Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_immicro/K1000/lora16/train.log

@@ -0,0 +1,83 @@
+2026-10-02T23:20:17 split=hoL_immicro K=1000 mode=lora trainable=1,296,224 balance=cohort ye2026_sepsis: train 73 rows / 529,816 cells, inner 9; imperial_eskd: train 146 rows / 462,172 cells, inner 11; giroux2022: train 24 rows / 254,250 cells, inner 0; liu2021: train 40 rows / 147,532 cells, inner 4
+2026-10-02T23:20:18 step 0 inner-val loss 0.11485 (pretrained) {'ye2026_sepsis': 0.12609728963838684, 'imperial_eskd': 0.1086193590678952, 'liu2021': 0.10984707903116941}
+2026-10-02T23:20:28 step 250 train 0.10602 inner-val 0.11414 best 0.11414@250 (10s) ye2026_sepsis=0.1254 imperial_eskd=0.1078 liu2021=0.1092
+2026-10-02T23:20:38 step 500 train 0.10332 inner-val 0.11240 best 0.11240@500 (20s) ye2026_sepsis=0.1237 imperial_eskd=0.1059 liu2021=0.1075
+2026-10-02T23:20:48 step 750 train 0.10313 inner-val 0.11035 best 0.11035@750 (30s) ye2026_sepsis=0.1214 imperial_eskd=0.1038 liu2021=0.1058
+2026-10-02T23:20:58 step 1000 train 0.10268 inner-val 0.10862 best 0.10862@1000 (40s) ye2026_sepsis=0.1192 imperial_eskd=0.1021 liu2021=0.1045
+2026-10-02T23:21:08 step 1250 train 0.10290 inner-val 0.10750 best 0.10750@1250 (50s) ye2026_sepsis=0.1177 imperial_eskd=0.1011 liu2021=0.1037
+2026-10-02T23:21:17 step 1500 train 0.10275 inner-val 0.10686 best 0.10686@1500 (60s) ye2026_sepsis=0.1169 imperial_eskd=0.1004 liu2021=0.1032
+2026-10-02T23:21:27 step 1750 train 0.10254 inner-val 0.10646 best 0.10646@1750 (70s) ye2026_sepsis=0.1164 imperial_eskd=0.1000 liu2021=0.1030
+2026-10-02T23:21:37 step 2000 train 0.10237 inner-val 0.10620 best 0.10620@2000 (80s) ye2026_sepsis=0.1161 imperial_eskd=0.0997 liu2021=0.1028
+2026-10-02T23:21:47 step 2250 train 0.10237 inner-val 0.10603 best 0.10603@2250 (89s) ye2026_sepsis=0.1158 imperial_eskd=0.0995 liu2021=0.1027
+2026-10-02T23:21:57 step 2500 train 0.10288 inner-val 0.10590 best 0.10590@2500 (99s) ye2026_sepsis=0.1157 imperial_eskd=0.0994 liu2021=0.1026
+2026-10-02T23:22:07 step 2750 train 0.10235 inner-val 0.10580 best 0.10580@2750 (109s) ye2026_sepsis=0.1155 imperial_eskd=0.0993 liu2021=0.1025
+2026-10-02T23:22:17 step 3000 train 0.10235 inner-val 0.10573 best 0.10573@3000 (119s) ye2026_sepsis=0.1154 imperial_eskd=0.0992 liu2021=0.1025
+2026-10-02T23:22:27 step 3250 train 0.10269 inner-val 0.10568 best 0.10568@3250 (129s) ye2026_sepsis=0.1154 imperial_eskd=0.0992 liu2021=0.1025
+2026-10-02T23:22:37 step 3500 train 0.10260 inner-val 0.10564 best 0.10564@3500 (139s) ye2026_sepsis=0.1153 imperial_eskd=0.0992 liu2021=0.1024
+2026-10-02T23:22:46 step 3750 train 0.10226 inner-val 0.10561 best 0.10561@3750 (148s) ye2026_sepsis=0.1153 imperial_eskd=0.0991 liu2021=0.1024
+2026-10-02T23:22:56 step 4000 train 0.10236 inner-val 0.10558 best 0.10558@4000 (158s) ye2026_sepsis=0.1152 imperial_eskd=0.0991 liu2021=0.1024
+2026-10-02T23:23:06 step 4250 train 0.10234 inner-val 0.10556 best 0.10556@4250 (168s) ye2026_sepsis=0.1152 imperial_eskd=0.0991 liu2021=0.1024
+2026-10-02T23:23:16 step 4500 train 0.10236 inner-val 0.10555 best 0.10555@4500 (178s) ye2026_sepsis=0.1152 imperial_eskd=0.0991 liu2021=0.1024
+2026-10-02T23:23:26 step 4750 train 0.10237 inner-val 0.10553 best 0.10553@4750 (188s) ye2026_sepsis=0.1152 imperial_eskd=0.0990 liu2021=0.1024
+2026-10-02T23:23:35 step 5000 train 0.10236 inner-val 0.10552 best 0.10552@5000 (198s) ye2026_sepsis=0.1152 imperial_eskd=0.0990 liu2021=0.1024
+2026-10-02T23:23:45 step 5250 train 0.10228 inner-val 0.10551 best 0.10551@5250 (207s) ye2026_sepsis=0.1151 imperial_eskd=0.0990 liu2021=0.1024
+2026-10-02T23:23:55 step 5500 train 0.10233 inner-val 0.10550 best 0.10550@5500 (217s) ye2026_sepsis=0.1151 imperial_eskd=0.0990 liu2021=0.1024
+2026-10-02T23:24:05 step 5750 train 0.10194 inner-val 0.10550 best 0.10550@5750 (227s) ye2026_sepsis=0.1151 imperial_eskd=0.0990 liu2021=0.1024
+2026-10-02T23:24:15 step 6000 train 0.10217 inner-val 0.10549 best 0.10549@6000 (237s) ye2026_sepsis=0.1151 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T23:24:25 step 6250 train 0.10230 inner-val 0.10548 best 0.10548@6250 (247s) ye2026_sepsis=0.1151 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T23:24:35 step 6500 train 0.10231 inner-val 0.10548 best 0.10548@6500 (257s) ye2026_sepsis=0.1151 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T23:24:45 step 6750 train 0.10225 inner-val 0.10547 best 0.10547@6750 (267s) ye2026_sepsis=0.1151 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T23:24:55 step 7000 train 0.10206 inner-val 0.10546 best 0.10546@7000 (277s) ye2026_sepsis=0.1151 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:25:05 step 7250 train 0.10233 inner-val 0.10546 best 0.10546@7250 (287s) ye2026_sepsis=0.1151 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:25:16 step 7500 train 0.10220 inner-val 0.10545 best 0.10545@7500 (299s) ye2026_sepsis=0.1151 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:25:28 step 7750 train 0.10216 inner-val 0.10545 best 0.10545@7750 (310s) ye2026_sepsis=0.1151 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:25:39 step 8000 train 0.10179 inner-val 0.10544 best 0.10544@8000 (321s) ye2026_sepsis=0.1151 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:25:50 step 8250 train 0.10218 inner-val 0.10544 best 0.10544@8000 (332s) ye2026_sepsis=0.1151 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:26:00 step 8500 train 0.10187 inner-val 0.10544 best 0.10544@8500 (342s) ye2026_sepsis=0.1151 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:26:10 step 8750 train 0.10248 inner-val 0.10543 best 0.10543@8750 (352s) ye2026_sepsis=0.1151 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:26:20 step 9000 train 0.10227 inner-val 0.10543 best 0.10543@8750 (362s) ye2026_sepsis=0.1151 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:26:32 step 9250 train 0.10212 inner-val 0.10543 best 0.10543@9250 (374s) ye2026_sepsis=0.1151 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:26:41 step 9500 train 0.10213 inner-val 0.10543 best 0.10543@9250 (383s) ye2026_sepsis=0.1152 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:26:51 step 9750 train 0.10217 inner-val 0.10543 best 0.10543@9250 (393s) ye2026_sepsis=0.1152 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:27:01 step 10000 train 0.10210 inner-val 0.10543 best 0.10543@9250 (403s) ye2026_sepsis=0.1152 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:27:11 step 10250 train 0.10218 inner-val 0.10543 best 0.10543@9250 (413s) ye2026_sepsis=0.1152 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:27:22 step 10500 train 0.10235 inner-val 0.10542 best 0.10542@10500 (424s) ye2026_sepsis=0.1152 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:27:33 step 10750 train 0.10222 inner-val 0.10542 best 0.10542@10750 (435s) ye2026_sepsis=0.1152 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T23:27:43 step 11000 train 0.10200 inner-val 0.10542 best 0.10542@11000 (445s) ye2026_sepsis=0.1152 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T23:27:53 step 11250 train 0.10202 inner-val 0.10542 best 0.10542@11000 (455s) ye2026_sepsis=0.1152 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T23:28:04 step 11500 train 0.10203 inner-val 0.10542 best 0.10542@11500 (466s) ye2026_sepsis=0.1152 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T23:28:14 step 11750 train 0.10200 inner-val 0.10542 best 0.10542@11500 (476s) ye2026_sepsis=0.1152 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T23:28:23 step 12000 train 0.10181 inner-val 0.10541 best 0.10541@12000 (485s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:28:33 step 12250 train 0.10199 inner-val 0.10541 best 0.10541@12250 (495s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:28:43 step 12500 train 0.10220 inner-val 0.10541 best 0.10541@12500 (505s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:28:52 step 12750 train 0.10197 inner-val 0.10541 best 0.10541@12750 (514s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:29:02 step 13000 train 0.10222 inner-val 0.10540 best 0.10540@13000 (524s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:29:12 step 13250 train 0.10214 inner-val 0.10540 best 0.10540@13250 (534s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:29:23 step 13500 train 0.10193 inner-val 0.10540 best 0.10540@13500 (545s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:29:34 step 13750 train 0.10216 inner-val 0.10540 best 0.10540@13500 (556s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:29:45 step 14000 train 0.10207 inner-val 0.10539 best 0.10539@14000 (567s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:29:56 step 14250 train 0.10168 inner-val 0.10539 best 0.10539@14250 (578s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:30:07 step 14500 train 0.10201 inner-val 0.10539 best 0.10539@14500 (589s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:30:18 step 14750 train 0.10228 inner-val 0.10539 best 0.10539@14500 (600s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:30:28 step 15000 train 0.10225 inner-val 0.10539 best 0.10539@14500 (610s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:30:40 step 15250 train 0.10156 inner-val 0.10539 best 0.10539@14500 (622s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:30:51 step 15500 train 0.10214 inner-val 0.10538 best 0.10538@15500 (633s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:31:02 step 15750 train 0.10189 inner-val 0.10539 best 0.10538@15500 (644s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:31:13 step 16000 train 0.10184 inner-val 0.10538 best 0.10538@15500 (655s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:31:23 step 16250 train 0.10215 inner-val 0.10539 best 0.10538@15500 (665s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:31:32 step 16500 train 0.10209 inner-val 0.10539 best 0.10538@15500 (674s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:31:43 step 16750 train 0.10195 inner-val 0.10539 best 0.10538@15500 (685s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:31:52 step 17000 train 0.10225 inner-val 0.10538 best 0.10538@17000 (694s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:32:03 step 17250 train 0.10224 inner-val 0.10538 best 0.10538@17000 (705s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:32:13 step 17500 train 0.10231 inner-val 0.10538 best 0.10538@17000 (715s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:32:22 step 17750 train 0.10173 inner-val 0.10538 best 0.10538@17750 (724s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:32:32 step 18000 train 0.10238 inner-val 0.10537 best 0.10537@18000 (734s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:32:42 step 18250 train 0.10182 inner-val 0.10537 best 0.10537@18250 (744s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:32:52 step 18500 train 0.10211 inner-val 0.10537 best 0.10537@18250 (754s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:33:02 step 18750 train 0.10181 inner-val 0.10537 best 0.10537@18750 (764s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:33:13 step 19000 train 0.10198 inner-val 0.10537 best 0.10537@18750 (775s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:33:23 step 19250 train 0.10188 inner-val 0.10537 best 0.10537@19250 (785s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-02T23:33:35 step 19500 train 0.10202 inner-val 0.10537 best 0.10537@19250 (797s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-02T23:33:46 step 19750 train 0.10189 inner-val 0.10537 best 0.10537@19750 (808s) ye2026_sepsis=0.1152 imperial_eskd=0.0988 liu2021=0.1021
+2026-10-02T23:33:58 step 20000 train 0.10189 inner-val 0.10537 best 0.10537@19750 (820s) ye2026_sepsis=0.1151 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T23:34:02 saved /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_immicro/K1000/lora16/checkpoint_ft.pt (selected step 19750, inner-val 0.10537 vs pretrained 0.11485); sha 8407719a2838

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_immicro/K1000/lora16sat/RUN.json

@@ -0,0 +1,542 @@
+{
+ "schema": "pbisc_sdd.ft_crosscohort.run.v1",
+ "source_checkpoint": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/runs/f01_6k_1x_seed20260627/checkpoint_epoch_016.pt",
+ "source_checkpoint_sha256": "8c7cd0e83e2bc19fe03dfcbc084919a1e2f61be8cc8bc12f4caf543dfda91633",
+ "split": "hoL_immicro",
+ "split_file": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/splits/hoL_immicro.json",
+ "split_sha256": "d5646bee4a5f735e64f490824ca865a2cf8191234a8de40c0963cd590ebe6743",
+ "held_out": "immicro",
+ "recipe_k": 1000,
+ "balance": "cohort",
+ "mode": "lora",
+ "trainable": {
+  "mode": "lora",
+  "lora_layers": [
+   "time_encoder.0",
+   "time_encoder.2",
+   "condition_encoder.0",
+   "condition_encoder.3",
+   "context_mixer.2",
+   "down_blocks.0.input_projection",
+   "down_blocks.0.context_projection.1",
+   "down_blocks.1.input_projection",
+   "down_blocks.1.context_projection.1",
+   "down_blocks.2.input_projection",
+   "down_blocks.2.context_projection.1",
+   "down_blocks.3.input_projection",
+   "down_blocks.3.context_projection.1",
+   "up_blocks.0.input_projection",
+   "up_blocks.0.context_projection.1",
+   "up_blocks.1.input_projection",
+   "up_blocks.1.context_projection.1",
+   "up_blocks.2.input_projection",
+   "up_blocks.2.context_projection.1",
+   "output_trunk.0",
+   "value_head",
+   "active_head"
+  ],
+  "lora_rank": 16,
+  "lora_alpha": 16.0,
+  "n_trainable": 1296224,
+  "n_total_base": 54185184,
+  "trainable_tensors": 44
+ },
+ "lr": 0.0005,
+ "warmup": 200,
+ "batch_size": 256,
+ "ema_decay": 0.999,
+ "max_steps": 200000,
+ "steps_run": 28500,
+ "eval_every": 250,
+ "patience": 8,
+ "selected_step": 26500,
+ "selected_inner_val_loss": 0.1053253309236783,
+ "pretrained_inner_val_loss": 0.11485457591248383,
+ "cohorts": [
+  {
+   "cohort": "ye2026_sepsis",
+   "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/data/ye2026_sepsis/conditions_raw_K1000.npz",
+   "condition_npz_sha256": "ce8b61d7dcd3e62b9e63d52189cfb5bba881f87a661384277a7949a629231cb4",
+   "train_rows": [
+    "Abd-PS04",
+    "Abd-PS05",
+    "Abd-PS07",
+    "Abd-PS19",
+    "Abd-PS20",
+    "Abd-PS27",
+    "Abd-S103",
+    "Abd-S120",
+    "Abd-S123",
+    "Abd-S156",
+    "Abd-S39",
+    "Abd-S42",
+    "Abd-S47",
+    "Abd-S51",
+    "Abd-S57",
+    "Abd-S66",
+    "Abd-S79",
+    "Abd-S83",
+    "HC05",
+    "HC07",
+    "HC09",
+    "HC12",
+    "HC13",
+    "HC16",
+    "IC10",
+    "IC15",
+    "IC16",
+    "IC22",
+    "IC23",
+    "PHC01",
+    "PHC02",
+    "PHC03",
+    "PHC05",
+    "PIC01",
+    "PIC02",
+    "PIC04",
+    "PIC05",
+    "Res-PS02",
+    "Res-PS03",
+    "Res-PS12",
+    "Res-PS21",
+    "Res-PS22",
+    "Res-PS25",
+    "Res-PS26",
+    "Res-S10",
+    "Res-S125",
+    "Res-S127",
+    "Res-S136",
+    "Res-S146",
+    "Res-S149",
+    "Res-S150",
+    "Res-S164",
+    "Res-S166",
+    "Res-S177",
+    "Res-S184",
+    "Res-S19",
+    "Res-S26",
+    "Res-S40",
+    "Res-S77",
+    "Res-S87",
+    "Skin-S140",
+    "Skin-S142",
+    "Skin-S152",
+    "Skin-S38",
+    "Skin-S43",
+    "Skin-S46",
+    "Skin-S52",
+    "Skin-S74",
+    "Uri-S134",
+    "Uri-S147",
+    "Uri-S157",
+    "Uri-S20",
+    "Uri-S93"
+   ],
+   "inner_val_rows": [
+    "Abd-PS23",
+    "Abd-S102",
+    "Abd-S54",
+    "Abd-S98",
+    "HC01",
+    "IC19",
+    "Res-PS16",
+    "Res-S139",
+    "Res-S174"
+   ]
+  },
+  {
+   "cohort": "imperial_eskd",
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paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_immicro/K1000/lora16sat/history.json

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paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_immicro/K1000/lora16sat/milestones.json

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