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eval_act_grab-test-2

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Re-upload all files from training folder

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  1. .gitattributes +1 -0
  2. wandb/debug-internal.log +15 -0
  3. wandb/debug.log +23 -0
  4. wandb/run-20250615_011116-h5jstcmg/files/output.log +245 -0
  5. wandb/run-20250615_011116-h5jstcmg/logs/debug-core.log +14 -0
  6. wandb/run-20250615_011116-h5jstcmg/logs/debug-internal.log +15 -0
  7. wandb/run-20250615_011116-h5jstcmg/logs/debug.log +23 -0
  8. wandb/run-20250615_011116-h5jstcmg/run-h5jstcmg.wandb +3 -0
.gitattributes CHANGED
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+ INFO 2025-06-15 01:11:17 db_utils.py:103 Track this run --> https://wandb.ai/combatroad-keimyung-university/lerobot/runs/h5jstcmg
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+ INFO 2025-06-15 01:11:17 ts/train.py:127 Creating dataset
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+ tasks.jsonl: 100% 43.0/43.0 [00:00<00:00, 259kB/s]
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+ info.json: 100% 3.35k/3.35k [00:00<00:00, 13.4MB/s]
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+ episodes.jsonl: 100% 64.0/64.0 [00:00<00:00, 453kB/s]
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+ episodes_stats.jsonl: 100% 2.19k/2.19k [00:00<00:00, 11.3MB/s]
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+ Fetching 4 files: 100% 4/4 [00:00<00:00, 23.87it/s]s]
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+ README.md: 100% 3.87k/3.87k [00:00<00:00, 19.5MB/s]
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+ videos/chunk-000/observation.images.side(…): 100% 9.55M/9.55M [00:01<00:00, 7.93MB/s]
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+ Fetching 9 files: 100% 9/9 [00:01<00:00, 6.74it/s].17M/9.17M [00:00<00:00, 14.8MB/s]
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+ Generating train split: 425 examples [00:00, 66551.40 examples/s]:01<00:00, 7.96MB/s]
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+ INFO 2025-06-15 01:11:19 ts/train.py:138 Creating policy
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+ INFO 2025-06-15 01:11:20 ts/train.py:144 Creating optimizer and scheduler
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+ INFO 2025-06-15 01:11:20 ts/train.py:156 Output dir: outputs/train/eval_act_grab-test-2
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+ INFO 2025-06-15 01:11:20 ts/train.py:159 cfg.steps=40000 (40K)
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+ INFO 2025-06-15 01:11:20 ts/train.py:160 dataset.num_frames=425 (425)
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+ INFO 2025-06-15 01:11:20 ts/train.py:161 dataset.num_episodes=1
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+ INFO 2025-06-15 01:11:20 ts/train.py:162 num_learnable_params=51597190 (52M)
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+ INFO 2025-06-15 01:11:20 ts/train.py:163 num_total_params=51597238 (52M)
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+ INFO 2025-06-15 01:11:20 ts/train.py:202 Start offline training on a fixed dataset
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+ INFO 2025-06-15 01:12:18 ts/train.py:232 step:200 smpl:2K ep:4 epch:3.76 loss:5.636 grdn:146.118 lr:1.0e-05 updt_s:0.262 data_s:0.025
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+ INFO 2025-06-15 01:13:09 ts/train.py:232 step:400 smpl:3K ep:8 epch:7.53 loss:2.653 grdn:82.455 lr:1.0e-05 updt_s:0.235 data_s:0.023
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+ INFO 2025-06-15 01:14:01 ts/train.py:232 step:600 smpl:5K ep:11 epch:11.29 loss:2.280 grdn:75.804 lr:1.0e-05 updt_s:0.234 data_s:0.021
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+ INFO 2025-06-15 01:14:51 ts/train.py:232 step:800 smpl:6K ep:15 epch:15.06 loss:1.965 grdn:69.023 lr:1.0e-05 updt_s:0.236 data_s:0.016
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+ INFO 2025-06-15 01:15:43 ts/train.py:232 step:1K smpl:8K ep:19 epch:18.82 loss:1.713 grdn:66.089 lr:1.0e-05 updt_s:0.234 data_s:0.022
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+ INFO 2025-06-15 01:16:35 ts/train.py:232 step:1K smpl:10K ep:23 epch:22.59 loss:1.522 grdn:62.062 lr:1.0e-05 updt_s:0.235 data_s:0.025
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+ INFO 2025-06-15 01:17:26 ts/train.py:232 step:1K smpl:11K ep:26 epch:26.35 loss:1.362 grdn:59.223 lr:1.0e-05 updt_s:0.236 data_s:0.018
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+ INFO 2025-06-15 01:18:18 ts/train.py:232 step:2K smpl:13K ep:30 epch:30.12 loss:1.217 grdn:56.104 lr:1.0e-05 updt_s:0.234 data_s:0.022
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+ INFO 2025-06-15 01:19:09 ts/train.py:232 step:2K smpl:14K ep:34 epch:33.88 loss:1.082 grdn:52.909 lr:1.0e-05 updt_s:0.235 data_s:0.023
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+ INFO 2025-06-15 01:20:01 ts/train.py:232 step:2K smpl:16K ep:38 epch:37.65 loss:0.979 grdn:49.833 lr:1.0e-05 updt_s:0.233 data_s:0.022
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+ INFO 2025-06-15 01:20:01 ts/train.py:241 Checkpoint policy after step 2000
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+ INFO 2025-06-15 01:21:45 ts/train.py:232 step:2K smpl:19K ep:45 epch:45.18 loss:0.775 grdn:44.919 lr:1.0e-05 updt_s:0.236 data_s:0.022
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+ INFO 2025-06-15 01:22:37 ts/train.py:232 step:3K smpl:21K ep:49 epch:48.94 loss:0.697 grdn:42.161 lr:1.0e-05 updt_s:0.234 data_s:0.025
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+ INFO 2025-06-15 01:23:28 ts/train.py:232 step:3K smpl:22K ep:53 epch:52.71 loss:0.620 grdn:39.421 lr:1.0e-05 updt_s:0.236 data_s:0.017
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+ INFO 2025-06-15 01:24:20 ts/train.py:232 step:3K smpl:24K ep:56 epch:56.47 loss:0.549 grdn:37.159 lr:1.0e-05 updt_s:0.234 data_s:0.024
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+ INFO 2025-06-15 01:25:12 ts/train.py:232 step:3K smpl:26K ep:60 epch:60.24 loss:0.489 grdn:34.932 lr:1.0e-05 updt_s:0.235 data_s:0.022
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+ INFO 2025-06-15 01:27:47 ts/train.py:232 step:4K smpl:30K ep:72 epch:71.53 loss:0.355 grdn:29.736 lr:1.0e-05 updt_s:0.235 data_s:0.025
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+ INFO 2025-06-15 01:28:38 ts/train.py:241 Checkpoint policy after step 4000
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+ INFO 2025-06-15 01:30:23 ts/train.py:232 step:4K smpl:35K ep:83 epch:82.82 loss:0.268 grdn:25.509 lr:1.0e-05 updt_s:0.235 data_s:0.023
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+ INFO 2025-06-15 01:31:15 ts/train.py:232 step:5K smpl:37K ep:87 epch:86.59 loss:0.245 grdn:24.868 lr:1.0e-05 updt_s:0.234 data_s:0.025
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+ INFO 2025-06-15 02:11:44 ts/train.py:241 Checkpoint policy after step 14000
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+ INFO 2025-06-15 02:20:21 ts/train.py:241 Checkpoint policy after step 16000
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+ INFO 2025-06-15 02:28:59 ts/train.py:241 Checkpoint policy after step 18000
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+ INFO 2025-06-15 02:37:36 ts/train.py:241 Checkpoint policy after step 20000
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+ INFO 2025-06-15 02:54:52 ts/train.py:241 Checkpoint policy after step 24000
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+ INFO 2025-06-15 03:03:32 ts/train.py:241 Checkpoint policy after step 26000
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+ INFO 2025-06-15 03:11:18 ts/train.py:232 step:28K smpl:222K ep:523 epch:523.29 loss:0.037 grdn:6.109 lr:1.0e-05 updt_s:0.237 data_s:0.017
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+ INFO 2025-06-15 03:12:10 ts/train.py:241 Checkpoint policy after step 28000
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+ 2025-06-15 01:11:16,906 INFO MainThread:4025658 [wandb_init.py:init():1104] starting run threads in backend
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+ 2025-06-15 01:11:17,324 INFO MainThread:4025658 [wandb_run.py:_console_start():2573] atexit reg
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+ 2025-06-15 01:11:17,325 INFO MainThread:4025658 [wandb_run.py:_redirect():2421] redirect: wrap_raw
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+ 2025-06-15 01:11:17,325 INFO MainThread:4025658 [wandb_run.py:_redirect():2490] Wrapping output streams.
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+ 2025-06-15 01:11:17,325 INFO MainThread:4025658 [wandb_run.py:_redirect():2513] Redirects installed.
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+ 2025-06-15 01:11:17,326 INFO MainThread:4025658 [wandb_init.py:init():1150] run started, returning control to user process
23
+ 2025-06-15 04:03:59,296 INFO MsgRouterThr:4025658 [mailbox.py:close():129] [no run ID] Closing mailbox, abandoning 2 handles.
wandb/run-20250615_011116-h5jstcmg/run-h5jstcmg.wandb ADDED
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