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Creating dataset +Resolving data files: 100%|█████████████████████████████████████████████████| 50/50 [00:00<00:00, 253585.49it/s] +INFO 2025-04-02 16:41:22 ts/train.py:136 Creating env +INFO 2025-04-02 16:41:22 /__init__.py:84 MUJOCO_GL=%s, attempting to import specified OpenGL backend. +INFO 2025-04-02 16:41:22 pesloader.py:70 Failed to load library ( %r ): %s +INFO 2025-04-02 16:41:22 /__init__.py:31 MuJoCo library version is: %s +INFO 2025-04-02 16:41:26 ts/train.py:139 Creating policy +INFO 2025-04-02 16:41:26 ts/train.py:145 Creating optimizer and scheduler +INFO 2025-04-02 16:41:26 ts/train.py:157 Output dir: outputs/train/pusht_aloha_transfer_cube +INFO 2025-04-02 16:41:26 ts/train.py:159 cfg.env.task='AlohaTransferCube-v0' +INFO 2025-04-02 16:41:26 ts/train.py:160 cfg.steps=100000 (100K) +INFO 2025-04-02 16:41:26 ts/train.py:161 dataset.num_frames=20000 (20K) +INFO 2025-04-02 16:41:26 ts/train.py:162 dataset.num_episodes=50 +INFO 2025-04-02 16:41:26 ts/train.py:163 num_learnable_params=2905610 (3M) +INFO 2025-04-02 16:41:26 ts/train.py:164 num_total_params=14072612 (14M) +INFO 2025-04-02 16:41:26 ts/train.py:203 Start offline training on a fixed dataset +INFO 2025-04-02 16:45:12 ts/train.py:254 step:1K smpl:8K ep:20 epch:0.40 loss:0.170 grdn:1.066 lr:1.0e-04 updt_s:0.217 data_s:0.008 +INFO 2025-04-02 16:47:35 ts/train.py:254 step:2K smpl:16K ep:40 epch:0.80 loss:0.103 grdn:0.912 lr:1.0e-04 updt_s:0.142 data_s:0.000 +INFO 2025-04-02 16:50:01 ts/train.py:254 step:3K smpl:24K ep:60 epch:1.20 loss:0.090 grdn:0.857 lr:1.0e-04 updt_s:0.141 data_s:0.005 +INFO 2025-04-02 16:52:23 ts/train.py:254 step:4K smpl:32K ep:80 epch:1.60 loss:0.082 grdn:0.845 lr:1.0e-04 updt_s:0.141 data_s:0.000 +INFO 2025-04-02 16:54:45 ts/train.py:254 step:5K smpl:40K ep:100 epch:2.00 loss:0.077 grdn:0.820 lr:1.0e-04 updt_s:0.141 data_s:0.000 +INFO 2025-04-02 16:57:11 ts/train.py:254 step:6K smpl:48K ep:120 epch:2.40 loss:0.073 grdn:0.804 lr:1.0e-04 updt_s:0.141 data_s:0.004 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data_s:0.000 +INFO 2025-04-02 18:56:24 ts/train.py:254 step:55K smpl:440K ep:1K epch:22.00 loss:0.025 grdn:0.543 lr:1.0e-04 updt_s:0.145 data_s:0.000 +INFO 2025-04-02 18:58:56 ts/train.py:254 step:56K smpl:448K ep:1K epch:22.40 loss:0.025 grdn:0.559 lr:1.0e-04 updt_s:0.146 data_s:0.005 +INFO 2025-04-02 19:01:23 ts/train.py:254 step:57K smpl:456K ep:1K epch:22.80 loss:0.025 grdn:0.543 lr:1.0e-04 updt_s:0.146 data_s:0.000 +INFO 2025-04-02 19:03:53 ts/train.py:254 step:58K smpl:464K ep:1K epch:23.20 loss:0.024 grdn:0.543 lr:1.0e-04 updt_s:0.145 data_s:0.005 +INFO 2025-04-02 19:06:20 ts/train.py:254 step:59K smpl:472K ep:1K epch:23.60 loss:0.024 grdn:0.539 lr:1.0e-04 updt_s:0.145 data_s:0.000 +INFO 2025-04-02 19:08:46 ts/train.py:254 step:60K smpl:480K ep:1K epch:24.00 loss:0.024 grdn:0.535 lr:1.0e-04 updt_s:0.145 data_s:0.000 +INFO 2025-04-02 19:08:46 ts/train.py:263 Checkpoint policy after step 60000 +INFO 2025-04-02 19:11:17 ts/train.py:254 step:61K smpl:488K ep:1K epch:24.40 loss:0.024 grdn:0.539 lr:1.0e-04 updt_s:0.146 data_s:0.005 +INFO 2025-04-02 19:13:44 ts/train.py:254 step:62K smpl:496K ep:1K epch:24.80 loss:0.024 grdn:0.539 lr:1.0e-04 updt_s:0.146 data_s:0.000 +INFO 2025-04-02 19:16:15 ts/train.py:254 step:63K smpl:504K ep:1K epch:25.20 loss:0.023 grdn:0.534 lr:1.0e-04 updt_s:0.146 data_s:0.004 +INFO 2025-04-02 19:18:42 ts/train.py:254 step:64K smpl:512K ep:1K epch:25.60 loss:0.023 grdn:0.536 lr:1.0e-04 updt_s:0.146 data_s:0.000 +INFO 2025-04-02 19:21:09 ts/train.py:254 step:65K smpl:520K ep:1K epch:26.00 loss:0.023 grdn:0.532 lr:1.0e-04 updt_s:0.146 data_s:0.000 +INFO 2025-04-02 19:23:40 ts/train.py:254 step:66K smpl:528K ep:1K epch:26.40 loss:0.023 grdn:0.529 lr:1.0e-04 updt_s:0.146 data_s:0.005 +INFO 2025-04-02 19:26:08 ts/train.py:254 step:67K smpl:536K ep:1K epch:26.80 loss:0.023 grdn:0.524 lr:1.0e-04 updt_s:0.146 data_s:0.000 +INFO 2025-04-02 19:28:39 ts/train.py:254 step:68K smpl:544K ep:1K epch:27.20 loss:0.023 grdn:0.520 lr:1.0e-04 updt_s:0.147 data_s:0.004 +INFO 2025-04-02 19:31:07 ts/train.py:254 step:69K smpl:552K ep:1K epch:27.60 loss:0.022 grdn:0.525 lr:1.0e-04 updt_s:0.146 data_s:0.000 +INFO 2025-04-02 19:33:34 ts/train.py:254 step:70K smpl:560K ep:1K epch:28.00 loss:0.022 grdn:0.524 lr:1.0e-04 updt_s:0.146 data_s:0.000 +INFO 2025-04-02 19:33:34 ts/train.py:263 Checkpoint policy after step 70000 +INFO 2025-04-02 19:36:05 ts/train.py:254 step:71K smpl:568K ep:1K epch:28.40 loss:0.022 grdn:0.522 lr:1.0e-04 updt_s:0.146 data_s:0.004 +INFO 2025-04-02 19:38:32 ts/train.py:254 step:72K smpl:576K ep:1K epch:28.80 loss:0.022 grdn:0.522 lr:1.0e-04 updt_s:0.146 data_s:0.000 +INFO 2025-04-02 19:41:04 ts/train.py:254 step:73K smpl:584K ep:1K epch:29.20 loss:0.022 grdn:0.509 lr:1.0e-04 updt_s:0.147 data_s:0.005 +INFO 2025-04-02 19:43:32 ts/train.py:254 step:74K smpl:592K ep:1K epch:29.60 loss:0.021 grdn:0.513 lr:1.0e-04 updt_s:0.147 data_s:0.000 +INFO 2025-04-02 19:45:59 ts/train.py:254 step:75K smpl:600K ep:2K epch:30.00 loss:0.022 grdn:0.516 lr:1.0e-04 updt_s:0.146 data_s:0.000 +INFO 2025-04-02 19:48:31 ts/train.py:254 step:76K smpl:608K ep:2K epch:30.40 loss:0.021 grdn:0.510 lr:1.0e-04 updt_s:0.146 data_s:0.005 +INFO 2025-04-02 19:50:58 ts/train.py:254 step:77K smpl:616K ep:2K epch:30.80 loss:0.021 grdn:0.508 lr:1.0e-04 updt_s:0.147 data_s:0.000 +INFO 2025-04-02 19:53:31 ts/train.py:254 step:78K smpl:624K ep:2K epch:31.20 loss:0.021 grdn:0.508 lr:1.0e-04 updt_s:0.147 data_s:0.004 +INFO 2025-04-02 19:55:59 ts/train.py:254 step:79K smpl:632K ep:2K epch:31.60 loss:0.021 grdn:0.507 lr:1.0e-04 updt_s:0.147 data_s:0.000 +INFO 2025-04-02 19:58:27 ts/train.py:254 step:80K smpl:640K ep:2K epch:32.00 loss:0.021 grdn:0.495 lr:1.0e-04 updt_s:0.147 data_s:0.000 +INFO 2025-04-02 19:58:27 ts/train.py:263 Checkpoint policy after step 80000 +INFO 2025-04-02 20:01:00 ts/train.py:254 step:81K smpl:648K ep:2K epch:32.40 loss:0.021 grdn:0.503 lr:1.0e-04 updt_s:0.147 data_s:0.005 +INFO 2025-04-02 20:03:29 ts/train.py:254 step:82K smpl:656K ep:2K epch:32.80 loss:0.021 grdn:0.502 lr:1.0e-04 updt_s:0.148 data_s:0.000 +INFO 2025-04-02 20:06:01 ts/train.py:254 step:83K smpl:664K ep:2K epch:33.20 loss:0.020 grdn:0.502 lr:1.0e-04 updt_s:0.148 data_s:0.004 +INFO 2025-04-02 20:08:29 ts/train.py:254 step:84K smpl:672K ep:2K epch:33.60 loss:0.020 grdn:0.497 lr:1.0e-04 updt_s:0.147 data_s:0.000 +INFO 2025-04-02 20:10:58 ts/train.py:254 step:85K smpl:680K ep:2K epch:34.00 loss:0.020 grdn:0.494 lr:1.0e-04 updt_s:0.148 data_s:0.000 +INFO 2025-04-02 20:13:30 ts/train.py:254 step:86K smpl:688K ep:2K epch:34.40 loss:0.020 grdn:0.500 lr:1.0e-04 updt_s:0.148 data_s:0.004 +INFO 2025-04-02 20:15:58 ts/train.py:254 step:87K smpl:696K ep:2K epch:34.80 loss:0.020 grdn:0.499 lr:1.0e-04 updt_s:0.147 data_s:0.000 +INFO 2025-04-02 20:18:31 ts/train.py:254 step:88K smpl:704K ep:2K epch:35.20 loss:0.020 grdn:0.500 lr:1.0e-04 updt_s:0.147 data_s:0.004 +INFO 2025-04-02 20:21:00 ts/train.py:254 step:89K smpl:712K ep:2K epch:35.60 loss:0.020 grdn:0.490 lr:1.0e-04 updt_s:0.148 data_s:0.000 +INFO 2025-04-02 20:23:28 ts/train.py:254 step:90K smpl:720K ep:2K epch:36.00 loss:0.020 grdn:0.495 lr:1.0e-04 updt_s:0.147 data_s:0.000 +INFO 2025-04-02 20:23:28 ts/train.py:263 Checkpoint policy after step 90000 +INFO 2025-04-02 20:26:02 ts/train.py:254 step:91K smpl:728K ep:2K epch:36.40 loss:0.019 grdn:0.484 lr:1.0e-04 updt_s:0.148 data_s:0.005 +INFO 2025-04-02 20:28:32 ts/train.py:254 step:92K smpl:736K ep:2K epch:36.80 loss:0.019 grdn:0.483 lr:1.0e-04 updt_s:0.148 data_s:0.000 +INFO 2025-04-02 20:31:05 ts/train.py:254 step:93K smpl:744K ep:2K epch:37.20 loss:0.019 grdn:0.487 lr:1.0e-04 updt_s:0.148 data_s:0.005 +INFO 2025-04-02 20:33:35 ts/train.py:254 step:94K smpl:752K ep:2K epch:37.60 loss:0.019 grdn:0.487 lr:1.0e-04 updt_s:0.148 data_s:0.000 +INFO 2025-04-02 20:36:03 ts/train.py:254 step:95K smpl:760K ep:2K epch:38.00 loss:0.019 grdn:0.491 lr:1.0e-04 updt_s:0.148 data_s:0.000 +INFO 2025-04-02 20:38:37 ts/train.py:254 step:96K smpl:768K ep:2K epch:38.40 loss:0.019 grdn:0.482 lr:1.0e-04 updt_s:0.148 data_s:0.004 +INFO 2025-04-02 20:41:06 ts/train.py:254 step:97K smpl:776K ep:2K epch:38.80 loss:0.019 grdn:0.483 lr:1.0e-04 updt_s:0.148 data_s:0.000 +INFO 2025-04-02 20:43:40 ts/train.py:254 step:98K smpl:784K ep:2K epch:39.20 loss:0.019 grdn:0.483 lr:1.0e-04 updt_s:0.148 data_s:0.004 +INFO 2025-04-02 20:46:09 ts/train.py:254 step:99K smpl:792K ep:2K epch:39.60 loss:0.019 grdn:0.477 lr:1.0e-04 updt_s:0.148 data_s:0.000 +INFO 2025-04-02 20:48:38 ts/train.py:254 step:100K smpl:800K ep:2K epch:40.00 loss:0.019 grdn:0.485 lr:1.0e-04 updt_s:0.149 data_s:0.000 +INFO 2025-04-02 20:48:38 ts/train.py:263 Checkpoint policy after step 100000 +INFO 2025-04-02 20:48:39 ts/train.py:272 Eval policy at step 100000 +Stepping through eval batches: 100%|████████████████| 1/1 [29:09<00:00, 1749.63s/it, running_success_rate=58.0%] +INFO 2025-04-02 21:17:50 ts/train.py:297 step:100K smpl:800K ep:2K epch:40.00 ∑rwrd:244.680 success:58.0 eval_s:1750.801 +WARNING 2025-04-02 21:17:50 db_utils.py:119 WandB logging of key "per_episode" was ignored as its type is not handled by this wrapper. +WARNING 2025-04-02 21:17:50 db_utils.py:119 WandB logging of key "aggregated" was ignored as its type is not handled by this wrapper. +WARNING 2025-04-02 21:17:50 db_utils.py:119 WandB logging of key "video_paths" was ignored as its type is not handled by this wrapper. +INFO 2025-04-02 21:17:50 ts/train.py:305 End of training diff --git a/train/pusht_aloha_transfer_cube/wandb/run-20250402_164121-dr64qj8o/files/requirements.txt b/train/pusht_aloha_transfer_cube/wandb/run-20250402_164121-dr64qj8o/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fd860d1b7cfe5594daf2f61554ca15a65bc0ae61 --- /dev/null +++ b/train/pusht_aloha_transfer_cube/wandb/run-20250402_164121-dr64qj8o/files/requirements.txt @@ -0,0 +1,178 @@ +setuptools==59.6.0 +Pygments==2.19.1 +jedi==0.19.2 +decorator==5.2.1 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