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+Fetching 276 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 276/276 [00:01<00:00, 194.58it/s]
+Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [00:00<00:00, 173238.81it/s]
+Downloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 90/90 [00:00<00:00, 106967.23files/s]
+Generating train split: 119457 examples [00:00, 823555.76 examples/s]
+INFO 2025-06-14 23:27:19 ts/train.py:138 Creating policy
+Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
+100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 44.7M/44.7M [00:01<00:00, 35.7MB/s]
+INFO 2025-06-14 23:27:21 ts/train.py:144 Creating optimizer and scheduler
+INFO 2025-06-14 23:27:21 ts/train.py:156 Output dir: outputs/train/chess_pickup
+INFO 2025-06-14 23:27:21 ts/train.py:159 cfg.steps=100000 (100K)
+INFO 2025-06-14 23:27:21 ts/train.py:160 dataset.num_frames=119457 (119K)
+INFO 2025-06-14 23:27:21 ts/train.py:161 dataset.num_episodes=90
+INFO 2025-06-14 23:27:21 ts/train.py:162 num_learnable_params=51597190 (52M)
+INFO 2025-06-14 23:27:21 ts/train.py:163 num_total_params=51597238 (52M)
+INFO 2025-06-14 23:27:21 ts/train.py:202 Start offline training on a fixed dataset
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+INFO 2025-06-15 01:07:30 ts/train.py:232 step:42K smpl:336K ep:253 epch:2.81 loss:0.135 grdn:6.761 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:07:58 ts/train.py:232 step:42K smpl:338K ep:254 epch:2.83 loss:0.137 grdn:6.945 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:08:25 ts/train.py:232 step:42K smpl:339K ep:256 epch:2.84 loss:0.137 grdn:6.751 lr:1.0e-05 updt_s:0.137 data_s:0.000
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+INFO 2025-06-15 01:10:16 ts/train.py:232 step:43K smpl:346K ep:260 epch:2.89 loss:0.132 grdn:6.518 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:10:44 ts/train.py:232 step:43K smpl:347K ep:262 epch:2.91 loss:0.138 grdn:6.932 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:11:11 ts/train.py:232 step:44K smpl:349K ep:263 epch:2.92 loss:0.134 grdn:6.581 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:11:39 ts/train.py:232 step:44K smpl:350K ep:264 epch:2.93 loss:0.134 grdn:6.916 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:12:07 ts/train.py:232 step:44K smpl:352K ep:265 epch:2.95 loss:0.132 grdn:6.341 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:12:34 ts/train.py:232 step:44K smpl:354K ep:266 epch:2.96 loss:0.136 grdn:6.678 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:13:02 ts/train.py:232 step:44K smpl:355K ep:268 epch:2.97 loss:0.134 grdn:6.527 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:13:29 ts/train.py:232 step:45K smpl:357K ep:269 epch:2.99 loss:0.131 grdn:6.669 lr:1.0e-05 updt_s:0.137 data_s:0.000
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+INFO 2025-06-15 01:14:25 ts/train.py:232 step:45K smpl:360K ep:271 epch:3.01 loss:0.128 grdn:6.740 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:14:53 ts/train.py:232 step:45K smpl:362K ep:272 epch:3.03 loss:0.127 grdn:6.341 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:15:21 ts/train.py:232 step:45K smpl:363K ep:274 epch:3.04 loss:0.130 grdn:6.441 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:15:48 ts/train.py:232 step:46K smpl:365K ep:275 epch:3.05 loss:0.128 grdn:6.343 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:16:16 ts/train.py:232 step:46K smpl:366K ep:276 epch:3.07 loss:0.129 grdn:6.527 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:16:44 ts/train.py:232 step:46K smpl:368K ep:277 epch:3.08 loss:0.131 grdn:6.756 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:17:11 ts/train.py:232 step:46K smpl:370K ep:278 epch:3.09 loss:0.129 grdn:6.577 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:17:39 ts/train.py:232 step:46K smpl:371K ep:280 epch:3.11 loss:0.131 grdn:6.602 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:18:07 ts/train.py:232 step:47K smpl:373K ep:281 epch:3.12 loss:0.129 grdn:6.518 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:18:34 ts/train.py:232 step:47K smpl:374K ep:282 epch:3.13 loss:0.126 grdn:6.288 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:19:02 ts/train.py:232 step:47K smpl:376K ep:283 epch:3.15 loss:0.129 grdn:6.385 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:19:29 ts/train.py:232 step:47K smpl:378K ep:284 epch:3.16 loss:0.129 grdn:6.403 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:19:57 ts/train.py:232 step:47K smpl:379K ep:286 epch:3.17 loss:0.131 grdn:6.538 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:20:25 ts/train.py:232 step:48K smpl:381K ep:287 epch:3.19 loss:0.127 grdn:6.377 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:20:52 ts/train.py:232 step:48K smpl:382K ep:288 epch:3.20 loss:0.124 grdn:6.136 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:21:20 ts/train.py:232 step:48K smpl:384K ep:289 epch:3.21 loss:0.124 grdn:6.418 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:21:48 ts/train.py:232 step:48K smpl:386K ep:291 epch:3.23 loss:0.127 grdn:6.105 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:22:15 ts/train.py:232 step:48K smpl:387K ep:292 epch:3.24 loss:0.127 grdn:6.310 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:22:43 ts/train.py:232 step:49K smpl:389K ep:293 epch:3.25 loss:0.128 grdn:6.453 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:23:11 ts/train.py:232 step:49K smpl:390K ep:294 epch:3.27 loss:0.126 grdn:6.257 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:23:38 ts/train.py:232 step:49K smpl:392K ep:295 epch:3.28 loss:0.125 grdn:6.083 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:24:06 ts/train.py:232 step:49K smpl:394K ep:297 epch:3.29 loss:0.129 grdn:6.442 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:24:33 ts/train.py:232 step:49K smpl:395K ep:298 epch:3.31 loss:0.126 grdn:6.296 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:25:01 ts/train.py:232 step:50K smpl:397K ep:299 epch:3.32 loss:0.124 grdn:6.178 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:25:29 ts/train.py:232 step:50K smpl:398K ep:300 epch:3.34 loss:0.126 grdn:6.180 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:25:56 ts/train.py:232 step:50K smpl:400K ep:301 epch:3.35 loss:0.123 grdn:5.999 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:26:24 ts/train.py:232 step:50K smpl:402K ep:303 epch:3.36 loss:0.124 grdn:6.150 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:26:52 ts/train.py:232 step:50K smpl:403K ep:304 epch:3.38 loss:0.126 grdn:6.198 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:27:19 ts/train.py:232 step:51K smpl:405K ep:305 epch:3.39 loss:0.125 grdn:6.132 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:27:47 ts/train.py:232 step:51K smpl:406K ep:306 epch:3.40 loss:0.125 grdn:6.268 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:28:14 ts/train.py:232 step:51K smpl:408K ep:307 epch:3.42 loss:0.124 grdn:6.126 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:28:42 ts/train.py:232 step:51K smpl:410K ep:309 epch:3.43 loss:0.125 grdn:6.167 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:29:10 ts/train.py:232 step:51K smpl:411K ep:310 epch:3.44 loss:0.123 grdn:5.891 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:29:37 ts/train.py:232 step:52K smpl:413K ep:311 epch:3.46 loss:0.124 grdn:6.145 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:30:05 ts/train.py:232 step:52K smpl:414K ep:312 epch:3.47 loss:0.125 grdn:6.365 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:30:33 ts/train.py:232 step:52K smpl:416K ep:313 epch:3.48 loss:0.123 grdn:6.229 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:31:00 ts/train.py:232 step:52K smpl:418K ep:315 epch:3.50 loss:0.122 grdn:6.160 lr:1.0e-05 updt_s:0.138 data_s:0.000
+INFO 2025-06-15 01:31:28 ts/train.py:232 step:52K smpl:419K ep:316 epch:3.51 loss:0.123 grdn:5.976 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:31:55 ts/train.py:232 step:53K smpl:421K ep:317 epch:3.52 loss:0.123 grdn:5.888 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:32:23 ts/train.py:232 step:53K smpl:422K ep:318 epch:3.54 loss:0.122 grdn:6.164 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:32:51 ts/train.py:232 step:53K smpl:424K ep:319 epch:3.55 loss:0.121 grdn:6.099 lr:1.0e-05 updt_s:0.137 data_s:0.000
+INFO 2025-06-15 01:33:18 ts/train.py:232 step:53K smpl:426K ep:321 epch:3.56 loss:0.119 grdn:5.787 lr:1.0e-05 updt_s:0.137 data_s:0.000
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+INFO 2025-06-15 01:34:13 ts/train.py:232 step:54K smpl:429K ep:323 epch:3.59 loss:0.121 grdn:6.210 lr:1.0e-05 updt_s:0.137 data_s:0.000
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+INFO 2025-06-15 01:35:09 ts/train.py:232 step:54K smpl:432K ep:325 epch:3.62 loss:0.123 grdn:5.897 lr:1.0e-05 updt_s:0.137 data_s:0.000
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+INFO 2025-06-15 01:39:17 ts/train.py:232 step:56K smpl:446K ep:336 epch:3.74 loss:0.121 grdn:5.950 lr:1.0e-05 updt_s:0.137 data_s:0.000
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+INFO 2025-06-15 01:48:58 ts/train.py:241 Checkpoint policy after step 60000
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+INFO 2025-06-15 03:21:12 ts/train.py:241 Checkpoint policy after step 100000
+INFO 2025-06-15 03:21:14 ts/train.py:283 End of training

wandb/run-20250614_232714-v429osbu/files/requirements.txt

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