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+INFO 2025-07-10 18:22:41 ts/train.py:144 Creating optimizer and scheduler
+INFO 2025-07-10 18:22:41 ts/train.py:156 Output dir: outputs/train/act_jenga
+INFO 2025-07-10 18:22:41 ts/train.py:159 cfg.steps=100000 (100K)
+INFO 2025-07-10 18:22:41 ts/train.py:160 dataset.num_frames=2570 (3K)
+INFO 2025-07-10 18:22:41 ts/train.py:161 dataset.num_episodes=10
+INFO 2025-07-10 18:22:41 ts/train.py:162 num_learnable_params=51597190 (52M)
+INFO 2025-07-10 18:22:41 ts/train.py:163 num_total_params=51597238 (52M)
+INFO 2025-07-10 18:22:41 ts/train.py:202 Start offline training on a fixed dataset
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+INFO 2025-07-10 18:23:01 ts/train.py:232 step:400 smpl:3K ep:12 epch:1.25 loss:2.860 grdn:84.204 lr:1.0e-05 updt_s:0.049 data_s:0.001
+INFO 2025-07-10 18:23:11 ts/train.py:232 step:600 smpl:5K ep:19 epch:1.87 loss:2.351 grdn:77.869 lr:1.0e-05 updt_s:0.049 data_s:0.000
+INFO 2025-07-10 18:23:21 ts/train.py:232 step:800 smpl:6K ep:25 epch:2.49 loss:1.986 grdn:69.938 lr:1.0e-05 updt_s:0.049 data_s:0.001
+INFO 2025-07-10 18:23:31 ts/train.py:232 step:1K smpl:8K ep:31 epch:3.11 loss:1.790 grdn:67.803 lr:1.0e-05 updt_s:0.048 data_s:0.001
+INFO 2025-07-10 18:23:41 ts/train.py:232 step:1K smpl:10K ep:37 epch:3.74 loss:1.587 grdn:62.692 lr:1.0e-05 updt_s:0.048 data_s:0.000
+INFO 2025-07-10 18:23:51 ts/train.py:232 step:1K smpl:11K ep:44 epch:4.36 loss:1.438 grdn:60.526 lr:1.0e-05 updt_s:0.048 data_s:0.001
+INFO 2025-07-10 18:24:01 ts/train.py:232 step:2K smpl:13K ep:50 epch:4.98 loss:1.297 grdn:57.273 lr:1.0e-05 updt_s:0.048 data_s:0.000
+INFO 2025-07-10 18:24:10 ts/train.py:232 step:2K smpl:14K ep:56 epch:5.60 loss:1.180 grdn:54.129 lr:1.0e-05 updt_s:0.048 data_s:0.001
+INFO 2025-07-10 18:24:20 ts/train.py:232 step:2K smpl:16K ep:62 epch:6.23 loss:1.073 grdn:53.256 lr:1.0e-05 updt_s:0.048 data_s:0.001
+INFO 2025-07-10 18:24:30 ts/train.py:232 step:2K smpl:18K ep:68 epch:6.85 loss:0.976 grdn:50.010 lr:1.0e-05 updt_s:0.049 data_s:0.000
+INFO 2025-07-10 18:24:40 ts/train.py:232 step:2K smpl:19K ep:75 epch:7.47 loss:0.869 grdn:47.136 lr:1.0e-05 updt_s:0.048 data_s:0.001
+INFO 2025-07-10 18:24:50 ts/train.py:232 step:3K smpl:21K ep:81 epch:8.09 loss:0.785 grdn:44.460 lr:1.0e-05 updt_s:0.048 data_s:0.001
+INFO 2025-07-10 18:25:00 ts/train.py:232 step:3K smpl:22K ep:87 epch:8.72 loss:0.710 grdn:42.699 lr:1.0e-05 updt_s:0.049 data_s:0.000
+INFO 2025-07-10 18:25:10 ts/train.py:232 step:3K smpl:24K ep:93 epch:9.34 loss:0.647 grdn:41.767 lr:1.0e-05 updt_s:0.048 data_s:0.001
+INFO 2025-07-10 18:25:19 ts/train.py:232 step:3K smpl:26K ep:100 epch:9.96 loss:0.584 grdn:38.589 lr:1.0e-05 updt_s:0.048 data_s:0.000
+INFO 2025-07-10 18:25:29 ts/train.py:232 step:3K smpl:27K ep:106 epch:10.58 loss:0.524 grdn:35.853 lr:1.0e-05 updt_s:0.048 data_s:0.001
+INFO 2025-07-10 18:25:39 ts/train.py:232 step:4K smpl:29K ep:112 epch:11.21 loss:0.476 grdn:35.067 lr:1.0e-05 updt_s:0.049 data_s:0.001
+INFO 2025-07-10 18:25:49 ts/train.py:232 step:4K smpl:30K ep:118 epch:11.83 loss:0.435 grdn:32.412 lr:1.0e-05 updt_s:0.048 data_s:0.000
+INFO 2025-07-10 18:26:00 ts/train.py:232 step:4K smpl:32K ep:125 epch:12.45 loss:0.403 grdn:32.250 lr:1.0e-05 updt_s:0.051 data_s:0.001
+INFO 2025-07-10 18:26:10 ts/train.py:232 step:4K smpl:34K ep:131 epch:13.07 loss:0.371 grdn:30.517 lr:1.0e-05 updt_s:0.051 data_s:0.001
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+INFO 2025-07-10 18:40:02 ts/train.py:241 Checkpoint policy after step 20000
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+INFO 2025-07-10 18:57:36 ts/train.py:241 Checkpoint policy after step 40000
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+INFO 2025-07-10 19:15:10 ts/train.py:241 Checkpoint policy after step 60000
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+INFO 2025-07-10 19:32:44 ts/train.py:241 Checkpoint policy after step 80000
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+INFO 2025-07-10 19:50:18 ts/train.py:241 Checkpoint policy after step 100000
+INFO 2025-07-10 19:50:18 ts/train.py:283 End of training

wandb/run-20250710_182237-9a34iqpp/files/requirements.txt

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