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_ye2026_sepsis/K1000/lora16/train.log

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+2026-10-02T22:44:47 split=hoL_ye2026_sepsis K=1000 mode=lora trainable=1,296,224 balance=cohort immicro: train 95 rows / 347,061 cells, inner 12; 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
+2026-10-02T22:44:49 step 0 inner-val loss 0.10828 (pretrained) {'immicro': 0.10636159498244524, 'imperial_eskd': 0.1086193590678952, 'liu2021': 0.10984707903116941}
+2026-10-02T22:44:59 step 250 train 0.10334 inner-val 0.10760 best 0.10760@250 (10s) immicro=0.1057 imperial_eskd=0.1079 liu2021=0.1092
+2026-10-02T22:45:11 step 500 train 0.10093 inner-val 0.10596 best 0.10596@500 (22s) immicro=0.1043 imperial_eskd=0.1061 liu2021=0.1075
+2026-10-02T22:45:22 step 750 train 0.10057 inner-val 0.10429 best 0.10429@750 (33s) immicro=0.1030 imperial_eskd=0.1041 liu2021=0.1059
+2026-10-02T22:45:34 step 1000 train 0.10027 inner-val 0.10292 best 0.10292@1000 (45s) immicro=0.1019 imperial_eskd=0.1023 liu2021=0.1046
+2026-10-02T22:45:45 step 1250 train 0.10035 inner-val 0.10197 best 0.10197@1250 (56s) immicro=0.1010 imperial_eskd=0.1012 liu2021=0.1037
+2026-10-02T22:45:57 step 1500 train 0.10010 inner-val 0.10134 best 0.10134@1500 (68s) immicro=0.1004 imperial_eskd=0.1004 liu2021=0.1032
+2026-10-02T22:46:08 step 1750 train 0.10021 inner-val 0.10093 best 0.10093@1750 (79s) immicro=0.0999 imperial_eskd=0.1000 liu2021=0.1029
+2026-10-02T22:46:20 step 2000 train 0.09997 inner-val 0.10067 best 0.10067@2000 (90s) immicro=0.0996 imperial_eskd=0.0997 liu2021=0.1027
+2026-10-02T22:46:31 step 2250 train 0.10009 inner-val 0.10049 best 0.10049@2250 (102s) immicro=0.0994 imperial_eskd=0.0995 liu2021=0.1026
+2026-10-02T22:46:43 step 2500 train 0.10035 inner-val 0.10037 best 0.10037@2500 (113s) immicro=0.0992 imperial_eskd=0.0994 liu2021=0.1025
+2026-10-02T22:46:54 step 2750 train 0.09997 inner-val 0.10028 best 0.10028@2750 (125s) immicro=0.0991 imperial_eskd=0.0993 liu2021=0.1025
+2026-10-02T22:47:05 step 3000 train 0.09993 inner-val 0.10022 best 0.10022@3000 (136s) immicro=0.0990 imperial_eskd=0.0992 liu2021=0.1024
+2026-10-02T22:47:15 step 3250 train 0.10014 inner-val 0.10018 best 0.10018@3250 (146s) immicro=0.0990 imperial_eskd=0.0992 liu2021=0.1024
+2026-10-02T22:47:25 step 3500 train 0.10010 inner-val 0.10015 best 0.10015@3500 (156s) immicro=0.0989 imperial_eskd=0.0992 liu2021=0.1024
+2026-10-02T22:47:35 step 3750 train 0.09998 inner-val 0.10012 best 0.10012@3750 (166s) immicro=0.0989 imperial_eskd=0.0991 liu2021=0.1024
+2026-10-02T22:47:45 step 4000 train 0.09992 inner-val 0.10010 best 0.10010@4000 (176s) immicro=0.0988 imperial_eskd=0.0991 liu2021=0.1023
+2026-10-02T22:47:55 step 4250 train 0.09995 inner-val 0.10008 best 0.10008@4250 (186s) immicro=0.0988 imperial_eskd=0.0991 liu2021=0.1023
+2026-10-02T22:48:05 step 4500 train 0.09989 inner-val 0.10007 best 0.10007@4500 (196s) immicro=0.0988 imperial_eskd=0.0991 liu2021=0.1023
+2026-10-02T22:48:15 step 4750 train 0.09979 inner-val 0.10006 best 0.10006@4750 (206s) immicro=0.0988 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T22:48:25 step 5000 train 0.09996 inner-val 0.10004 best 0.10004@5000 (216s) immicro=0.0988 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T22:48:35 step 5250 train 0.09992 inner-val 0.10003 best 0.10003@5250 (226s) immicro=0.0988 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T22:48:45 step 5500 train 0.09996 inner-val 0.10002 best 0.10002@5500 (236s) immicro=0.0987 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T22:48:55 step 5750 train 0.09952 inner-val 0.10001 best 0.10001@5750 (246s) immicro=0.0987 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T22:49:05 step 6000 train 0.09982 inner-val 0.10000 best 0.10000@6000 (256s) immicro=0.0987 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T22:49:15 step 6250 train 0.09987 inner-val 0.09999 best 0.09999@6250 (266s) immicro=0.0987 imperial_eskd=0.0990 liu2021=0.1023
+2026-10-02T22:49:25 step 6500 train 0.09995 inner-val 0.09998 best 0.09998@6500 (276s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T22:49:35 step 6750 train 0.09981 inner-val 0.09997 best 0.09997@6750 (286s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T22:49:45 step 7000 train 0.09974 inner-val 0.09996 best 0.09996@7000 (296s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T22:49:55 step 7250 train 0.09997 inner-val 0.09995 best 0.09995@7250 (306s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T22:50:05 step 7500 train 0.09978 inner-val 0.09994 best 0.09994@7500 (316s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T22:50:15 step 7750 train 0.09977 inner-val 0.09994 best 0.09994@7750 (326s) immicro=0.0987 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T22:50:25 step 8000 train 0.09947 inner-val 0.09993 best 0.09993@8000 (336s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T22:50:35 step 8250 train 0.09992 inner-val 0.09992 best 0.09992@8250 (346s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1023
+2026-10-02T22:50:45 step 8500 train 0.09951 inner-val 0.09992 best 0.09992@8500 (356s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:50:55 step 8750 train 0.09998 inner-val 0.09991 best 0.09991@8750 (366s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:51:05 step 9000 train 0.09978 inner-val 0.09991 best 0.09991@9000 (376s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:51:15 step 9250 train 0.09980 inner-val 0.09990 best 0.09990@9250 (386s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:51:25 step 9500 train 0.09972 inner-val 0.09990 best 0.09990@9500 (396s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:51:35 step 9750 train 0.09976 inner-val 0.09990 best 0.09990@9750 (406s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:51:45 step 10000 train 0.09970 inner-val 0.09990 best 0.09990@10000 (416s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:51:55 step 10250 train 0.09985 inner-val 0.09990 best 0.09990@10250 (426s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:52:05 step 10500 train 0.09991 inner-val 0.09989 best 0.09989@10500 (436s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:52:15 step 10750 train 0.09971 inner-val 0.09990 best 0.09989@10500 (446s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:52:25 step 11000 train 0.09973 inner-val 0.09989 best 0.09989@10500 (456s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:52:35 step 11250 train 0.09965 inner-val 0.09989 best 0.09989@11250 (466s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:52:47 step 11500 train 0.09972 inner-val 0.09989 best 0.09989@11500 (478s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:52:58 step 11750 train 0.09962 inner-val 0.09988 best 0.09988@11750 (489s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:53:10 step 12000 train 0.09943 inner-val 0.09988 best 0.09988@12000 (500s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:53:21 step 12250 train 0.09960 inner-val 0.09988 best 0.09988@12250 (512s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:53:32 step 12500 train 0.09978 inner-val 0.09987 best 0.09987@12500 (523s) immicro=0.0986 imperial_eskd=0.0989 liu2021=0.1022
+2026-10-02T22:53:44 step 12750 train 0.09972 inner-val 0.09987 best 0.09987@12750 (535s) immicro=0.0986 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:53:55 step 13000 train 0.09981 inner-val 0.09987 best 0.09987@13000 (546s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:54:07 step 13250 train 0.09978 inner-val 0.09986 best 0.09986@13250 (558s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:54:18 step 13500 train 0.09964 inner-val 0.09986 best 0.09986@13500 (569s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:54:29 step 13750 train 0.09974 inner-val 0.09986 best 0.09986@13750 (580s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:54:41 step 14000 train 0.09959 inner-val 0.09986 best 0.09986@14000 (592s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:54:52 step 14250 train 0.09949 inner-val 0.09985 best 0.09985@14250 (603s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:55:04 step 14500 train 0.09984 inner-val 0.09985 best 0.09985@14500 (614s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:55:15 step 14750 train 0.09970 inner-val 0.09985 best 0.09985@14750 (626s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:55:26 step 15000 train 0.09981 inner-val 0.09985 best 0.09985@14750 (637s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:55:38 step 15250 train 0.09957 inner-val 0.09985 best 0.09985@14750 (649s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:55:49 step 15500 train 0.09977 inner-val 0.09985 best 0.09985@15500 (660s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:56:01 step 15750 train 0.09952 inner-val 0.09984 best 0.09984@15750 (672s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:56:12 step 16000 train 0.09958 inner-val 0.09984 best 0.09984@15750 (683s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:56:24 step 16250 train 0.09966 inner-val 0.09984 best 0.09984@15750 (694s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:56:35 step 16500 train 0.09978 inner-val 0.09984 best 0.09984@16500 (706s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:56:46 step 16750 train 0.09950 inner-val 0.09984 best 0.09984@16750 (717s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:56:58 step 17000 train 0.09981 inner-val 0.09984 best 0.09984@16750 (728s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:57:09 step 17250 train 0.09989 inner-val 0.09984 best 0.09984@17250 (740s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:57:20 step 17500 train 0.09974 inner-val 0.09983 best 0.09983@17500 (751s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:57:32 step 17750 train 0.09945 inner-val 0.09983 best 0.09983@17750 (763s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:57:43 step 18000 train 0.10002 inner-val 0.09983 best 0.09983@17750 (774s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:57:55 step 18250 train 0.09945 inner-val 0.09983 best 0.09983@17750 (786s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:58:06 step 18500 train 0.09975 inner-val 0.09983 best 0.09983@17750 (797s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:58:18 step 18750 train 0.09952 inner-val 0.09982 best 0.09982@18750 (808s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:58:29 step 19000 train 0.09966 inner-val 0.09982 best 0.09982@19000 (820s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:58:40 step 19250 train 0.09955 inner-val 0.09982 best 0.09982@19250 (831s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:58:52 step 19500 train 0.09954 inner-val 0.09982 best 0.09982@19250 (842s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:59:03 step 19750 train 0.09948 inner-val 0.09982 best 0.09982@19250 (854s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:59:14 step 20000 train 0.09970 inner-val 0.09982 best 0.09982@19250 (865s) immicro=0.0985 imperial_eskd=0.0988 liu2021=0.1022
+2026-10-02T22:59:18 saved /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/K1000/lora16/checkpoint_ft.pt (selected step 19250, inner-val 0.09982 vs pretrained 0.10828); sha 40961e0229e5

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/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_ye2026_sepsis",
+ "split_file": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/splits/hoL_ye2026_sepsis.json",
+ "split_sha256": "39cad7b25faf6c15cd65f0512977b9e526353cfb085dc2c45d18f8edc56db426",
+ "held_out": "ye2026_sepsis",
+ "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": 26750,
+ "eval_every": 250,
+ "patience": 8,
+ "selected_step": 24750,
+ "selected_inner_val_loss": 0.09978930106518245,
+ "pretrained_inner_val_loss": 0.10827601102716995,
+ "cohorts": [
+  {
+   "cohort": "immicro",
+   "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/data/immicro/conditions_raw_K1000.npz",
+   "condition_npz_sha256": "2be5717d19e86bdf38e4838d4bd2886a211ba78bfbfe749c458305a3e0ac2dcc",
+   "train_rows": [
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+   ],
+   "inner_val_rows": [
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+   ]
+  },
+  {
+   "cohort": "imperial_eskd",
+   "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/data/imperial_eskd/conditions_raw_K1000.npz",
+   "condition_npz_sha256": "41a62291e98e367d05108a0a95a859ef9ca1997f1fda4656cba58eeefbe56442",
+   "train_rows": [
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paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/runs/hoL_ye2026_sepsis/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_ye2026_sepsis/K1000/lora16sat/milestones.json

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