 5 features — v4 equivalent of v3 phase6a.",
"run_name": "experiments/geometry_vec_gt/dim5"
}
]
v4/scripts/experiments/no_geom_sweep.json
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[
{
"run_name": "experiments/ensemble_fused/no_geom"
}
]
v4/scripts/experiments/tri_v1/baseline_ensemble.json
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[
{
"_note": "Replacement run of ensemble_fused (img + cd) — keeps original alongside.",
"run_name": "experiments/tri_v1/baseline_ensemble"
}
]
v4/scripts/experiments/tri_v1/baseline_solo.json
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[
{
"_note": "Standalone geometry tower (UNet seg, MonoBridge) — 10-rep baseline.",
"run_name": "experiments/tri_v1/baseline_solo"
}
]
v4/scripts/experiments/tri_v1/baseline_tri.json
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[
{
"_note": "Tritower with default params (bcd_prob=0.5, cw=false, nt_epochs=36) — 10-rep baseline.",
"run_name": "experiments/tri_v1/baseline_tri"
}
]
v4/scripts/experiments/tri_v1/grid.json
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[
{ "_note": "tritower grid pilot (3 reps each) — 3 bcd × 2 cw × 3 nt_epochs = 18 cells", "run_name": "experiments/tri_v1/grid/bcd35_cw0_nt15", "reps": 3, "overrides": { "training": { "bcd_prob": 0.35, "class_weighted": false } }, "stage_overrides": { "nt": { "epochs": 15 } } },
{ "run_name": "experiments/tri_v1/grid/bcd35_cw0_nt25", "reps": 3, "overrides": { "training": { "bcd_prob": 0.35, "class_weighted": false } }, "stage_overrides": { "nt": { "epochs": 25 } } },
{ "run_name": "experiments/tri_v1/grid/bcd35_cw0_nt36", "reps": 3, "overrides": { "training": { "bcd_prob": 0.35, "class_weighted": false } }, "stage_overrides": { "nt": { "epochs": 36 } } },
{ "run_name": "experiments/tri_v1/grid/bcd35_cw1_nt15", "reps": 3, "overrides": { "training": { "bcd_prob": 0.35, "class_weighted": true } }, "stage_overrides": { "nt": { "epochs": 15 } } },
{ "run_name": "experiments/tri_v1/grid/bcd35_cw1_nt25", "reps": 3, "overrides": { "training": { "bcd_prob": 0.35, "class_weighted": true } }, "stage_overrides": { "nt": { "epochs": 25 } } },
{ "run_name": "experiments/tri_v1/grid/bcd35_cw1_nt36", "reps": 3, "overrides": { "training": { "bcd_prob": 0.35, "class_weighted": true } }, "stage_overrides": { "nt": { "epochs": 36 } } },
{ "run_name": "experiments/tri_v1/grid/bcd50_cw0_nt15", "reps": 3, "overrides": { "training": { "bcd_prob": 0.50, "class_weighted": false } }, "stage_overrides": { "nt": { "epochs": 15 } } },
{ "run_name": "experiments/tri_v1/grid/bcd50_cw0_nt25", "reps": 3, "overrides": { "training": { "bcd_prob": 0.50, "class_weighted": false } }, "stage_overrides": { "nt": { "epochs": 25 } } },
{ "run_name": "experiments/tri_v1/grid/bcd50_cw0_nt36", "reps": 3, "overrides": { "training": { "bcd_prob": 0.50, "class_weighted": false } }, "stage_overrides": { "nt": { "epochs": 36 } } },
{ "run_name": "experiments/tri_v1/grid/bcd50_cw1_nt15", "reps": 3, "overrides": { "training": { "bcd_prob": 0.50, "class_weighted": true } }, "stage_overrides": { "nt": { "epochs": 15 } } },
{ "run_name": "experiments/tri_v1/grid/bcd50_cw1_nt25", "reps": 3, "overrides": { "training": { "bcd_prob": 0.50, "class_weighted": true } }, "stage_overrides": { "nt": { "epochs": 25 } } },
{ "run_name": "experiments/tri_v1/grid/bcd50_cw1_nt36", "reps": 3, "overrides": { "training": { "bcd_prob": 0.50, "class_weighted": true } }, "stage_overrides": { "nt": { "epochs": 36 } } },
{ "run_name": "experiments/tri_v1/grid/bcd75_cw0_nt15", "reps": 3, "overrides": { "training": { "bcd_prob": 0.75, "class_weighted": false } }, "stage_overrides": { "nt": { "epochs": 15 } } },
{ "run_name": "experiments/tri_v1/grid/bcd75_cw0_nt25", "reps": 3, "overrides": { "training": { "bcd_prob": 0.75, "class_weighted": false } }, "stage_overrides": { "nt": { "epochs": 25 } } },
{ "run_name": "experiments/tri_v1/grid/bcd75_cw0_nt36", "reps": 3, "overrides": { "training": { "bcd_prob": 0.75, "class_weighted": false } }, "stage_overrides": { "nt": { "epochs": 36 } } },
{ "run_name": "experiments/tri_v1/grid/bcd75_cw1_nt15", "reps": 3, "overrides": { "training": { "bcd_prob": 0.75, "class_weighted": true } }, "stage_overrides": { "nt": { "epochs": 15 } } },
{ "run_name": "experiments/tri_v1/grid/bcd75_cw1_nt25", "reps": 3, "overrides": { "training": { "bcd_prob": 0.75, "class_weighted": true } }, "stage_overrides": { "nt": { "epochs": 25 } } },
{ "run_name": "experiments/tri_v1/grid/bcd75_cw1_nt36", "reps": 3, "overrides": { "training": { "bcd_prob": 0.75, "class_weighted": true } }, "stage_overrides": { "nt": { "epochs": 36 } } }
]