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Browse files- .gitattributes +1 -0
- checkpoints/050000/pretrained_model/config.json +80 -0
- checkpoints/050000/pretrained_model/model.safetensors +3 -0
- checkpoints/050000/pretrained_model/train_config.json +186 -0
- checkpoints/050000/training_state/optimizer_param_groups.json +189 -0
- checkpoints/050000/training_state/optimizer_state.safetensors +3 -0
- checkpoints/050000/training_state/rng_state.safetensors +3 -0
- checkpoints/050000/training_state/training_step.json +3 -0
- checkpoints/100000/pretrained_model/config.json +80 -0
- checkpoints/100000/pretrained_model/model.safetensors +3 -0
- checkpoints/100000/pretrained_model/train_config.json +186 -0
- checkpoints/100000/training_state/optimizer_param_groups.json +189 -0
- checkpoints/100000/training_state/optimizer_state.safetensors +3 -0
- checkpoints/100000/training_state/rng_state.safetensors +3 -0
- checkpoints/100000/training_state/training_step.json +3 -0
- wandb/debug-internal.log +15 -0
- wandb/debug.log +23 -0
- wandb/run-20250529_003039-sam_fold_cloth_single/files/config.yaml +167 -0
- wandb/run-20250529_003039-sam_fold_cloth_single/files/output.log +798 -0
- wandb/run-20250529_003039-sam_fold_cloth_single/files/requirements.txt +682 -0
- wandb/run-20250529_003039-sam_fold_cloth_single/files/wandb-metadata.json +45 -0
- wandb/run-20250529_003039-sam_fold_cloth_single/files/wandb-summary.json +1 -0
- wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug-core.log +14 -0
- wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug-internal.log +15 -0
- wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug.log +23 -0
- wandb/run-20250529_003039-sam_fold_cloth_single/run-sam_fold_cloth_single.wandb +3 -0
.gitattributes
CHANGED
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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wandb/run-20250529_003039-sam_fold_cloth_single/run-sam_fold_cloth_single.wandb filter=lfs diff=lfs merge=lfs -text
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checkpoints/050000/pretrained_model/config.json
ADDED
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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|
| 179 |
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|
| 180 |
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|
| 181 |
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|
| 184 |
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|
| 185 |
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|
| 186 |
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|
checkpoints/050000/training_state/optimizer_param_groups.json
ADDED
|
@@ -0,0 +1,189 @@
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checkpoints/050000/training_state/optimizer_state.safetensors
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size 412948796
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checkpoints/050000/training_state/rng_state.safetensors
ADDED
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@@ -0,0 +1,3 @@
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checkpoints/050000/training_state/training_step.json
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checkpoints/100000/pretrained_model/config.json
ADDED
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| 41 |
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|
| 42 |
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| 48 |
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| 49 |
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|
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
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|
| 66 |
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|
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|
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
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|
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|
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|
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|
| 79 |
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|
| 80 |
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checkpoints/100000/pretrained_model/model.safetensors
ADDED
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| 3 |
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size 206767408
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checkpoints/100000/pretrained_model/train_config.json
ADDED
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| 1 |
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|
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| 149 |
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|
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|
| 160 |
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|
| 161 |
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|
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|
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|
| 185 |
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|
| 186 |
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}
|
checkpoints/100000/training_state/optimizer_param_groups.json
ADDED
|
@@ -0,0 +1,189 @@
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
|
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|
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|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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checkpoints/100000/training_state/optimizer_state.safetensors
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checkpoints/100000/training_state/rng_state.safetensors
ADDED
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checkpoints/100000/training_state/training_step.json
ADDED
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|
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{
|
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"step": 100000
|
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|
wandb/debug-internal.log
ADDED
|
@@ -0,0 +1,15 @@
|
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{"time":"2025-05-29T00:30:39.113350824Z","level":"INFO","msg":"stream: starting","core version":"0.19.11","symlink path":"outputs/train/sam_fold_cloth_single/wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug-core.log"}
|
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{"time":"2025-05-29T00:30:39.438587436Z","level":"INFO","msg":"created new stream","id":"sam_fold_cloth_single"}
|
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{"time":"2025-05-29T00:30:39.438634484Z","level":"INFO","msg":"stream: started","id":"sam_fold_cloth_single"}
|
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{"time":"2025-05-29T00:30:39.43868417Z","level":"INFO","msg":"writer: Do: started","stream_id":"sam_fold_cloth_single"}
|
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{"time":"2025-05-29T00:30:39.43869285Z","level":"INFO","msg":"sender: started","stream_id":"sam_fold_cloth_single"}
|
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{"time":"2025-05-29T00:30:39.438753599Z","level":"INFO","msg":"handler: started","stream_id":"sam_fold_cloth_single"}
|
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{"time":"2025-05-29T00:30:39.706088017Z","level":"INFO","msg":"Starting system monitor"}
|
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{"time":"2025-05-29T14:22:51.326120472Z","level":"INFO","msg":"stream: closing","id":"sam_fold_cloth_single"}
|
| 9 |
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{"time":"2025-05-29T14:22:51.326158611Z","level":"INFO","msg":"Stopping system monitor"}
|
| 10 |
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|
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|
| 12 |
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{"time":"2025-05-29T14:23:06.143936063Z","level":"INFO","msg":"handler: closed","stream_id":"sam_fold_cloth_single"}
|
| 13 |
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|
| 14 |
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{"time":"2025-05-29T14:23:06.144016547Z","level":"INFO","msg":"sender: closed","stream_id":"sam_fold_cloth_single"}
|
| 15 |
+
{"time":"2025-05-29T14:23:06.144063686Z","level":"INFO","msg":"stream: closed","id":"sam_fold_cloth_single"}
|
wandb/debug.log
ADDED
|
@@ -0,0 +1,23 @@
|
|
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|
|
|
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|
| 1 |
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2025-05-29 00:30:39,101 INFO MainThread:2917 [wandb_setup.py:_flush():70] Current SDK version is 0.19.11
|
| 2 |
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2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_setup.py:_flush():70] Configure stats pid to 2917
|
| 3 |
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2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_setup.py:_flush():70] Loading settings from /root/.config/wandb/settings
|
| 4 |
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2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_setup.py:_flush():70] Loading settings from /content/wandb/settings
|
| 5 |
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2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_setup.py:_flush():70] Loading settings from environment variables
|
| 6 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:setup_run_log_directory():724] Logging user logs to outputs/train/sam_fold_cloth_single/wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug.log
|
| 7 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:setup_run_log_directory():725] Logging internal logs to outputs/train/sam_fold_cloth_single/wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug-internal.log
|
| 8 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:init():852] calling init triggers
|
| 9 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:init():857] wandb.init called with sweep_config: {}
|
| 10 |
+
config: {'dataset': {'repo_id': 'girardijp/sam_fold_cloth_single', 'root': None, 'episodes': None, 'image_transforms': {'enable': False, 'max_num_transforms': 3, 'random_order': False, 'tfs': {'brightness': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'brightness': [0.8, 1.2]}}, 'contrast': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'contrast': [0.8, 1.2]}}, 'saturation': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'saturation': [0.5, 1.5]}}, 'hue': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'hue': [-0.05, 0.05]}}, 'sharpness': {'weight': 1.0, 'type': 'SharpnessJitter', 'kwargs': {'sharpness': [0.5, 1.5]}}}}, 'revision': None, 'use_imagenet_stats': True, 'video_backend': 'torchcodec'}, 'env': None, 'policy': {'type': 'act', 'n_obs_steps': 1, 'normalization_mapping': {'VISUAL': <NormalizationMode.MEAN_STD: 'MEAN_STD'>, 'STATE': <NormalizationMode.MEAN_STD: 'MEAN_STD'>, 'ACTION': <NormalizationMode.MEAN_STD: 'MEAN_STD'>}, 'input_features': {}, 'output_features': {}, 'device': 'cuda', 'use_amp': False, 'chunk_size': 100, 'n_action_steps': 100, 'vision_backbone': 'resnet18', 'pretrained_backbone_weights': 'ResNet18_Weights.IMAGENET1K_V1', 'replace_final_stride_with_dilation': False, 'pre_norm': False, 'dim_model': 512, 'n_heads': 8, 'dim_feedforward': 3200, 'feedforward_activation': 'relu', 'n_encoder_layers': 4, 'n_decoder_layers': 1, 'use_vae': True, 'latent_dim': 32, 'n_vae_encoder_layers': 4, 'temporal_ensemble_coeff': None, 'dropout': 0.1, 'kl_weight': 10.0, 'optimizer_lr': 1e-05, 'optimizer_weight_decay': 0.0001, 'optimizer_lr_backbone': 1e-05}, 'output_dir': 'outputs/train/sam_fold_cloth_single', 'job_name': 'sam_fold_cloth_single', 'resume': False, 'seed': 1000, 'num_workers': 4, 'batch_size': 8, 'steps': 100000, 'eval_freq': 20000, 'log_freq': 200, 'save_checkpoint': True, 'save_freq': 50000, 'use_policy_training_preset': True, 'optimizer': {'type': 'adamw', 'lr': 1e-05, 'weight_decay': 0.0001, 'grad_clip_norm': 10.0, 'betas': [0.9, 0.999], 'eps': 1e-08}, 'scheduler': None, 'eval': {'n_episodes': 50, 'batch_size': 50, 'use_async_envs': False}, 'wandb': {'enable': True, 'disable_artifact': False, 'project': 'lerobot', 'entity': None, 'notes': None, 'run_id': 'sam_fold_cloth_single', 'mode': None}, '_wandb': {}}
|
| 11 |
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2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:init():893] starting backend
|
| 12 |
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2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:init():897] sending inform_init request
|
| 13 |
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2025-05-29 00:30:39,110 INFO MainThread:2917 [backend.py:_multiprocessing_setup():101] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
|
| 14 |
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2025-05-29 00:30:39,110 INFO MainThread:2917 [wandb_init.py:init():907] backend started and connected
|
| 15 |
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2025-05-29 00:30:39,112 INFO MainThread:2917 [wandb_init.py:init():1005] updated telemetry
|
| 16 |
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2025-05-29 00:30:39,113 INFO MainThread:2917 [wandb_init.py:init():1029] communicating run to backend with 90.0 second timeout
|
| 17 |
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2025-05-29 00:30:39,703 INFO MainThread:2917 [wandb_init.py:init():1104] starting run threads in backend
|
| 18 |
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2025-05-29 00:30:40,104 INFO MainThread:2917 [wandb_run.py:_console_start():2573] atexit reg
|
| 19 |
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2025-05-29 00:30:40,104 INFO MainThread:2917 [wandb_run.py:_redirect():2421] redirect: wrap_raw
|
| 20 |
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2025-05-29 00:30:40,104 INFO MainThread:2917 [wandb_run.py:_redirect():2490] Wrapping output streams.
|
| 21 |
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2025-05-29 00:30:40,104 INFO MainThread:2917 [wandb_run.py:_redirect():2513] Redirects installed.
|
| 22 |
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2025-05-29 00:30:40,109 INFO MainThread:2917 [wandb_init.py:init():1150] run started, returning control to user process
|
| 23 |
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2025-05-29 14:22:51,325 INFO MsgRouterThr:2917 [mailbox.py:close():129] [no run ID] Closing mailbox, abandoning 2 handles.
|
wandb/run-20250529_003039-sam_fold_cloth_single/files/config.yaml
ADDED
|
@@ -0,0 +1,167 @@
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
|
| 1 |
+
_wandb:
|
| 2 |
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value:
|
| 3 |
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cli_version: 0.19.11
|
| 4 |
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m: []
|
| 5 |
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python_version: 3.11.12
|
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t:
|
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|
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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| 14 |
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|
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|
| 16 |
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|
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|
| 18 |
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|
| 19 |
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|
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|
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|
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|
| 23 |
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|
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|
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|
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|
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|
| 28 |
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|
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"4": 3.11.12
|
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|
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|
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"10":
|
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|
| 35 |
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"12": 0.19.11
|
| 36 |
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"13": linux-x86_64
|
| 37 |
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batch_size:
|
| 38 |
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value: 8
|
| 39 |
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dataset:
|
| 40 |
+
value:
|
| 41 |
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episodes: null
|
| 42 |
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image_transforms:
|
| 43 |
+
enable: false
|
| 44 |
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max_num_transforms: 3
|
| 45 |
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random_order: false
|
| 46 |
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tfs:
|
| 47 |
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brightness:
|
| 48 |
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kwargs:
|
| 49 |
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brightness:
|
| 50 |
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- 0.8
|
| 51 |
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- 1.2
|
| 52 |
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type: ColorJitter
|
| 53 |
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weight: 1
|
| 54 |
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contrast:
|
| 55 |
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kwargs:
|
| 56 |
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contrast:
|
| 57 |
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- 0.8
|
| 58 |
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- 1.2
|
| 59 |
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type: ColorJitter
|
| 60 |
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weight: 1
|
| 61 |
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hue:
|
| 62 |
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kwargs:
|
| 63 |
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hue:
|
| 64 |
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- -0.05
|
| 65 |
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- 0.05
|
| 66 |
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type: ColorJitter
|
| 67 |
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weight: 1
|
| 68 |
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saturation:
|
| 69 |
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kwargs:
|
| 70 |
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saturation:
|
| 71 |
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- 0.5
|
| 72 |
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- 1.5
|
| 73 |
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type: ColorJitter
|
| 74 |
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weight: 1
|
| 75 |
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sharpness:
|
| 76 |
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kwargs:
|
| 77 |
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sharpness:
|
| 78 |
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- 0.5
|
| 79 |
+
- 1.5
|
| 80 |
+
type: SharpnessJitter
|
| 81 |
+
weight: 1
|
| 82 |
+
repo_id: girardijp/sam_fold_cloth_single
|
| 83 |
+
revision: null
|
| 84 |
+
root: null
|
| 85 |
+
use_imagenet_stats: true
|
| 86 |
+
video_backend: torchcodec
|
| 87 |
+
env:
|
| 88 |
+
value: null
|
| 89 |
+
eval:
|
| 90 |
+
value:
|
| 91 |
+
batch_size: 50
|
| 92 |
+
n_episodes: 50
|
| 93 |
+
use_async_envs: false
|
| 94 |
+
eval_freq:
|
| 95 |
+
value: 20000
|
| 96 |
+
job_name:
|
| 97 |
+
value: sam_fold_cloth_single
|
| 98 |
+
log_freq:
|
| 99 |
+
value: 200
|
| 100 |
+
num_workers:
|
| 101 |
+
value: 4
|
| 102 |
+
optimizer:
|
| 103 |
+
value:
|
| 104 |
+
betas:
|
| 105 |
+
- 0.9
|
| 106 |
+
- 0.999
|
| 107 |
+
eps: 1e-08
|
| 108 |
+
grad_clip_norm: 10
|
| 109 |
+
lr: 1e-05
|
| 110 |
+
type: adamw
|
| 111 |
+
weight_decay: 0.0001
|
| 112 |
+
output_dir:
|
| 113 |
+
value: outputs/train/sam_fold_cloth_single
|
| 114 |
+
policy:
|
| 115 |
+
value:
|
| 116 |
+
chunk_size: 100
|
| 117 |
+
device: cuda
|
| 118 |
+
dim_feedforward: 3200
|
| 119 |
+
dim_model: 512
|
| 120 |
+
dropout: 0.1
|
| 121 |
+
feedforward_activation: relu
|
| 122 |
+
kl_weight: 10
|
| 123 |
+
latent_dim: 32
|
| 124 |
+
n_action_steps: 100
|
| 125 |
+
n_decoder_layers: 1
|
| 126 |
+
n_encoder_layers: 4
|
| 127 |
+
n_heads: 8
|
| 128 |
+
n_obs_steps: 1
|
| 129 |
+
n_vae_encoder_layers: 4
|
| 130 |
+
normalization_mapping:
|
| 131 |
+
ACTION: MEAN_STD
|
| 132 |
+
STATE: MEAN_STD
|
| 133 |
+
VISUAL: MEAN_STD
|
| 134 |
+
optimizer_lr: 1e-05
|
| 135 |
+
optimizer_lr_backbone: 1e-05
|
| 136 |
+
optimizer_weight_decay: 0.0001
|
| 137 |
+
pre_norm: false
|
| 138 |
+
pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
|
| 139 |
+
replace_final_stride_with_dilation: false
|
| 140 |
+
temporal_ensemble_coeff: null
|
| 141 |
+
type: act
|
| 142 |
+
use_amp: false
|
| 143 |
+
use_vae: true
|
| 144 |
+
vision_backbone: resnet18
|
| 145 |
+
resume:
|
| 146 |
+
value: false
|
| 147 |
+
save_checkpoint:
|
| 148 |
+
value: true
|
| 149 |
+
save_freq:
|
| 150 |
+
value: 50000
|
| 151 |
+
scheduler:
|
| 152 |
+
value: null
|
| 153 |
+
seed:
|
| 154 |
+
value: 1000
|
| 155 |
+
steps:
|
| 156 |
+
value: 100000
|
| 157 |
+
use_policy_training_preset:
|
| 158 |
+
value: true
|
| 159 |
+
wandb:
|
| 160 |
+
value:
|
| 161 |
+
disable_artifact: false
|
| 162 |
+
enable: true
|
| 163 |
+
entity: null
|
| 164 |
+
mode: null
|
| 165 |
+
notes: null
|
| 166 |
+
project: lerobot
|
| 167 |
+
run_id: sam_fold_cloth_single
|
wandb/run-20250529_003039-sam_fold_cloth_single/files/output.log
ADDED
|
@@ -0,0 +1,798 @@
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[1m[34mLogs will be synced with wandb.[0m
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INFO 2025-05-29 00:30:40 ndb_utils.py:96 Track this run --> [1m[33mhttps://wandb.ai/girardijp3-universidade-federal-de-santa-catarina/lerobot/runs/sam_fold_cloth_single[0m
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INFO 2025-05-29 01:05:56 ts/train.py:232 step:4K smpl:34K ep:32 epch:0.60 loss:0.571 grdn:29.255 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 01:07:36 ts/train.py:232 step:4K smpl:35K ep:34 epch:0.63 loss:0.541 grdn:28.306 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 01:09:16 ts/train.py:232 step:5K smpl:37K ep:35 epch:0.65 loss:0.504 grdn:27.019 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:10:55 ts/train.py:232 step:5K smpl:38K ep:37 epch:0.68 loss:0.479 grdn:25.650 lr:1.0e-05 updt_s:0.497 data_s:0.000
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INFO 2025-05-29 01:12:35 ts/train.py:232 step:5K smpl:40K ep:38 epch:0.71 loss:0.454 grdn:25.043 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 01:14:15 ts/train.py:232 step:5K smpl:42K ep:40 epch:0.74 loss:0.429 grdn:23.884 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 01:15:55 ts/train.py:232 step:5K smpl:43K ep:41 epch:0.77 loss:0.412 grdn:23.125 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:17:35 ts/train.py:232 step:6K smpl:45K ep:43 epch:0.80 loss:0.401 grdn:22.758 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 01:19:14 ts/train.py:232 step:6K smpl:46K ep:45 epch:0.82 loss:0.380 grdn:21.620 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 01:20:54 ts/train.py:232 step:6K smpl:48K ep:46 epch:0.85 loss:0.373 grdn:21.753 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 01:22:33 ts/train.py:232 step:6K smpl:50K ep:48 epch:0.88 loss:0.357 grdn:20.648 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 01:24:13 ts/train.py:232 step:6K smpl:51K ep:49 epch:0.91 loss:0.349 grdn:20.780 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 01:25:52 ts/train.py:232 step:7K smpl:53K ep:51 epch:0.94 loss:0.335 grdn:19.939 lr:1.0e-05 updt_s:0.495 data_s:0.000
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INFO 2025-05-29 01:27:32 ts/train.py:232 step:7K smpl:54K ep:52 epch:0.97 loss:0.328 grdn:19.550 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 01:29:11 ts/train.py:232 step:7K smpl:56K ep:54 epch:1.00 loss:0.319 grdn:19.212 lr:1.0e-05 updt_s:0.497 data_s:0.000
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INFO 2025-05-29 01:30:52 ts/train.py:232 step:7K smpl:58K ep:55 epch:1.02 loss:0.306 grdn:18.486 lr:1.0e-05 updt_s:0.499 data_s:0.005
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INFO 2025-05-29 01:32:33 ts/train.py:232 step:7K smpl:59K ep:57 epch:1.05 loss:0.298 grdn:18.248 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:34:13 ts/train.py:232 step:8K smpl:61K ep:58 epch:1.08 loss:0.292 grdn:17.789 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:35:53 ts/train.py:232 step:8K smpl:62K ep:60 epch:1.11 loss:0.287 grdn:17.569 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 01:37:33 ts/train.py:232 step:8K smpl:64K ep:61 epch:1.14 loss:0.282 grdn:17.374 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:39:13 ts/train.py:232 step:8K smpl:66K ep:63 epch:1.17 loss:0.273 grdn:16.943 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:40:53 ts/train.py:232 step:8K smpl:67K ep:64 epch:1.19 loss:0.271 grdn:16.520 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:42:33 ts/train.py:232 step:9K smpl:69K ep:66 epch:1.22 loss:0.262 grdn:16.453 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 01:44:13 ts/train.py:232 step:9K smpl:70K ep:68 epch:1.25 loss:0.261 grdn:16.616 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 01:45:53 ts/train.py:232 step:9K smpl:72K ep:69 epch:1.28 loss:0.254 grdn:15.842 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:47:33 ts/train.py:232 step:9K smpl:74K ep:71 epch:1.31 loss:0.254 grdn:15.956 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:49:13 ts/train.py:232 step:9K smpl:75K ep:72 epch:1.34 loss:0.247 grdn:15.508 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:50:53 ts/train.py:232 step:10K smpl:77K ep:74 epch:1.36 loss:0.241 grdn:15.129 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 01:52:33 ts/train.py:232 step:10K smpl:78K ep:75 epch:1.39 loss:0.241 grdn:15.277 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:54:13 ts/train.py:232 step:10K smpl:80K ep:77 epch:1.42 loss:0.231 grdn:14.276 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 01:55:53 ts/train.py:232 step:10K smpl:82K ep:78 epch:1.45 loss:0.232 grdn:14.543 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:57:33 ts/train.py:232 step:10K smpl:83K ep:80 epch:1.48 loss:0.229 grdn:14.456 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 01:59:13 ts/train.py:232 step:11K smpl:85K ep:81 epch:1.51 loss:0.224 grdn:14.282 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 02:00:53 ts/train.py:232 step:11K smpl:86K ep:83 epch:1.54 loss:0.221 grdn:13.935 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 02:02:33 ts/train.py:232 step:11K smpl:88K ep:84 epch:1.56 loss:0.216 grdn:13.963 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 02:04:13 ts/train.py:232 step:11K smpl:90K ep:86 epch:1.59 loss:0.217 grdn:14.182 lr:1.0e-05 updt_s:0.497 data_s:0.000
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INFO 2025-05-29 02:05:52 ts/train.py:232 step:11K smpl:91K ep:88 epch:1.62 loss:0.213 grdn:13.677 lr:1.0e-05 updt_s:0.497 data_s:0.000
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INFO 2025-05-29 02:07:32 ts/train.py:232 step:12K smpl:93K ep:89 epch:1.65 loss:0.211 grdn:13.386 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 02:09:12 ts/train.py:232 step:12K smpl:94K ep:91 epch:1.68 loss:0.205 grdn:13.258 lr:1.0e-05 updt_s:0.497 data_s:0.000
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INFO 2025-05-29 02:10:51 ts/train.py:232 step:12K smpl:96K ep:92 epch:1.71 loss:0.207 grdn:13.162 lr:1.0e-05 updt_s:0.497 data_s:0.000
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INFO 2025-05-29 02:12:31 ts/train.py:232 step:12K smpl:98K ep:94 epch:1.73 loss:0.204 grdn:13.064 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 02:14:11 ts/train.py:232 step:12K smpl:99K ep:95 epch:1.76 loss:0.202 grdn:12.943 lr:1.0e-05 updt_s:0.495 data_s:0.000
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INFO 2025-05-29 02:15:50 ts/train.py:232 step:13K smpl:101K ep:97 epch:1.79 loss:0.200 grdn:12.630 lr:1.0e-05 updt_s:0.495 data_s:0.000
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INFO 2025-05-29 02:17:29 ts/train.py:232 step:13K smpl:102K ep:98 epch:1.82 loss:0.203 grdn:12.579 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 02:19:09 ts/train.py:232 step:13K smpl:104K ep:100 epch:1.85 loss:0.199 grdn:12.659 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 02:20:48 ts/train.py:232 step:13K smpl:106K ep:101 epch:1.88 loss:0.193 grdn:12.417 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 02:22:28 ts/train.py:232 step:13K smpl:107K ep:103 epch:1.91 loss:0.193 grdn:11.882 lr:1.0e-05 updt_s:0.497 data_s:0.000
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INFO 2025-05-29 02:24:08 ts/train.py:232 step:14K smpl:109K ep:104 epch:1.93 loss:0.195 grdn:12.270 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 02:25:47 ts/train.py:232 step:14K smpl:110K ep:106 epch:1.96 loss:0.189 grdn:12.140 lr:1.0e-05 updt_s:0.496 data_s:0.000
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INFO 2025-05-29 02:27:27 ts/train.py:232 step:14K smpl:112K ep:107 epch:1.99 loss:0.187 grdn:12.116 lr:1.0e-05 updt_s:0.497 data_s:0.000
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INFO 2025-05-29 02:29:08 ts/train.py:232 step:14K smpl:114K ep:109 epch:2.02 loss:0.187 grdn:11.768 lr:1.0e-05 updt_s:0.497 data_s:0.005
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INFO 2025-05-29 02:30:48 ts/train.py:232 step:14K smpl:115K ep:111 epch:2.05 loss:0.185 grdn:11.644 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 02:32:28 ts/train.py:232 step:15K smpl:117K ep:112 epch:2.08 loss:0.181 grdn:11.467 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 02:34:08 ts/train.py:232 step:15K smpl:118K ep:114 epch:2.10 loss:0.179 grdn:11.343 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 02:35:47 ts/train.py:232 step:15K smpl:120K ep:115 epch:2.13 loss:0.178 grdn:11.293 lr:1.0e-05 updt_s:0.495 data_s:0.000
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INFO 2025-05-29 02:37:27 ts/train.py:232 step:15K smpl:122K ep:117 epch:2.16 loss:0.178 grdn:11.522 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 02:39:07 ts/train.py:232 step:15K smpl:123K ep:118 epch:2.19 loss:0.176 grdn:11.359 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 02:40:47 ts/train.py:232 step:16K smpl:125K ep:120 epch:2.22 loss:0.174 grdn:11.124 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 02:42:27 ts/train.py:232 step:16K smpl:126K ep:121 epch:2.25 loss:0.173 grdn:11.021 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 02:44:07 ts/train.py:232 step:16K smpl:128K ep:123 epch:2.27 loss:0.171 grdn:10.935 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 02:45:47 ts/train.py:232 step:16K smpl:130K ep:124 epch:2.30 loss:0.172 grdn:10.908 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 02:47:27 ts/train.py:232 step:16K smpl:131K ep:126 epch:2.33 loss:0.170 grdn:10.661 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 02:49:07 ts/train.py:232 step:17K smpl:133K ep:127 epch:2.36 loss:0.169 grdn:10.837 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 02:50:47 ts/train.py:232 step:17K smpl:134K ep:129 epch:2.39 loss:0.165 grdn:10.771 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 02:52:27 ts/train.py:232 step:17K smpl:136K ep:131 epch:2.42 loss:0.167 grdn:10.659 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 02:54:07 ts/train.py:232 step:17K smpl:138K ep:132 epch:2.45 loss:0.165 grdn:10.467 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 02:55:47 ts/train.py:232 step:17K smpl:139K ep:134 epch:2.47 loss:0.165 grdn:10.526 lr:1.0e-05 updt_s:0.495 data_s:0.000
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INFO 2025-05-29 02:57:26 ts/train.py:232 step:18K smpl:141K ep:135 epch:2.50 loss:0.165 grdn:10.292 lr:1.0e-05 updt_s:0.495 data_s:0.000
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INFO 2025-05-29 02:59:06 ts/train.py:232 step:18K smpl:142K ep:137 epch:2.53 loss:0.162 grdn:10.317 lr:1.0e-05 updt_s:0.497 data_s:0.000
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INFO 2025-05-29 03:00:46 ts/train.py:232 step:18K smpl:144K ep:138 epch:2.56 loss:0.159 grdn:9.844 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:02:26 ts/train.py:232 step:18K smpl:146K ep:140 epch:2.59 loss:0.161 grdn:10.118 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:04:06 ts/train.py:232 step:18K smpl:147K ep:141 epch:2.62 loss:0.161 grdn:10.317 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:05:46 ts/train.py:232 step:19K smpl:149K ep:143 epch:2.64 loss:0.161 grdn:9.823 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:07:26 ts/train.py:232 step:19K smpl:150K ep:144 epch:2.67 loss:0.156 grdn:9.671 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:09:06 ts/train.py:232 step:19K smpl:152K ep:146 epch:2.70 loss:0.158 grdn:10.032 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:10:46 ts/train.py:232 step:19K smpl:154K ep:147 epch:2.73 loss:0.156 grdn:9.691 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:12:26 ts/train.py:232 step:19K smpl:155K ep:149 epch:2.76 loss:0.153 grdn:9.641 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:14:06 ts/train.py:232 step:20K smpl:157K ep:150 epch:2.79 loss:0.150 grdn:9.391 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:15:46 ts/train.py:232 step:20K smpl:158K ep:152 epch:2.82 loss:0.153 grdn:9.553 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:17:26 ts/train.py:232 step:20K smpl:160K ep:154 epch:2.84 loss:0.152 grdn:9.539 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:19:06 ts/train.py:232 step:20K smpl:162K ep:155 epch:2.87 loss:0.152 grdn:9.508 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:20:46 ts/train.py:232 step:20K smpl:163K ep:157 epch:2.90 loss:0.149 grdn:9.295 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:22:26 ts/train.py:232 step:21K smpl:165K ep:158 epch:2.93 loss:0.149 grdn:9.172 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:24:06 ts/train.py:232 step:21K smpl:166K ep:160 epch:2.96 loss:0.149 grdn:9.033 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:25:46 ts/train.py:232 step:21K smpl:168K ep:161 epch:2.99 loss:0.149 grdn:9.372 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:27:27 ts/train.py:232 step:21K smpl:170K ep:163 epch:3.01 loss:0.147 grdn:9.131 lr:1.0e-05 updt_s:0.498 data_s:0.006
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INFO 2025-05-29 03:29:07 ts/train.py:232 step:21K smpl:171K ep:164 epch:3.04 loss:0.148 grdn:9.019 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:30:47 ts/train.py:232 step:22K smpl:173K ep:166 epch:3.07 loss:0.142 grdn:8.831 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:32:27 ts/train.py:232 step:22K smpl:174K ep:167 epch:3.10 loss:0.145 grdn:8.792 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:34:07 ts/train.py:232 step:22K smpl:176K ep:169 epch:3.13 loss:0.142 grdn:8.426 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:35:47 ts/train.py:232 step:22K smpl:178K ep:170 epch:3.16 loss:0.143 grdn:8.778 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:37:27 ts/train.py:232 step:22K smpl:179K ep:172 epch:3.18 loss:0.144 grdn:9.442 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:39:07 ts/train.py:232 step:23K smpl:181K ep:174 epch:3.21 loss:0.143 grdn:8.351 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:40:47 ts/train.py:232 step:23K smpl:182K ep:175 epch:3.24 loss:0.142 grdn:8.520 lr:1.0e-05 updt_s:0.499 data_s:0.000
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INFO 2025-05-29 03:42:27 ts/train.py:232 step:23K smpl:184K ep:177 epch:3.27 loss:0.141 grdn:8.523 lr:1.0e-05 updt_s:0.498 data_s:0.000
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INFO 2025-05-29 03:44:07 ts/train.py:232 step:23K smpl:186K ep:178 epch:3.30 loss:0.138 grdn:8.527 lr:1.0e-05 updt_s:0.499 data_s:0.000
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| 412 |
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INFO 2025-05-29 03:45:47 ts/train.py:232 step:23K smpl:187K ep:180 epch:3.33 loss:0.139 grdn:8.502 lr:1.0e-05 updt_s:0.499 data_s:0.000
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| 413 |
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INFO 2025-05-29 03:47:27 ts/train.py:232 step:24K smpl:189K ep:181 epch:3.36 loss:0.139 grdn:8.474 lr:1.0e-05 updt_s:0.498 data_s:0.000
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| 414 |
+
INFO 2025-05-29 03:49:07 ts/train.py:232 step:24K smpl:190K ep:183 epch:3.38 loss:0.137 grdn:8.254 lr:1.0e-05 updt_s:0.499 data_s:0.000
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| 415 |
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INFO 2025-05-29 03:50:47 ts/train.py:232 step:24K smpl:192K ep:184 epch:3.41 loss:0.136 grdn:8.299 lr:1.0e-05 updt_s:0.498 data_s:0.000
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| 416 |
+
INFO 2025-05-29 03:52:27 ts/train.py:232 step:24K smpl:194K ep:186 epch:3.44 loss:0.138 grdn:8.259 lr:1.0e-05 updt_s:0.499 data_s:0.000
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| 417 |
+
INFO 2025-05-29 03:54:07 ts/train.py:232 step:24K smpl:195K ep:187 epch:3.47 loss:0.139 grdn:8.291 lr:1.0e-05 updt_s:0.497 data_s:0.000
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| 418 |
+
INFO 2025-05-29 03:55:47 ts/train.py:232 step:25K smpl:197K ep:189 epch:3.50 loss:0.135 grdn:8.071 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 419 |
+
INFO 2025-05-29 03:57:26 ts/train.py:232 step:25K smpl:198K ep:190 epch:3.53 loss:0.137 grdn:8.424 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 420 |
+
INFO 2025-05-29 03:59:06 ts/train.py:232 step:25K smpl:200K ep:192 epch:3.55 loss:0.136 grdn:8.187 lr:1.0e-05 updt_s:0.496 data_s:0.000
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| 421 |
+
INFO 2025-05-29 04:00:46 ts/train.py:232 step:25K smpl:202K ep:193 epch:3.58 loss:0.134 grdn:8.119 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 422 |
+
INFO 2025-05-29 04:02:25 ts/train.py:232 step:25K smpl:203K ep:195 epch:3.61 loss:0.134 grdn:8.118 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 423 |
+
INFO 2025-05-29 04:04:05 ts/train.py:232 step:26K smpl:205K ep:197 epch:3.64 loss:0.131 grdn:8.163 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 424 |
+
INFO 2025-05-29 04:05:45 ts/train.py:232 step:26K smpl:206K ep:198 epch:3.67 loss:0.133 grdn:8.236 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 425 |
+
INFO 2025-05-29 04:07:24 ts/train.py:232 step:26K smpl:208K ep:200 epch:3.70 loss:0.134 grdn:7.830 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 426 |
+
INFO 2025-05-29 04:09:04 ts/train.py:232 step:26K smpl:210K ep:201 epch:3.73 loss:0.131 grdn:8.013 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 427 |
+
INFO 2025-05-29 04:10:43 ts/train.py:232 step:26K smpl:211K ep:203 epch:3.75 loss:0.131 grdn:7.958 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 428 |
+
INFO 2025-05-29 04:12:23 ts/train.py:232 step:27K smpl:213K ep:204 epch:3.78 loss:0.131 grdn:7.809 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 429 |
+
INFO 2025-05-29 04:14:03 ts/train.py:232 step:27K smpl:214K ep:206 epch:3.81 loss:0.132 grdn:7.934 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 430 |
+
INFO 2025-05-29 04:15:42 ts/train.py:232 step:27K smpl:216K ep:207 epch:3.84 loss:0.127 grdn:7.808 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 431 |
+
INFO 2025-05-29 04:17:22 ts/train.py:232 step:27K smpl:218K ep:209 epch:3.87 loss:0.131 grdn:7.644 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 432 |
+
INFO 2025-05-29 04:19:02 ts/train.py:232 step:27K smpl:219K ep:210 epch:3.90 loss:0.131 grdn:7.923 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 433 |
+
INFO 2025-05-29 04:20:41 ts/train.py:232 step:28K smpl:221K ep:212 epch:3.92 loss:0.126 grdn:7.465 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 434 |
+
INFO 2025-05-29 04:22:21 ts/train.py:232 step:28K smpl:222K ep:213 epch:3.95 loss:0.127 grdn:7.632 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 435 |
+
INFO 2025-05-29 04:24:00 ts/train.py:232 step:28K smpl:224K ep:215 epch:3.98 loss:0.128 grdn:7.807 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 436 |
+
INFO 2025-05-29 04:25:41 ts/train.py:232 step:28K smpl:226K ep:217 epch:4.01 loss:0.127 grdn:7.570 lr:1.0e-05 updt_s:0.496 data_s:0.006
|
| 437 |
+
INFO 2025-05-29 04:27:21 ts/train.py:232 step:28K smpl:227K ep:218 epch:4.04 loss:0.126 grdn:7.738 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 438 |
+
INFO 2025-05-29 04:29:01 ts/train.py:232 step:29K smpl:229K ep:220 epch:4.07 loss:0.123 grdn:7.526 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 439 |
+
INFO 2025-05-29 04:30:41 ts/train.py:232 step:29K smpl:230K ep:221 epch:4.09 loss:0.127 grdn:7.622 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 440 |
+
INFO 2025-05-29 04:32:21 ts/train.py:232 step:29K smpl:232K ep:223 epch:4.12 loss:0.124 grdn:7.454 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 441 |
+
INFO 2025-05-29 04:34:00 ts/train.py:232 step:29K smpl:234K ep:224 epch:4.15 loss:0.123 grdn:7.373 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 442 |
+
INFO 2025-05-29 04:35:40 ts/train.py:232 step:29K smpl:235K ep:226 epch:4.18 loss:0.124 grdn:7.260 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 443 |
+
INFO 2025-05-29 04:37:19 ts/train.py:232 step:30K smpl:237K ep:227 epch:4.21 loss:0.123 grdn:7.358 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 444 |
+
INFO 2025-05-29 04:38:58 ts/train.py:232 step:30K smpl:238K ep:229 epch:4.24 loss:0.122 grdn:7.311 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 445 |
+
INFO 2025-05-29 04:40:38 ts/train.py:232 step:30K smpl:240K ep:230 epch:4.27 loss:0.126 grdn:7.427 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 446 |
+
INFO 2025-05-29 04:42:18 ts/train.py:232 step:30K smpl:242K ep:232 epch:4.29 loss:0.123 grdn:7.065 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 447 |
+
INFO 2025-05-29 04:43:58 ts/train.py:232 step:30K smpl:243K ep:233 epch:4.32 loss:0.122 grdn:7.233 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 448 |
+
INFO 2025-05-29 04:45:38 ts/train.py:232 step:31K smpl:245K ep:235 epch:4.35 loss:0.122 grdn:7.301 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 449 |
+
INFO 2025-05-29 04:47:19 ts/train.py:232 step:31K smpl:246K ep:236 epch:4.38 loss:0.123 grdn:7.420 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 450 |
+
INFO 2025-05-29 04:48:59 ts/train.py:232 step:31K smpl:248K ep:238 epch:4.41 loss:0.121 grdn:7.302 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 451 |
+
INFO 2025-05-29 04:50:39 ts/train.py:232 step:31K smpl:250K ep:240 epch:4.44 loss:0.120 grdn:7.102 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 452 |
+
INFO 2025-05-29 04:52:19 ts/train.py:232 step:31K smpl:251K ep:241 epch:4.46 loss:0.122 grdn:7.391 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 453 |
+
INFO 2025-05-29 04:53:59 ts/train.py:232 step:32K smpl:253K ep:243 epch:4.49 loss:0.123 grdn:7.106 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 454 |
+
INFO 2025-05-29 04:55:39 ts/train.py:232 step:32K smpl:254K ep:244 epch:4.52 loss:0.116 grdn:6.813 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 455 |
+
INFO 2025-05-29 04:57:19 ts/train.py:232 step:32K smpl:256K ep:246 epch:4.55 loss:0.120 grdn:6.953 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 456 |
+
INFO 2025-05-29 04:58:59 ts/train.py:232 step:32K smpl:258K ep:247 epch:4.58 loss:0.120 grdn:6.804 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 457 |
+
INFO 2025-05-29 05:00:39 ts/train.py:232 step:32K smpl:259K ep:249 epch:4.61 loss:0.120 grdn:6.827 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 458 |
+
INFO 2025-05-29 05:02:19 ts/train.py:232 step:33K smpl:261K ep:250 epch:4.63 loss:0.118 grdn:6.924 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 459 |
+
INFO 2025-05-29 05:03:59 ts/train.py:232 step:33K smpl:262K ep:252 epch:4.66 loss:0.120 grdn:7.050 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 460 |
+
INFO 2025-05-29 05:05:39 ts/train.py:232 step:33K smpl:264K ep:253 epch:4.69 loss:0.120 grdn:7.072 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 461 |
+
INFO 2025-05-29 05:07:19 ts/train.py:232 step:33K smpl:266K ep:255 epch:4.72 loss:0.119 grdn:6.885 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 462 |
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INFO 2025-05-29 05:08:58 ts/train.py:232 step:33K smpl:267K ep:256 epch:4.75 loss:0.117 grdn:6.900 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 463 |
+
INFO 2025-05-29 05:10:38 ts/train.py:232 step:34K smpl:269K ep:258 epch:4.78 loss:0.118 grdn:7.085 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 464 |
+
INFO 2025-05-29 05:12:18 ts/train.py:232 step:34K smpl:270K ep:260 epch:4.81 loss:0.116 grdn:7.047 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 465 |
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INFO 2025-05-29 05:13:57 ts/train.py:232 step:34K smpl:272K ep:261 epch:4.83 loss:0.119 grdn:7.265 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 466 |
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INFO 2025-05-29 05:15:37 ts/train.py:232 step:34K smpl:274K ep:263 epch:4.86 loss:0.117 grdn:6.755 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 467 |
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INFO 2025-05-29 05:17:16 ts/train.py:232 step:34K smpl:275K ep:264 epch:4.89 loss:0.116 grdn:6.916 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 468 |
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INFO 2025-05-29 05:18:56 ts/train.py:232 step:35K smpl:277K ep:266 epch:4.92 loss:0.115 grdn:6.660 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 469 |
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INFO 2025-05-29 05:20:36 ts/train.py:232 step:35K smpl:278K ep:267 epch:4.95 loss:0.113 grdn:6.739 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 470 |
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INFO 2025-05-29 05:22:15 ts/train.py:232 step:35K smpl:280K ep:269 epch:4.98 loss:0.115 grdn:6.981 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 471 |
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INFO 2025-05-29 05:23:56 ts/train.py:232 step:35K smpl:282K ep:270 epch:5.00 loss:0.115 grdn:6.700 lr:1.0e-05 updt_s:0.496 data_s:0.006
|
| 472 |
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INFO 2025-05-29 05:25:36 ts/train.py:232 step:35K smpl:283K ep:272 epch:5.03 loss:0.112 grdn:6.335 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 473 |
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INFO 2025-05-29 05:27:16 ts/train.py:232 step:36K smpl:285K ep:273 epch:5.06 loss:0.112 grdn:6.449 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 474 |
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INFO 2025-05-29 05:28:56 ts/train.py:232 step:36K smpl:286K ep:275 epch:5.09 loss:0.112 grdn:6.373 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 475 |
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INFO 2025-05-29 05:30:36 ts/train.py:232 step:36K smpl:288K ep:276 epch:5.12 loss:0.114 grdn:6.802 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 476 |
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INFO 2025-05-29 05:32:16 ts/train.py:232 step:36K smpl:290K ep:278 epch:5.15 loss:0.114 grdn:6.626 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 477 |
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INFO 2025-05-29 05:33:56 ts/train.py:232 step:36K smpl:291K ep:279 epch:5.18 loss:0.111 grdn:6.369 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 478 |
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INFO 2025-05-29 05:35:36 ts/train.py:232 step:37K smpl:293K ep:281 epch:5.20 loss:0.111 grdn:6.429 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 479 |
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INFO 2025-05-29 05:37:16 ts/train.py:232 step:37K smpl:294K ep:283 epch:5.23 loss:0.111 grdn:6.546 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 480 |
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INFO 2025-05-29 05:38:56 ts/train.py:232 step:37K smpl:296K ep:284 epch:5.26 loss:0.109 grdn:6.179 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 481 |
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INFO 2025-05-29 05:40:36 ts/train.py:232 step:37K smpl:298K ep:286 epch:5.29 loss:0.110 grdn:6.531 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 482 |
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INFO 2025-05-29 05:42:16 ts/train.py:232 step:37K smpl:299K ep:287 epch:5.32 loss:0.111 grdn:6.526 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 483 |
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INFO 2025-05-29 05:43:56 ts/train.py:232 step:38K smpl:301K ep:289 epch:5.35 loss:0.111 grdn:6.365 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 484 |
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INFO 2025-05-29 05:45:36 ts/train.py:232 step:38K smpl:302K ep:290 epch:5.37 loss:0.112 grdn:6.533 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 485 |
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INFO 2025-05-29 05:47:16 ts/train.py:232 step:38K smpl:304K ep:292 epch:5.40 loss:0.112 grdn:6.447 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 486 |
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INFO 2025-05-29 05:48:56 ts/train.py:232 step:38K smpl:306K ep:293 epch:5.43 loss:0.111 grdn:6.430 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 487 |
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INFO 2025-05-29 05:50:36 ts/train.py:232 step:38K smpl:307K ep:295 epch:5.46 loss:0.110 grdn:6.653 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 488 |
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INFO 2025-05-29 05:52:16 ts/train.py:232 step:39K smpl:309K ep:296 epch:5.49 loss:0.109 grdn:6.167 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 489 |
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INFO 2025-05-29 05:53:56 ts/train.py:232 step:39K smpl:310K ep:298 epch:5.52 loss:0.109 grdn:6.302 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 490 |
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INFO 2025-05-29 05:55:36 ts/train.py:232 step:39K smpl:312K ep:299 epch:5.54 loss:0.109 grdn:6.101 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 491 |
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INFO 2025-05-29 05:57:16 ts/train.py:232 step:39K smpl:314K ep:301 epch:5.57 loss:0.109 grdn:6.263 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 492 |
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INFO 2025-05-29 05:58:56 ts/train.py:232 step:39K smpl:315K ep:302 epch:5.60 loss:0.111 grdn:6.486 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 493 |
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INFO 2025-05-29 06:00:36 ts/train.py:232 step:40K smpl:317K ep:304 epch:5.63 loss:0.110 grdn:6.179 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 494 |
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INFO 2025-05-29 06:02:16 ts/train.py:232 step:40K smpl:318K ep:306 epch:5.66 loss:0.110 grdn:6.400 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 495 |
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INFO 2025-05-29 06:03:55 ts/train.py:232 step:40K smpl:320K ep:307 epch:5.69 loss:0.108 grdn:6.264 lr:1.0e-05 updt_s:0.496 data_s:0.000
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| 496 |
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INFO 2025-05-29 06:05:35 ts/train.py:232 step:40K smpl:322K ep:309 epch:5.72 loss:0.106 grdn:6.071 lr:1.0e-05 updt_s:0.497 data_s:0.000
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| 497 |
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INFO 2025-05-29 06:07:15 ts/train.py:232 step:40K smpl:323K ep:310 epch:5.74 loss:0.107 grdn:6.437 lr:1.0e-05 updt_s:0.497 data_s:0.000
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| 498 |
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INFO 2025-05-29 06:08:54 ts/train.py:232 step:41K smpl:325K ep:312 epch:5.77 loss:0.108 grdn:6.294 lr:1.0e-05 updt_s:0.497 data_s:0.000
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| 499 |
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INFO 2025-05-29 06:10:34 ts/train.py:232 step:41K smpl:326K ep:313 epch:5.80 loss:0.105 grdn:6.136 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 500 |
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INFO 2025-05-29 06:12:13 ts/train.py:232 step:41K smpl:328K ep:315 epch:5.83 loss:0.107 grdn:6.099 lr:1.0e-05 updt_s:0.496 data_s:0.000
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| 501 |
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INFO 2025-05-29 06:13:53 ts/train.py:232 step:41K smpl:330K ep:316 epch:5.86 loss:0.106 grdn:6.262 lr:1.0e-05 updt_s:0.497 data_s:0.000
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| 502 |
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INFO 2025-05-29 06:15:33 ts/train.py:232 step:41K smpl:331K ep:318 epch:5.89 loss:0.109 grdn:6.179 lr:1.0e-05 updt_s:0.496 data_s:0.000
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| 503 |
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INFO 2025-05-29 06:17:12 ts/train.py:232 step:42K smpl:333K ep:319 epch:5.91 loss:0.108 grdn:6.045 lr:1.0e-05 updt_s:0.497 data_s:0.000
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| 504 |
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INFO 2025-05-29 06:18:52 ts/train.py:232 step:42K smpl:334K ep:321 epch:5.94 loss:0.107 grdn:6.293 lr:1.0e-05 updt_s:0.497 data_s:0.000
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| 505 |
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INFO 2025-05-29 06:20:31 ts/train.py:232 step:42K smpl:336K ep:322 epch:5.97 loss:0.107 grdn:6.173 lr:1.0e-05 updt_s:0.496 data_s:0.000
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| 506 |
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INFO 2025-05-29 06:22:11 ts/train.py:232 step:42K smpl:338K ep:324 epch:6.00 loss:0.106 grdn:5.987 lr:1.0e-05 updt_s:0.497 data_s:0.000
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| 507 |
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INFO 2025-05-29 06:23:52 ts/train.py:232 step:42K smpl:339K ep:326 epch:6.03 loss:0.103 grdn:5.874 lr:1.0e-05 updt_s:0.497 data_s:0.006
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| 508 |
+
INFO 2025-05-29 06:25:32 ts/train.py:232 step:43K smpl:341K ep:327 epch:6.06 loss:0.104 grdn:5.882 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 509 |
+
INFO 2025-05-29 06:27:11 ts/train.py:232 step:43K smpl:342K ep:329 epch:6.09 loss:0.106 grdn:6.234 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 510 |
+
INFO 2025-05-29 06:28:51 ts/train.py:232 step:43K smpl:344K ep:330 epch:6.11 loss:0.103 grdn:6.019 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 511 |
+
INFO 2025-05-29 06:30:31 ts/train.py:232 step:43K smpl:346K ep:332 epch:6.14 loss:0.104 grdn:5.653 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 512 |
+
INFO 2025-05-29 06:32:11 ts/train.py:232 step:43K smpl:347K ep:333 epch:6.17 loss:0.103 grdn:5.782 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 513 |
+
INFO 2025-05-29 06:33:50 ts/train.py:232 step:44K smpl:349K ep:335 epch:6.20 loss:0.104 grdn:5.820 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 514 |
+
INFO 2025-05-29 06:35:30 ts/train.py:232 step:44K smpl:350K ep:336 epch:6.23 loss:0.104 grdn:5.730 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 515 |
+
INFO 2025-05-29 06:37:09 ts/train.py:232 step:44K smpl:352K ep:338 epch:6.26 loss:0.103 grdn:5.777 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 516 |
+
INFO 2025-05-29 06:38:49 ts/train.py:232 step:44K smpl:354K ep:339 epch:6.28 loss:0.105 grdn:6.017 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 517 |
+
INFO 2025-05-29 06:40:29 ts/train.py:232 step:44K smpl:355K ep:341 epch:6.31 loss:0.102 grdn:5.962 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 518 |
+
INFO 2025-05-29 06:42:08 ts/train.py:232 step:45K smpl:357K ep:342 epch:6.34 loss:0.103 grdn:5.987 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 519 |
+
INFO 2025-05-29 06:43:48 ts/train.py:232 step:45K smpl:358K ep:344 epch:6.37 loss:0.104 grdn:6.116 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 520 |
+
INFO 2025-05-29 06:45:28 ts/train.py:232 step:45K smpl:360K ep:345 epch:6.40 loss:0.101 grdn:5.932 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 521 |
+
INFO 2025-05-29 06:47:07 ts/train.py:232 step:45K smpl:362K ep:347 epch:6.43 loss:0.103 grdn:5.845 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 522 |
+
INFO 2025-05-29 06:48:47 ts/train.py:232 step:45K smpl:363K ep:349 epch:6.45 loss:0.100 grdn:5.746 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 523 |
+
INFO 2025-05-29 06:50:26 ts/train.py:232 step:46K smpl:365K ep:350 epch:6.48 loss:0.104 grdn:5.929 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 524 |
+
INFO 2025-05-29 06:52:06 ts/train.py:232 step:46K smpl:366K ep:352 epch:6.51 loss:0.101 grdn:5.653 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 525 |
+
INFO 2025-05-29 06:53:46 ts/train.py:232 step:46K smpl:368K ep:353 epch:6.54 loss:0.099 grdn:5.812 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 526 |
+
INFO 2025-05-29 06:55:25 ts/train.py:232 step:46K smpl:370K ep:355 epch:6.57 loss:0.102 grdn:5.852 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 527 |
+
INFO 2025-05-29 06:57:05 ts/train.py:232 step:46K smpl:371K ep:356 epch:6.60 loss:0.101 grdn:6.019 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 528 |
+
INFO 2025-05-29 06:58:45 ts/train.py:232 step:47K smpl:373K ep:358 epch:6.63 loss:0.102 grdn:5.933 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 529 |
+
INFO 2025-05-29 07:00:24 ts/train.py:232 step:47K smpl:374K ep:359 epch:6.65 loss:0.101 grdn:5.592 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 530 |
+
INFO 2025-05-29 07:02:04 ts/train.py:232 step:47K smpl:376K ep:361 epch:6.68 loss:0.103 grdn:5.962 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 531 |
+
INFO 2025-05-29 07:03:44 ts/train.py:232 step:47K smpl:378K ep:362 epch:6.71 loss:0.103 grdn:5.670 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 532 |
+
INFO 2025-05-29 07:05:23 ts/train.py:232 step:47K smpl:379K ep:364 epch:6.74 loss:0.101 grdn:5.559 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 533 |
+
INFO 2025-05-29 07:07:03 ts/train.py:232 step:48K smpl:381K ep:365 epch:6.77 loss:0.102 grdn:5.755 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 534 |
+
INFO 2025-05-29 07:08:42 ts/train.py:232 step:48K smpl:382K ep:367 epch:6.80 loss:0.098 grdn:5.585 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 535 |
+
INFO 2025-05-29 07:10:22 ts/train.py:232 step:48K smpl:384K ep:369 epch:6.82 loss:0.100 grdn:5.655 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 536 |
+
INFO 2025-05-29 07:12:02 ts/train.py:232 step:48K smpl:386K ep:370 epch:6.85 loss:0.098 grdn:5.569 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 537 |
+
INFO 2025-05-29 07:13:41 ts/train.py:232 step:48K smpl:387K ep:372 epch:6.88 loss:0.098 grdn:5.570 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 538 |
+
INFO 2025-05-29 07:15:21 ts/train.py:232 step:49K smpl:389K ep:373 epch:6.91 loss:0.099 grdn:5.533 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 539 |
+
INFO 2025-05-29 07:17:01 ts/train.py:232 step:49K smpl:390K ep:375 epch:6.94 loss:0.100 grdn:5.512 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 540 |
+
INFO 2025-05-29 07:18:41 ts/train.py:232 step:49K smpl:392K ep:376 epch:6.97 loss:0.101 grdn:5.793 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 541 |
+
INFO 2025-05-29 07:20:21 ts/train.py:232 step:49K smpl:394K ep:378 epch:7.00 loss:0.098 grdn:5.405 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 542 |
+
INFO 2025-05-29 07:22:02 ts/train.py:232 step:49K smpl:395K ep:379 epch:7.02 loss:0.096 grdn:5.613 lr:1.0e-05 updt_s:0.498 data_s:0.006
|
| 543 |
+
INFO 2025-05-29 07:23:42 ts/train.py:232 step:50K smpl:397K ep:381 epch:7.05 loss:0.099 grdn:5.474 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 544 |
+
INFO 2025-05-29 07:25:21 ts/train.py:232 step:50K smpl:398K ep:382 epch:7.08 loss:0.096 grdn:5.473 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 545 |
+
INFO 2025-05-29 07:27:01 ts/train.py:232 step:50K smpl:400K ep:384 epch:7.11 loss:0.098 grdn:5.252 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 546 |
+
INFO 2025-05-29 07:27:01 ts/train.py:241 Checkpoint policy after step 50000
|
| 547 |
+
INFO 2025-05-29 07:28:42 ts/train.py:232 step:50K smpl:402K ep:385 epch:7.14 loss:0.098 grdn:5.230 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 548 |
+
INFO 2025-05-29 07:30:22 ts/train.py:232 step:50K smpl:403K ep:387 epch:7.17 loss:0.097 grdn:5.457 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 549 |
+
INFO 2025-05-29 07:32:02 ts/train.py:232 step:51K smpl:405K ep:388 epch:7.19 loss:0.098 grdn:5.640 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 550 |
+
INFO 2025-05-29 07:33:41 ts/train.py:232 step:51K smpl:406K ep:390 epch:7.22 loss:0.095 grdn:5.436 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 551 |
+
INFO 2025-05-29 07:35:21 ts/train.py:232 step:51K smpl:408K ep:392 epch:7.25 loss:0.097 grdn:5.292 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 552 |
+
INFO 2025-05-29 07:37:01 ts/train.py:232 step:51K smpl:410K ep:393 epch:7.28 loss:0.099 grdn:5.587 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 553 |
+
INFO 2025-05-29 07:38:41 ts/train.py:232 step:51K smpl:411K ep:395 epch:7.31 loss:0.097 grdn:5.373 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 554 |
+
INFO 2025-05-29 07:40:21 ts/train.py:232 step:52K smpl:413K ep:396 epch:7.34 loss:0.096 grdn:5.263 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 555 |
+
INFO 2025-05-29 07:42:01 ts/train.py:232 step:52K smpl:414K ep:398 epch:7.36 loss:0.095 grdn:5.448 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 556 |
+
INFO 2025-05-29 07:43:41 ts/train.py:232 step:52K smpl:416K ep:399 epch:7.39 loss:0.095 grdn:5.284 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 557 |
+
INFO 2025-05-29 07:45:21 ts/train.py:232 step:52K smpl:418K ep:401 epch:7.42 loss:0.097 grdn:5.475 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 558 |
+
INFO 2025-05-29 07:47:01 ts/train.py:232 step:52K smpl:419K ep:402 epch:7.45 loss:0.097 grdn:5.394 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 559 |
+
INFO 2025-05-29 07:48:41 ts/train.py:232 step:53K smpl:421K ep:404 epch:7.48 loss:0.096 grdn:5.410 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 560 |
+
INFO 2025-05-29 07:50:21 ts/train.py:232 step:53K smpl:422K ep:405 epch:7.51 loss:0.096 grdn:5.417 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 561 |
+
INFO 2025-05-29 07:52:01 ts/train.py:232 step:53K smpl:424K ep:407 epch:7.54 loss:0.094 grdn:5.168 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 562 |
+
INFO 2025-05-29 07:53:41 ts/train.py:232 step:53K smpl:426K ep:408 epch:7.56 loss:0.096 grdn:5.585 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 563 |
+
INFO 2025-05-29 07:55:21 ts/train.py:232 step:53K smpl:427K ep:410 epch:7.59 loss:0.096 grdn:5.362 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 564 |
+
INFO 2025-05-29 07:57:00 ts/train.py:232 step:54K smpl:429K ep:412 epch:7.62 loss:0.095 grdn:5.456 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 565 |
+
INFO 2025-05-29 07:58:39 ts/train.py:232 step:54K smpl:430K ep:413 epch:7.65 loss:0.096 grdn:5.241 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 566 |
+
INFO 2025-05-29 08:00:19 ts/train.py:232 step:54K smpl:432K ep:415 epch:7.68 loss:0.094 grdn:5.179 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 567 |
+
INFO 2025-05-29 08:01:59 ts/train.py:232 step:54K smpl:434K ep:416 epch:7.71 loss:0.093 grdn:5.435 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 568 |
+
INFO 2025-05-29 08:03:39 ts/train.py:232 step:54K smpl:435K ep:418 epch:7.73 loss:0.095 grdn:5.245 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 569 |
+
INFO 2025-05-29 08:05:19 ts/train.py:232 step:55K smpl:437K ep:419 epch:7.76 loss:0.094 grdn:5.286 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 570 |
+
INFO 2025-05-29 08:06:58 ts/train.py:232 step:55K smpl:438K ep:421 epch:7.79 loss:0.096 grdn:5.279 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 571 |
+
INFO 2025-05-29 08:08:37 ts/train.py:232 step:55K smpl:440K ep:422 epch:7.82 loss:0.095 grdn:5.404 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 572 |
+
INFO 2025-05-29 08:10:17 ts/train.py:232 step:55K smpl:442K ep:424 epch:7.85 loss:0.094 grdn:5.079 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 573 |
+
INFO 2025-05-29 08:11:57 ts/train.py:232 step:55K smpl:443K ep:425 epch:7.88 loss:0.093 grdn:5.249 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 574 |
+
INFO 2025-05-29 08:13:36 ts/train.py:232 step:56K smpl:445K ep:427 epch:7.91 loss:0.094 grdn:5.301 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 575 |
+
INFO 2025-05-29 08:15:16 ts/train.py:232 step:56K smpl:446K ep:428 epch:7.93 loss:0.093 grdn:5.200 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 576 |
+
INFO 2025-05-29 08:16:56 ts/train.py:232 step:56K smpl:448K ep:430 epch:7.96 loss:0.094 grdn:5.564 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 577 |
+
INFO 2025-05-29 08:18:36 ts/train.py:232 step:56K smpl:450K ep:431 epch:7.99 loss:0.090 grdn:4.927 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 578 |
+
INFO 2025-05-29 08:20:17 ts/train.py:232 step:56K smpl:451K ep:433 epch:8.02 loss:0.094 grdn:5.393 lr:1.0e-05 updt_s:0.498 data_s:0.006
|
| 579 |
+
INFO 2025-05-29 08:21:57 ts/train.py:232 step:57K smpl:453K ep:435 epch:8.05 loss:0.093 grdn:5.380 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 580 |
+
INFO 2025-05-29 08:23:37 ts/train.py:232 step:57K smpl:454K ep:436 epch:8.08 loss:0.091 grdn:4.997 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 581 |
+
INFO 2025-05-29 08:25:17 ts/train.py:232 step:57K smpl:456K ep:438 epch:8.10 loss:0.092 grdn:5.211 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 582 |
+
INFO 2025-05-29 08:26:57 ts/train.py:232 step:57K smpl:458K ep:439 epch:8.13 loss:0.093 grdn:5.314 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 583 |
+
INFO 2025-05-29 08:28:37 ts/train.py:232 step:57K smpl:459K ep:441 epch:8.16 loss:0.093 grdn:5.210 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 584 |
+
INFO 2025-05-29 08:30:17 ts/train.py:232 step:58K smpl:461K ep:442 epch:8.19 loss:0.091 grdn:4.981 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 585 |
+
INFO 2025-05-29 08:31:57 ts/train.py:232 step:58K smpl:462K ep:444 epch:8.22 loss:0.090 grdn:5.053 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 586 |
+
INFO 2025-05-29 08:33:37 ts/train.py:232 step:58K smpl:464K ep:445 epch:8.25 loss:0.092 grdn:5.020 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 587 |
+
INFO 2025-05-29 08:35:17 ts/train.py:232 step:58K smpl:466K ep:447 epch:8.27 loss:0.092 grdn:4.999 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 588 |
+
INFO 2025-05-29 08:36:57 ts/train.py:232 step:58K smpl:467K ep:448 epch:8.30 loss:0.093 grdn:5.228 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 589 |
+
INFO 2025-05-29 08:38:37 ts/train.py:232 step:59K smpl:469K ep:450 epch:8.33 loss:0.091 grdn:5.175 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 590 |
+
INFO 2025-05-29 08:40:18 ts/train.py:232 step:59K smpl:470K ep:451 epch:8.36 loss:0.091 grdn:4.937 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 591 |
+
INFO 2025-05-29 08:41:57 ts/train.py:232 step:59K smpl:472K ep:453 epch:8.39 loss:0.091 grdn:4.997 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 592 |
+
INFO 2025-05-29 08:43:37 ts/train.py:232 step:59K smpl:474K ep:455 epch:8.42 loss:0.090 grdn:4.865 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 593 |
+
INFO 2025-05-29 08:45:18 ts/train.py:232 step:59K smpl:475K ep:456 epch:8.45 loss:0.091 grdn:5.161 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 594 |
+
INFO 2025-05-29 08:46:58 ts/train.py:232 step:60K smpl:477K ep:458 epch:8.47 loss:0.091 grdn:4.836 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 595 |
+
INFO 2025-05-29 08:48:38 ts/train.py:232 step:60K smpl:478K ep:459 epch:8.50 loss:0.088 grdn:4.928 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 596 |
+
INFO 2025-05-29 08:50:18 ts/train.py:232 step:60K smpl:480K ep:461 epch:8.53 loss:0.089 grdn:4.867 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 597 |
+
INFO 2025-05-29 08:51:58 ts/train.py:232 step:60K smpl:482K ep:462 epch:8.56 loss:0.090 grdn:5.127 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 598 |
+
INFO 2025-05-29 08:53:38 ts/train.py:232 step:60K smpl:483K ep:464 epch:8.59 loss:0.091 grdn:5.190 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 599 |
+
INFO 2025-05-29 08:55:18 ts/train.py:232 step:61K smpl:485K ep:465 epch:8.62 loss:0.088 grdn:4.994 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 600 |
+
INFO 2025-05-29 08:56:58 ts/train.py:232 step:61K smpl:486K ep:467 epch:8.64 loss:0.089 grdn:5.157 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 601 |
+
INFO 2025-05-29 08:58:38 ts/train.py:232 step:61K smpl:488K ep:468 epch:8.67 loss:0.089 grdn:5.088 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 602 |
+
INFO 2025-05-29 09:00:18 ts/train.py:232 step:61K smpl:490K ep:470 epch:8.70 loss:0.090 grdn:5.172 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 603 |
+
INFO 2025-05-29 09:01:58 ts/train.py:232 step:61K smpl:491K ep:471 epch:8.73 loss:0.089 grdn:4.929 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 604 |
+
INFO 2025-05-29 09:03:37 ts/train.py:232 step:62K smpl:493K ep:473 epch:8.76 loss:0.089 grdn:5.021 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 605 |
+
INFO 2025-05-29 09:05:17 ts/train.py:232 step:62K smpl:494K ep:474 epch:8.79 loss:0.091 grdn:4.946 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 606 |
+
INFO 2025-05-29 09:06:56 ts/train.py:232 step:62K smpl:496K ep:476 epch:8.81 loss:0.090 grdn:4.882 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 607 |
+
INFO 2025-05-29 09:08:35 ts/train.py:232 step:62K smpl:498K ep:478 epch:8.84 loss:0.089 grdn:5.098 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 608 |
+
INFO 2025-05-29 09:10:15 ts/train.py:232 step:62K smpl:499K ep:479 epch:8.87 loss:0.089 grdn:4.905 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 609 |
+
INFO 2025-05-29 09:11:54 ts/train.py:232 step:63K smpl:501K ep:481 epch:8.90 loss:0.089 grdn:4.935 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 610 |
+
INFO 2025-05-29 09:13:34 ts/train.py:232 step:63K smpl:502K ep:482 epch:8.93 loss:0.088 grdn:4.772 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 611 |
+
INFO 2025-05-29 09:15:13 ts/train.py:232 step:63K smpl:504K ep:484 epch:8.96 loss:0.089 grdn:4.825 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 612 |
+
INFO 2025-05-29 09:16:52 ts/train.py:232 step:63K smpl:506K ep:485 epch:8.99 loss:0.088 grdn:4.931 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 613 |
+
INFO 2025-05-29 09:18:33 ts/train.py:232 step:63K smpl:507K ep:487 epch:9.01 loss:0.089 grdn:4.950 lr:1.0e-05 updt_s:0.495 data_s:0.006
|
| 614 |
+
INFO 2025-05-29 09:20:13 ts/train.py:232 step:64K smpl:509K ep:488 epch:9.04 loss:0.089 grdn:4.856 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 615 |
+
INFO 2025-05-29 09:21:53 ts/train.py:232 step:64K smpl:510K ep:490 epch:9.07 loss:0.088 grdn:4.846 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 616 |
+
INFO 2025-05-29 09:23:33 ts/train.py:232 step:64K smpl:512K ep:491 epch:9.10 loss:0.088 grdn:4.947 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 617 |
+
INFO 2025-05-29 09:25:13 ts/train.py:232 step:64K smpl:514K ep:493 epch:9.13 loss:0.088 grdn:4.878 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 618 |
+
INFO 2025-05-29 09:26:53 ts/train.py:232 step:64K smpl:515K ep:494 epch:9.16 loss:0.087 grdn:4.961 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 619 |
+
INFO 2025-05-29 09:28:33 ts/train.py:232 step:65K smpl:517K ep:496 epch:9.18 loss:0.087 grdn:4.731 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 620 |
+
INFO 2025-05-29 09:30:13 ts/train.py:232 step:65K smpl:518K ep:498 epch:9.21 loss:0.085 grdn:4.691 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 621 |
+
INFO 2025-05-29 09:31:53 ts/train.py:232 step:65K smpl:520K ep:499 epch:9.24 loss:0.089 grdn:4.880 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 622 |
+
INFO 2025-05-29 09:33:33 ts/train.py:232 step:65K smpl:522K ep:501 epch:9.27 loss:0.087 grdn:4.779 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 623 |
+
INFO 2025-05-29 09:35:13 ts/train.py:232 step:65K smpl:523K ep:502 epch:9.30 loss:0.087 grdn:4.742 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 624 |
+
INFO 2025-05-29 09:36:53 ts/train.py:232 step:66K smpl:525K ep:504 epch:9.33 loss:0.085 grdn:4.695 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 625 |
+
INFO 2025-05-29 09:38:33 ts/train.py:232 step:66K smpl:526K ep:505 epch:9.36 loss:0.087 grdn:4.756 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 626 |
+
INFO 2025-05-29 09:40:13 ts/train.py:232 step:66K smpl:528K ep:507 epch:9.38 loss:0.086 grdn:4.773 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 627 |
+
INFO 2025-05-29 09:41:53 ts/train.py:232 step:66K smpl:530K ep:508 epch:9.41 loss:0.086 grdn:4.734 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 628 |
+
INFO 2025-05-29 09:43:32 ts/train.py:232 step:66K smpl:531K ep:510 epch:9.44 loss:0.085 grdn:4.686 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 629 |
+
INFO 2025-05-29 09:45:12 ts/train.py:232 step:67K smpl:533K ep:511 epch:9.47 loss:0.086 grdn:4.683 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 630 |
+
INFO 2025-05-29 09:46:52 ts/train.py:232 step:67K smpl:534K ep:513 epch:9.50 loss:0.086 grdn:4.660 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 631 |
+
INFO 2025-05-29 09:48:31 ts/train.py:232 step:67K smpl:536K ep:514 epch:9.53 loss:0.087 grdn:4.705 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 632 |
+
INFO 2025-05-29 09:50:11 ts/train.py:232 step:67K smpl:538K ep:516 epch:9.55 loss:0.086 grdn:4.696 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 633 |
+
INFO 2025-05-29 09:51:51 ts/train.py:232 step:67K smpl:539K ep:517 epch:9.58 loss:0.084 grdn:4.600 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 634 |
+
INFO 2025-05-29 09:53:31 ts/train.py:232 step:68K smpl:541K ep:519 epch:9.61 loss:0.086 grdn:4.707 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 635 |
+
INFO 2025-05-29 09:55:11 ts/train.py:232 step:68K smpl:542K ep:521 epch:9.64 loss:0.087 grdn:4.809 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 636 |
+
INFO 2025-05-29 09:56:51 ts/train.py:232 step:68K smpl:544K ep:522 epch:9.67 loss:0.085 grdn:4.723 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 637 |
+
INFO 2025-05-29 09:58:31 ts/train.py:232 step:68K smpl:546K ep:524 epch:9.70 loss:0.088 grdn:4.962 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 638 |
+
INFO 2025-05-29 10:00:11 ts/train.py:232 step:68K smpl:547K ep:525 epch:9.72 loss:0.085 grdn:4.787 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 639 |
+
INFO 2025-05-29 10:01:51 ts/train.py:232 step:69K smpl:549K ep:527 epch:9.75 loss:0.085 grdn:4.694 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 640 |
+
INFO 2025-05-29 10:03:30 ts/train.py:232 step:69K smpl:550K ep:528 epch:9.78 loss:0.086 grdn:4.900 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 641 |
+
INFO 2025-05-29 10:05:10 ts/train.py:232 step:69K smpl:552K ep:530 epch:9.81 loss:0.086 grdn:4.635 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 642 |
+
INFO 2025-05-29 10:06:49 ts/train.py:232 step:69K smpl:554K ep:531 epch:9.84 loss:0.086 grdn:4.845 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 643 |
+
INFO 2025-05-29 10:08:28 ts/train.py:232 step:69K smpl:555K ep:533 epch:9.87 loss:0.086 grdn:4.769 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 644 |
+
INFO 2025-05-29 10:10:09 ts/train.py:232 step:70K smpl:557K ep:534 epch:9.90 loss:0.087 grdn:4.700 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 645 |
+
INFO 2025-05-29 10:11:48 ts/train.py:232 step:70K smpl:558K ep:536 epch:9.92 loss:0.085 grdn:4.670 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 646 |
+
INFO 2025-05-29 10:13:28 ts/train.py:232 step:70K smpl:560K ep:537 epch:9.95 loss:0.084 grdn:4.655 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 647 |
+
INFO 2025-05-29 10:15:08 ts/train.py:232 step:70K smpl:562K ep:539 epch:9.98 loss:0.084 grdn:4.566 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 648 |
+
INFO 2025-05-29 10:16:49 ts/train.py:232 step:70K smpl:563K ep:540 epch:10.01 loss:0.082 grdn:4.535 lr:1.0e-05 updt_s:0.497 data_s:0.006
|
| 649 |
+
INFO 2025-05-29 10:18:29 ts/train.py:232 step:71K smpl:565K ep:542 epch:10.04 loss:0.084 grdn:4.543 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 650 |
+
INFO 2025-05-29 10:20:09 ts/train.py:232 step:71K smpl:566K ep:544 epch:10.07 loss:0.085 grdn:4.753 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 651 |
+
INFO 2025-05-29 10:21:49 ts/train.py:232 step:71K smpl:568K ep:545 epch:10.09 loss:0.083 grdn:4.447 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 652 |
+
INFO 2025-05-29 10:23:29 ts/train.py:232 step:71K smpl:570K ep:547 epch:10.12 loss:0.083 grdn:4.447 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 653 |
+
INFO 2025-05-29 10:25:09 ts/train.py:232 step:71K smpl:571K ep:548 epch:10.15 loss:0.083 grdn:4.358 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 654 |
+
INFO 2025-05-29 10:26:49 ts/train.py:232 step:72K smpl:573K ep:550 epch:10.18 loss:0.083 grdn:4.584 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 655 |
+
INFO 2025-05-29 10:28:29 ts/train.py:232 step:72K smpl:574K ep:551 epch:10.21 loss:0.083 grdn:4.484 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 656 |
+
INFO 2025-05-29 10:30:08 ts/train.py:232 step:72K smpl:576K ep:553 epch:10.24 loss:0.083 grdn:4.703 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 657 |
+
INFO 2025-05-29 10:31:48 ts/train.py:232 step:72K smpl:578K ep:554 epch:10.27 loss:0.084 grdn:4.649 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 658 |
+
INFO 2025-05-29 10:33:27 ts/train.py:232 step:72K smpl:579K ep:556 epch:10.29 loss:0.082 grdn:4.628 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 659 |
+
INFO 2025-05-29 10:35:07 ts/train.py:232 step:73K smpl:581K ep:557 epch:10.32 loss:0.083 grdn:4.648 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 660 |
+
INFO 2025-05-29 10:36:46 ts/train.py:232 step:73K smpl:582K ep:559 epch:10.35 loss:0.083 grdn:4.717 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 661 |
+
INFO 2025-05-29 10:38:26 ts/train.py:232 step:73K smpl:584K ep:560 epch:10.38 loss:0.082 grdn:4.391 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 662 |
+
INFO 2025-05-29 10:40:05 ts/train.py:232 step:73K smpl:586K ep:562 epch:10.41 loss:0.082 grdn:4.505 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 663 |
+
INFO 2025-05-29 10:41:45 ts/train.py:232 step:73K smpl:587K ep:564 epch:10.44 loss:0.083 grdn:4.376 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 664 |
+
INFO 2025-05-29 10:43:25 ts/train.py:232 step:74K smpl:589K ep:565 epch:10.46 loss:0.084 grdn:4.618 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 665 |
+
INFO 2025-05-29 10:45:04 ts/train.py:232 step:74K smpl:590K ep:567 epch:10.49 loss:0.084 grdn:4.569 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 666 |
+
INFO 2025-05-29 10:46:44 ts/train.py:232 step:74K smpl:592K ep:568 epch:10.52 loss:0.081 grdn:4.639 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 667 |
+
INFO 2025-05-29 10:48:23 ts/train.py:232 step:74K smpl:594K ep:570 epch:10.55 loss:0.082 grdn:4.568 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 668 |
+
INFO 2025-05-29 10:50:03 ts/train.py:232 step:74K smpl:595K ep:571 epch:10.58 loss:0.082 grdn:4.592 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 669 |
+
INFO 2025-05-29 10:51:42 ts/train.py:232 step:75K smpl:597K ep:573 epch:10.61 loss:0.081 grdn:4.419 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 670 |
+
INFO 2025-05-29 10:53:22 ts/train.py:232 step:75K smpl:598K ep:574 epch:10.63 loss:0.082 grdn:4.498 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 671 |
+
INFO 2025-05-29 10:55:02 ts/train.py:232 step:75K smpl:600K ep:576 epch:10.66 loss:0.082 grdn:4.386 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 672 |
+
INFO 2025-05-29 10:56:41 ts/train.py:232 step:75K smpl:602K ep:577 epch:10.69 loss:0.082 grdn:4.480 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 673 |
+
INFO 2025-05-29 10:58:21 ts/train.py:232 step:75K smpl:603K ep:579 epch:10.72 loss:0.083 grdn:4.904 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 674 |
+
INFO 2025-05-29 11:00:00 ts/train.py:232 step:76K smpl:605K ep:580 epch:10.75 loss:0.083 grdn:4.406 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 675 |
+
INFO 2025-05-29 11:01:40 ts/train.py:232 step:76K smpl:606K ep:582 epch:10.78 loss:0.082 grdn:4.522 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 676 |
+
INFO 2025-05-29 11:03:20 ts/train.py:232 step:76K smpl:608K ep:583 epch:10.81 loss:0.082 grdn:4.549 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 677 |
+
INFO 2025-05-29 11:04:59 ts/train.py:232 step:76K smpl:610K ep:585 epch:10.83 loss:0.080 grdn:4.415 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 678 |
+
INFO 2025-05-29 11:06:39 ts/train.py:232 step:76K smpl:611K ep:587 epch:10.86 loss:0.081 grdn:4.347 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 679 |
+
INFO 2025-05-29 11:08:18 ts/train.py:232 step:77K smpl:613K ep:588 epch:10.89 loss:0.083 grdn:4.644 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 680 |
+
INFO 2025-05-29 11:09:58 ts/train.py:232 step:77K smpl:614K ep:590 epch:10.92 loss:0.081 grdn:4.515 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 681 |
+
INFO 2025-05-29 11:11:38 ts/train.py:232 step:77K smpl:616K ep:591 epch:10.95 loss:0.082 grdn:4.463 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 682 |
+
INFO 2025-05-29 11:13:17 ts/train.py:232 step:77K smpl:618K ep:593 epch:10.98 loss:0.082 grdn:4.564 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 683 |
+
INFO 2025-05-29 11:14:58 ts/train.py:232 step:77K smpl:619K ep:594 epch:11.00 loss:0.078 grdn:4.470 lr:1.0e-05 updt_s:0.496 data_s:0.005
|
| 684 |
+
INFO 2025-05-29 11:16:37 ts/train.py:232 step:78K smpl:621K ep:596 epch:11.03 loss:0.079 grdn:4.349 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 685 |
+
INFO 2025-05-29 11:18:16 ts/train.py:232 step:78K smpl:622K ep:597 epch:11.06 loss:0.081 grdn:4.359 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 686 |
+
INFO 2025-05-29 11:19:56 ts/train.py:232 step:78K smpl:624K ep:599 epch:11.09 loss:0.081 grdn:4.421 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 687 |
+
INFO 2025-05-29 11:21:35 ts/train.py:232 step:78K smpl:626K ep:600 epch:11.12 loss:0.079 grdn:4.438 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 688 |
+
INFO 2025-05-29 11:23:15 ts/train.py:232 step:78K smpl:627K ep:602 epch:11.15 loss:0.081 grdn:4.296 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 689 |
+
INFO 2025-05-29 11:24:54 ts/train.py:232 step:79K smpl:629K ep:603 epch:11.18 loss:0.081 grdn:4.508 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 690 |
+
INFO 2025-05-29 11:26:34 ts/train.py:232 step:79K smpl:630K ep:605 epch:11.20 loss:0.082 grdn:4.585 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 691 |
+
INFO 2025-05-29 11:28:14 ts/train.py:232 step:79K smpl:632K ep:607 epch:11.23 loss:0.080 grdn:4.305 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 692 |
+
INFO 2025-05-29 11:29:53 ts/train.py:232 step:79K smpl:634K ep:608 epch:11.26 loss:0.081 grdn:4.316 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 693 |
+
INFO 2025-05-29 11:31:33 ts/train.py:232 step:79K smpl:635K ep:610 epch:11.29 loss:0.081 grdn:4.385 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 694 |
+
INFO 2025-05-29 11:33:12 ts/train.py:232 step:80K smpl:637K ep:611 epch:11.32 loss:0.079 grdn:4.350 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 695 |
+
INFO 2025-05-29 11:34:52 ts/train.py:232 step:80K smpl:638K ep:613 epch:11.35 loss:0.079 grdn:4.349 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 696 |
+
INFO 2025-05-29 11:36:32 ts/train.py:232 step:80K smpl:640K ep:614 epch:11.37 loss:0.079 grdn:4.348 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 697 |
+
INFO 2025-05-29 11:38:11 ts/train.py:232 step:80K smpl:642K ep:616 epch:11.40 loss:0.080 grdn:4.550 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 698 |
+
INFO 2025-05-29 11:39:51 ts/train.py:232 step:80K smpl:643K ep:617 epch:11.43 loss:0.079 grdn:4.272 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 699 |
+
INFO 2025-05-29 11:41:31 ts/train.py:232 step:81K smpl:645K ep:619 epch:11.46 loss:0.078 grdn:4.187 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 700 |
+
INFO 2025-05-29 11:43:10 ts/train.py:232 step:81K smpl:646K ep:620 epch:11.49 loss:0.081 grdn:4.208 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 701 |
+
INFO 2025-05-29 11:44:50 ts/train.py:232 step:81K smpl:648K ep:622 epch:11.52 loss:0.081 grdn:4.478 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 702 |
+
INFO 2025-05-29 11:46:29 ts/train.py:232 step:81K smpl:650K ep:623 epch:11.54 loss:0.080 grdn:4.395 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 703 |
+
INFO 2025-05-29 11:48:09 ts/train.py:232 step:81K smpl:651K ep:625 epch:11.57 loss:0.080 grdn:4.240 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 704 |
+
INFO 2025-05-29 11:49:49 ts/train.py:232 step:82K smpl:653K ep:626 epch:11.60 loss:0.079 grdn:4.204 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 705 |
+
INFO 2025-05-29 11:51:28 ts/train.py:232 step:82K smpl:654K ep:628 epch:11.63 loss:0.078 grdn:4.264 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 706 |
+
INFO 2025-05-29 11:53:08 ts/train.py:232 step:82K smpl:656K ep:630 epch:11.66 loss:0.080 grdn:4.302 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 707 |
+
INFO 2025-05-29 11:54:47 ts/train.py:232 step:82K smpl:658K ep:631 epch:11.69 loss:0.078 grdn:4.279 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 708 |
+
INFO 2025-05-29 11:56:27 ts/train.py:232 step:82K smpl:659K ep:633 epch:11.72 loss:0.077 grdn:4.200 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 709 |
+
INFO 2025-05-29 11:58:07 ts/train.py:232 step:83K smpl:661K ep:634 epch:11.74 loss:0.078 grdn:4.316 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 710 |
+
INFO 2025-05-29 11:59:46 ts/train.py:232 step:83K smpl:662K ep:636 epch:11.77 loss:0.079 grdn:4.328 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 711 |
+
INFO 2025-05-29 12:01:26 ts/train.py:232 step:83K smpl:664K ep:637 epch:11.80 loss:0.079 grdn:4.332 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 712 |
+
INFO 2025-05-29 12:03:05 ts/train.py:232 step:83K smpl:666K ep:639 epch:11.83 loss:0.081 grdn:4.241 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 713 |
+
INFO 2025-05-29 12:04:45 ts/train.py:232 step:83K smpl:667K ep:640 epch:11.86 loss:0.078 grdn:4.257 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 714 |
+
INFO 2025-05-29 12:06:25 ts/train.py:232 step:84K smpl:669K ep:642 epch:11.89 loss:0.079 grdn:4.348 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 715 |
+
INFO 2025-05-29 12:08:04 ts/train.py:232 step:84K smpl:670K ep:643 epch:11.91 loss:0.078 grdn:4.300 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 716 |
+
INFO 2025-05-29 12:09:44 ts/train.py:232 step:84K smpl:672K ep:645 epch:11.94 loss:0.078 grdn:4.214 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 717 |
+
INFO 2025-05-29 12:11:23 ts/train.py:232 step:84K smpl:674K ep:646 epch:11.97 loss:0.078 grdn:4.333 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 718 |
+
INFO 2025-05-29 12:13:03 ts/train.py:232 step:84K smpl:675K ep:648 epch:12.00 loss:0.078 grdn:4.305 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 719 |
+
INFO 2025-05-29 12:14:44 ts/train.py:232 step:85K smpl:677K ep:650 epch:12.03 loss:0.077 grdn:4.172 lr:1.0e-05 updt_s:0.497 data_s:0.006
|
| 720 |
+
INFO 2025-05-29 12:16:24 ts/train.py:232 step:85K smpl:678K ep:651 epch:12.06 loss:0.078 grdn:4.254 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 721 |
+
INFO 2025-05-29 12:18:03 ts/train.py:232 step:85K smpl:680K ep:653 epch:12.09 loss:0.078 grdn:4.056 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 722 |
+
INFO 2025-05-29 12:19:42 ts/train.py:232 step:85K smpl:682K ep:654 epch:12.11 loss:0.077 grdn:4.099 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 723 |
+
INFO 2025-05-29 12:21:22 ts/train.py:232 step:85K smpl:683K ep:656 epch:12.14 loss:0.079 grdn:4.265 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 724 |
+
INFO 2025-05-29 12:23:01 ts/train.py:232 step:86K smpl:685K ep:657 epch:12.17 loss:0.078 grdn:4.142 lr:1.0e-05 updt_s:0.495 data_s:0.000
|
| 725 |
+
INFO 2025-05-29 12:24:40 ts/train.py:232 step:86K smpl:686K ep:659 epch:12.20 loss:0.077 grdn:4.130 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 726 |
+
INFO 2025-05-29 12:26:20 ts/train.py:232 step:86K smpl:688K ep:660 epch:12.23 loss:0.076 grdn:4.176 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 727 |
+
INFO 2025-05-29 12:28:00 ts/train.py:232 step:86K smpl:690K ep:662 epch:12.26 loss:0.077 grdn:4.419 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 728 |
+
INFO 2025-05-29 12:29:40 ts/train.py:232 step:86K smpl:691K ep:663 epch:12.28 loss:0.076 grdn:4.233 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 729 |
+
INFO 2025-05-29 12:31:20 ts/train.py:232 step:87K smpl:693K ep:665 epch:12.31 loss:0.076 grdn:3.940 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 730 |
+
INFO 2025-05-29 12:33:00 ts/train.py:232 step:87K smpl:694K ep:666 epch:12.34 loss:0.077 grdn:4.083 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 731 |
+
INFO 2025-05-29 12:34:40 ts/train.py:232 step:87K smpl:696K ep:668 epch:12.37 loss:0.077 grdn:4.191 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 732 |
+
INFO 2025-05-29 12:36:20 ts/train.py:232 step:87K smpl:698K ep:669 epch:12.40 loss:0.076 grdn:4.000 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 733 |
+
INFO 2025-05-29 12:38:01 ts/train.py:232 step:87K smpl:699K ep:671 epch:12.43 loss:0.078 grdn:4.296 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 734 |
+
INFO 2025-05-29 12:39:40 ts/train.py:232 step:88K smpl:701K ep:673 epch:12.45 loss:0.078 grdn:4.230 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 735 |
+
INFO 2025-05-29 12:41:20 ts/train.py:232 step:88K smpl:702K ep:674 epch:12.48 loss:0.077 grdn:4.193 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 736 |
+
INFO 2025-05-29 12:43:00 ts/train.py:232 step:88K smpl:704K ep:676 epch:12.51 loss:0.076 grdn:4.160 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 737 |
+
INFO 2025-05-29 12:44:40 ts/train.py:232 step:88K smpl:706K ep:677 epch:12.54 loss:0.075 grdn:4.115 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 738 |
+
INFO 2025-05-29 12:46:20 ts/train.py:232 step:88K smpl:707K ep:679 epch:12.57 loss:0.076 grdn:4.167 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 739 |
+
INFO 2025-05-29 12:48:00 ts/train.py:232 step:89K smpl:709K ep:680 epch:12.60 loss:0.077 grdn:4.201 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 740 |
+
INFO 2025-05-29 12:49:40 ts/train.py:232 step:89K smpl:710K ep:682 epch:12.63 loss:0.077 grdn:4.383 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 741 |
+
INFO 2025-05-29 12:51:20 ts/train.py:232 step:89K smpl:712K ep:683 epch:12.65 loss:0.076 grdn:3.984 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 742 |
+
INFO 2025-05-29 12:53:00 ts/train.py:232 step:89K smpl:714K ep:685 epch:12.68 loss:0.076 grdn:4.151 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 743 |
+
INFO 2025-05-29 12:54:40 ts/train.py:232 step:89K smpl:715K ep:686 epch:12.71 loss:0.076 grdn:4.125 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 744 |
+
INFO 2025-05-29 12:56:20 ts/train.py:232 step:90K smpl:717K ep:688 epch:12.74 loss:0.074 grdn:3.778 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 745 |
+
INFO 2025-05-29 12:58:00 ts/train.py:232 step:90K smpl:718K ep:689 epch:12.77 loss:0.075 grdn:4.065 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 746 |
+
INFO 2025-05-29 12:59:40 ts/train.py:232 step:90K smpl:720K ep:691 epch:12.80 loss:0.075 grdn:3.979 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 747 |
+
INFO 2025-05-29 13:01:20 ts/train.py:232 step:90K smpl:722K ep:693 epch:12.82 loss:0.076 grdn:4.225 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 748 |
+
INFO 2025-05-29 13:03:00 ts/train.py:232 step:90K smpl:723K ep:694 epch:12.85 loss:0.075 grdn:4.044 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 749 |
+
INFO 2025-05-29 13:04:40 ts/train.py:232 step:91K smpl:725K ep:696 epch:12.88 loss:0.074 grdn:4.025 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 750 |
+
INFO 2025-05-29 13:06:20 ts/train.py:232 step:91K smpl:726K ep:697 epch:12.91 loss:0.076 grdn:4.064 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 751 |
+
INFO 2025-05-29 13:08:00 ts/train.py:232 step:91K smpl:728K ep:699 epch:12.94 loss:0.075 grdn:4.126 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 752 |
+
INFO 2025-05-29 13:09:40 ts/train.py:232 step:91K smpl:730K ep:700 epch:12.97 loss:0.074 grdn:3.987 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 753 |
+
INFO 2025-05-29 13:11:20 ts/train.py:232 step:91K smpl:731K ep:702 epch:12.99 loss:0.077 grdn:4.014 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 754 |
+
INFO 2025-05-29 13:13:01 ts/train.py:232 step:92K smpl:733K ep:703 epch:13.02 loss:0.074 grdn:3.896 lr:1.0e-05 updt_s:0.497 data_s:0.006
|
| 755 |
+
INFO 2025-05-29 13:14:41 ts/train.py:232 step:92K smpl:734K ep:705 epch:13.05 loss:0.073 grdn:3.954 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 756 |
+
INFO 2025-05-29 13:16:21 ts/train.py:232 step:92K smpl:736K ep:706 epch:13.08 loss:0.076 grdn:4.198 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 757 |
+
INFO 2025-05-29 13:18:00 ts/train.py:232 step:92K smpl:738K ep:708 epch:13.11 loss:0.074 grdn:4.050 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 758 |
+
INFO 2025-05-29 13:19:40 ts/train.py:232 step:92K smpl:739K ep:709 epch:13.14 loss:0.074 grdn:4.022 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 759 |
+
INFO 2025-05-29 13:21:19 ts/train.py:232 step:93K smpl:741K ep:711 epch:13.17 loss:0.076 grdn:4.125 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 760 |
+
INFO 2025-05-29 13:22:59 ts/train.py:232 step:93K smpl:742K ep:712 epch:13.19 loss:0.075 grdn:4.063 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 761 |
+
INFO 2025-05-29 13:24:39 ts/train.py:232 step:93K smpl:744K ep:714 epch:13.22 loss:0.076 grdn:4.069 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 762 |
+
INFO 2025-05-29 13:26:18 ts/train.py:232 step:93K smpl:746K ep:716 epch:13.25 loss:0.073 grdn:4.125 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 763 |
+
INFO 2025-05-29 13:27:58 ts/train.py:232 step:93K smpl:747K ep:717 epch:13.28 loss:0.076 grdn:4.303 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 764 |
+
INFO 2025-05-29 13:29:37 ts/train.py:232 step:94K smpl:749K ep:719 epch:13.31 loss:0.076 grdn:3.901 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 765 |
+
INFO 2025-05-29 13:31:17 ts/train.py:232 step:94K smpl:750K ep:720 epch:13.34 loss:0.074 grdn:3.881 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 766 |
+
INFO 2025-05-29 13:32:56 ts/train.py:232 step:94K smpl:752K ep:722 epch:13.36 loss:0.074 grdn:4.060 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 767 |
+
INFO 2025-05-29 13:34:36 ts/train.py:232 step:94K smpl:754K ep:723 epch:13.39 loss:0.073 grdn:3.846 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 768 |
+
INFO 2025-05-29 13:36:15 ts/train.py:232 step:94K smpl:755K ep:725 epch:13.42 loss:0.075 grdn:4.190 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 769 |
+
INFO 2025-05-29 13:37:55 ts/train.py:232 step:95K smpl:757K ep:726 epch:13.45 loss:0.073 grdn:3.914 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 770 |
+
INFO 2025-05-29 13:39:34 ts/train.py:232 step:95K smpl:758K ep:728 epch:13.48 loss:0.074 grdn:3.931 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 771 |
+
INFO 2025-05-29 13:41:14 ts/train.py:232 step:95K smpl:760K ep:729 epch:13.51 loss:0.075 grdn:4.036 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 772 |
+
INFO 2025-05-29 13:42:54 ts/train.py:232 step:95K smpl:762K ep:731 epch:13.54 loss:0.074 grdn:4.082 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 773 |
+
INFO 2025-05-29 13:44:33 ts/train.py:232 step:95K smpl:763K ep:732 epch:13.56 loss:0.072 grdn:4.035 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 774 |
+
INFO 2025-05-29 13:46:13 ts/train.py:232 step:96K smpl:765K ep:734 epch:13.59 loss:0.076 grdn:4.075 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 775 |
+
INFO 2025-05-29 13:47:52 ts/train.py:232 step:96K smpl:766K ep:736 epch:13.62 loss:0.074 grdn:3.970 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 776 |
+
INFO 2025-05-29 13:49:32 ts/train.py:232 step:96K smpl:768K ep:737 epch:13.65 loss:0.075 grdn:4.138 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 777 |
+
INFO 2025-05-29 13:51:11 ts/train.py:232 step:96K smpl:770K ep:739 epch:13.68 loss:0.074 grdn:4.059 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 778 |
+
INFO 2025-05-29 13:52:51 ts/train.py:232 step:96K smpl:771K ep:740 epch:13.71 loss:0.073 grdn:3.890 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 779 |
+
INFO 2025-05-29 13:54:30 ts/train.py:232 step:97K smpl:773K ep:742 epch:13.73 loss:0.074 grdn:3.929 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 780 |
+
INFO 2025-05-29 13:56:10 ts/train.py:232 step:97K smpl:774K ep:743 epch:13.76 loss:0.074 grdn:4.061 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 781 |
+
INFO 2025-05-29 13:57:49 ts/train.py:232 step:97K smpl:776K ep:745 epch:13.79 loss:0.072 grdn:3.944 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 782 |
+
INFO 2025-05-29 13:59:29 ts/train.py:232 step:97K smpl:778K ep:746 epch:13.82 loss:0.072 grdn:3.908 lr:1.0e-05 updt_s:0.497 data_s:0.000
|
| 783 |
+
INFO 2025-05-29 14:01:08 ts/train.py:232 step:97K smpl:779K ep:748 epch:13.85 loss:0.073 grdn:3.997 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 784 |
+
INFO 2025-05-29 14:02:48 ts/train.py:232 step:98K smpl:781K ep:749 epch:13.88 loss:0.072 grdn:3.951 lr:1.0e-05 updt_s:0.496 data_s:0.000
|
| 785 |
+
INFO 2025-05-29 14:04:28 ts/train.py:232 step:98K smpl:782K ep:751 epch:13.90 loss:0.072 grdn:4.008 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 786 |
+
INFO 2025-05-29 14:06:08 ts/train.py:232 step:98K smpl:784K ep:752 epch:13.93 loss:0.073 grdn:3.972 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 787 |
+
INFO 2025-05-29 14:07:48 ts/train.py:232 step:98K smpl:786K ep:754 epch:13.96 loss:0.075 grdn:4.149 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 788 |
+
INFO 2025-05-29 14:09:28 ts/train.py:232 step:98K smpl:787K ep:755 epch:13.99 loss:0.073 grdn:3.832 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 789 |
+
INFO 2025-05-29 14:11:09 ts/train.py:232 step:99K smpl:789K ep:757 epch:14.02 loss:0.072 grdn:3.933 lr:1.0e-05 updt_s:0.498 data_s:0.006
|
| 790 |
+
INFO 2025-05-29 14:12:49 ts/train.py:232 step:99K smpl:790K ep:759 epch:14.05 loss:0.071 grdn:3.747 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 791 |
+
INFO 2025-05-29 14:14:29 ts/train.py:232 step:99K smpl:792K ep:760 epch:14.08 loss:0.072 grdn:4.039 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 792 |
+
INFO 2025-05-29 14:16:09 ts/train.py:232 step:99K smpl:794K ep:762 epch:14.10 loss:0.072 grdn:3.748 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 793 |
+
INFO 2025-05-29 14:17:49 ts/train.py:232 step:99K smpl:795K ep:763 epch:14.13 loss:0.073 grdn:3.916 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 794 |
+
INFO 2025-05-29 14:19:29 ts/train.py:232 step:100K smpl:797K ep:765 epch:14.16 loss:0.071 grdn:3.607 lr:1.0e-05 updt_s:0.499 data_s:0.000
|
| 795 |
+
INFO 2025-05-29 14:21:09 ts/train.py:232 step:100K smpl:798K ep:766 epch:14.19 loss:0.070 grdn:3.810 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 796 |
+
INFO 2025-05-29 14:22:49 ts/train.py:232 step:100K smpl:800K ep:768 epch:14.22 loss:0.073 grdn:3.963 lr:1.0e-05 updt_s:0.498 data_s:0.000
|
| 797 |
+
INFO 2025-05-29 14:22:49 ts/train.py:241 Checkpoint policy after step 100000
|
| 798 |
+
INFO 2025-05-29 14:22:50 ts/train.py:283 End of training
|
wandb/run-20250529_003039-sam_fold_cloth_single/files/requirements.txt
ADDED
|
@@ -0,0 +1,682 @@
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|
| 1 |
+
lerobot==0.1.0
|
| 2 |
+
av==14.4.0
|
| 3 |
+
opencv-python==4.11.0.86
|
| 4 |
+
pfzy==0.3.4
|
| 5 |
+
feetech-servo-sdk==1.0.0
|
| 6 |
+
nvidia-cusparse-cu12==12.3.1.170
|
| 7 |
+
TorchCodec==0.2.0
|
| 8 |
+
mergedeep==1.3.4
|
| 9 |
+
nvidia-cuda-runtime-cu12==12.4.127
|
| 10 |
+
ffmpeg-python==0.2.0
|
| 11 |
+
draccus==0.10.0
|
| 12 |
+
pyserial==3.5
|
| 13 |
+
typing-inspect==0.9.0
|
| 14 |
+
nvidia-cudnn-cu12==9.1.0.70
|
| 15 |
+
numcodecs==0.16.1
|
| 16 |
+
pynput==1.8.1
|
| 17 |
+
deepdiff==8.5.0
|
| 18 |
+
mypy_extensions==1.1.0
|
| 19 |
+
gymnasium==0.29.1
|
| 20 |
+
datasets==3.6.0
|
| 21 |
+
nvidia-cuda-nvrtc-cu12==12.4.127
|
| 22 |
+
evdev==1.9.2
|
| 23 |
+
nvidia-cufft-cu12==11.2.1.3
|
| 24 |
+
donfig==0.8.1.post1
|
| 25 |
+
python-xlib==0.33
|
| 26 |
+
nvidia-nvjitlink-cu12==12.4.127
|
| 27 |
+
inquirerpy==0.3.4
|
| 28 |
+
fsspec==2025.3.0
|
| 29 |
+
nvidia-cuda-cupti-cu12==12.4.127
|
| 30 |
+
jsonlines==4.0.0
|
| 31 |
+
orderly-set==5.4.1
|
| 32 |
+
nvidia-cublas-cu12==12.4.5.8
|
| 33 |
+
pyyaml-include==1.4.1
|
| 34 |
+
pymunk==7.0.0
|
| 35 |
+
zarr==3.0.8
|
| 36 |
+
crc32c==2.7.1
|
| 37 |
+
rerun-sdk==0.23.3
|
| 38 |
+
pyzmq==26.4.0
|
| 39 |
+
nvidia-cusolver-cu12==11.6.1.9
|
| 40 |
+
nvidia-curand-cu12==10.3.5.147
|
| 41 |
+
google-colab==1.0.0
|
| 42 |
+
uritemplate==4.1.1
|
| 43 |
+
scs==3.2.7.post2
|
| 44 |
+
bokeh==3.7.3
|
| 45 |
+
openai==1.81.0
|
| 46 |
+
python-louvain==0.16
|
| 47 |
+
google-cloud-language==2.17.1
|
| 48 |
+
PyOpenGL==3.1.9
|
| 49 |
+
tensorboard==2.18.0
|
| 50 |
+
language_data==1.3.0
|
| 51 |
+
cuml-cu12==25.2.1
|
| 52 |
+
toml==0.10.2
|
| 53 |
+
missingno==0.5.2
|
| 54 |
+
blinker==1.9.0
|
| 55 |
+
graphviz==0.20.3
|
| 56 |
+
Bottleneck==1.4.2
|
| 57 |
+
dask-cuda==25.2.0
|
| 58 |
+
iniconfig==2.1.0
|
| 59 |
+
idna==3.10
|
| 60 |
+
tensorflow==2.18.0
|
| 61 |
+
hpack==4.1.0
|
| 62 |
+
torchtune==0.6.1
|
| 63 |
+
oauthlib==3.2.2
|
| 64 |
+
cloudpathlib==0.21.1
|
| 65 |
+
backports.tarfile==1.2.0
|
| 66 |
+
GitPython==3.1.44
|
| 67 |
+
sphinxcontrib-serializinghtml==2.0.0
|
| 68 |
+
scikit-learn==1.6.1
|
| 69 |
+
html5lib==1.1
|
| 70 |
+
betterproto==2.0.0b6
|
| 71 |
+
xxhash==3.5.0
|
| 72 |
+
srsly==2.5.1
|
| 73 |
+
libraft-cu12==25.2.0
|
| 74 |
+
tensorflow-probability==0.25.0
|
| 75 |
+
nbclassic==1.3.1
|
| 76 |
+
tensorboard-data-server==0.7.2
|
| 77 |
+
google-cloud-functions==1.20.3
|
| 78 |
+
tf_keras==2.18.0
|
| 79 |
+
pygame==2.6.1
|
| 80 |
+
mistune==3.1.3
|
| 81 |
+
pyspark==3.5.1
|
| 82 |
+
MarkupSafe==3.0.2
|
| 83 |
+
grpclib==0.4.8
|
| 84 |
+
gcsfs==2025.3.2
|
| 85 |
+
nvidia-ml-py==12.575.51
|
| 86 |
+
pymystem3==0.2.0
|
| 87 |
+
notebook_shim==0.2.4
|
| 88 |
+
argon2-cffi==23.1.0
|
| 89 |
+
jeepney==0.9.0
|
| 90 |
+
zict==3.0.0
|
| 91 |
+
ibis-framework==9.5.0
|
| 92 |
+
h5py==3.13.0
|
| 93 |
+
intel-cmplr-lib-ur==2025.1.1
|
| 94 |
+
openpyxl==3.1.5
|
| 95 |
+
soupsieve==2.7
|
| 96 |
+
google-cloud-core==2.4.3
|
| 97 |
+
fastjsonschema==2.21.1
|
| 98 |
+
google-cloud-resource-manager==1.14.2
|
| 99 |
+
pynndescent==0.5.13
|
| 100 |
+
ipython-sql==0.5.0
|
| 101 |
+
community==1.0.0b1
|
| 102 |
+
curl_cffi==0.11.1
|
| 103 |
+
distributed-ucxx-cu12==0.42.0
|
| 104 |
+
hf_transfer==0.1.9
|
| 105 |
+
sortedcontainers==2.4.0
|
| 106 |
+
lxml==5.4.0
|
| 107 |
+
distro==1.9.0
|
| 108 |
+
keras==3.8.0
|
| 109 |
+
colorcet==3.1.0
|
| 110 |
+
cmdstanpy==1.2.5
|
| 111 |
+
scooby==0.10.1
|
| 112 |
+
jaxlib==0.5.1
|
| 113 |
+
propcache==0.3.1
|
| 114 |
+
CacheControl==0.14.3
|
| 115 |
+
greenlet==3.2.2
|
| 116 |
+
tf-slim==1.1.0
|
| 117 |
+
pyviz_comms==3.0.4
|
| 118 |
+
cyipopt==1.5.0
|
| 119 |
+
langchain-text-splitters==0.3.8
|
| 120 |
+
dm-tree==0.1.9
|
| 121 |
+
panel==1.7.0
|
| 122 |
+
cmake==3.31.6
|
| 123 |
+
sniffio==1.3.1
|
| 124 |
+
Sphinx==8.2.3
|
| 125 |
+
gitdb==4.0.12
|
| 126 |
+
mdurl==0.1.2
|
| 127 |
+
tensorflow-datasets==4.9.8
|
| 128 |
+
nvtx==0.2.11
|
| 129 |
+
tokenizers==0.21.1
|
| 130 |
+
tifffile==2025.5.21
|
| 131 |
+
pydantic_core==2.33.2
|
| 132 |
+
gym-notices==0.0.8
|
| 133 |
+
websockets==15.0.1
|
| 134 |
+
pyarrow==18.1.0
|
| 135 |
+
geographiclib==2.0
|
| 136 |
+
ipyparallel==8.8.0
|
| 137 |
+
astropy==7.1.0
|
| 138 |
+
easydict==1.13
|
| 139 |
+
cufflinks==0.17.3
|
| 140 |
+
proto-plus==1.26.1
|
| 141 |
+
multidict==6.4.4
|
| 142 |
+
holidays==0.73
|
| 143 |
+
albumentations==2.0.7
|
| 144 |
+
numexpr==2.10.2
|
| 145 |
+
ndindex==1.10.0
|
| 146 |
+
pandas-gbq==0.29.0
|
| 147 |
+
flax==0.10.6
|
| 148 |
+
sqlparse==0.5.3
|
| 149 |
+
opt_einsum==3.4.0
|
| 150 |
+
cuvs-cu12==25.2.1
|
| 151 |
+
patsy==1.0.1
|
| 152 |
+
matplotlib-venn==1.1.2
|
| 153 |
+
platformdirs==4.3.8
|
| 154 |
+
gin-config==0.5.0
|
| 155 |
+
pytest==8.3.5
|
| 156 |
+
jupyter-client==6.1.12
|
| 157 |
+
aiosignal==1.3.2
|
| 158 |
+
spacy==3.8.6
|
| 159 |
+
statsmodels==0.14.4
|
| 160 |
+
websocket-client==1.8.0
|
| 161 |
+
kaggle==1.7.4.5
|
| 162 |
+
libcugraph-cu12==25.2.0
|
| 163 |
+
Markdown==3.8
|
| 164 |
+
python-box==7.3.2
|
| 165 |
+
immutabledict==4.2.1
|
| 166 |
+
progressbar2==4.5.0
|
| 167 |
+
lightgbm==4.5.0
|
| 168 |
+
fastcore==1.7.29
|
| 169 |
+
orjson==3.10.18
|
| 170 |
+
distributed==2024.12.1
|
| 171 |
+
polars==1.21.0
|
| 172 |
+
pyerfa==2.0.1.5
|
| 173 |
+
contourpy==1.3.2
|
| 174 |
+
Werkzeug==3.1.3
|
| 175 |
+
pluggy==1.6.0
|
| 176 |
+
chardet==5.2.0
|
| 177 |
+
bigquery-magics==0.9.0
|
| 178 |
+
dill==0.3.7
|
| 179 |
+
pycocotools==2.0.8
|
| 180 |
+
babel==2.17.0
|
| 181 |
+
altair==5.5.0
|
| 182 |
+
murmurhash==1.0.12
|
| 183 |
+
pandas-datareader==0.10.0
|
| 184 |
+
gdown==5.2.0
|
| 185 |
+
en_core_web_sm==3.8.0
|
| 186 |
+
scipy==1.15.3
|
| 187 |
+
joblib==1.5.0
|
| 188 |
+
humanize==4.12.3
|
| 189 |
+
defusedxml==0.7.1
|
| 190 |
+
tenacity==9.1.2
|
| 191 |
+
simplejson==3.20.1
|
| 192 |
+
roman-numerals-py==3.1.0
|
| 193 |
+
click==8.2.1
|
| 194 |
+
pandas==2.2.2
|
| 195 |
+
stringzilla==3.12.5
|
| 196 |
+
dask-expr==1.1.21
|
| 197 |
+
nest-asyncio==1.6.0
|
| 198 |
+
matplotlib-inline==0.1.7
|
| 199 |
+
opencv-python-headless==4.11.0.86
|
| 200 |
+
cymem==2.0.11
|
| 201 |
+
jieba==0.42.1
|
| 202 |
+
pandas-stubs==2.2.2.240909
|
| 203 |
+
prettytable==3.16.0
|
| 204 |
+
nx-cugraph-cu12==25.2.0
|
| 205 |
+
httpcore==1.0.9
|
| 206 |
+
mpmath==1.3.0
|
| 207 |
+
chex==0.1.89
|
| 208 |
+
geopy==2.4.1
|
| 209 |
+
peewee==3.18.1
|
| 210 |
+
pylibcudf-cu12==25.2.1
|
| 211 |
+
sphinxcontrib-devhelp==2.0.0
|
| 212 |
+
seaborn==0.13.2
|
| 213 |
+
langchain==0.3.25
|
| 214 |
+
pylibraft-cu12==25.2.0
|
| 215 |
+
pycryptodomex==3.23.0
|
| 216 |
+
Pygments==2.19.1
|
| 217 |
+
nbformat==5.10.4
|
| 218 |
+
grpc-interceptor==0.15.4
|
| 219 |
+
geopandas==1.0.1
|
| 220 |
+
cramjam==2.10.0
|
| 221 |
+
pynvml==12.0.0
|
| 222 |
+
importlib_resources==6.5.2
|
| 223 |
+
torchaudio==2.6.0+cu124
|
| 224 |
+
nvidia-cuda-nvcc-cu12==12.5.82
|
| 225 |
+
build==1.2.2.post1
|
| 226 |
+
hyperframe==6.1.0
|
| 227 |
+
docker-pycreds==0.4.0
|
| 228 |
+
timm==1.0.15
|
| 229 |
+
tweepy==4.15.0
|
| 230 |
+
snowballstemmer==3.0.1
|
| 231 |
+
locket==1.0.0
|
| 232 |
+
tqdm==4.67.1
|
| 233 |
+
wandb==0.19.11
|
| 234 |
+
mlxtend==0.23.4
|
| 235 |
+
hyperopt==0.2.7
|
| 236 |
+
typeguard==4.4.2
|
| 237 |
+
rmm-cu12==25.2.0
|
| 238 |
+
tensorstore==0.1.74
|
| 239 |
+
tornado==6.4.2
|
| 240 |
+
python-dateutil==2.9.0.post0
|
| 241 |
+
ipykernel==6.17.1
|
| 242 |
+
imagesize==1.4.1
|
| 243 |
+
nvidia-cusparselt-cu12==0.6.2
|
| 244 |
+
transformers==4.52.2
|
| 245 |
+
jupyter-server==1.16.0
|
| 246 |
+
torch==2.6.0+cu124
|
| 247 |
+
pyOpenSSL==24.2.1
|
| 248 |
+
aiohttp==3.11.15
|
| 249 |
+
docutils==0.21.2
|
| 250 |
+
huggingface-hub==0.31.4
|
| 251 |
+
flatbuffers==25.2.10
|
| 252 |
+
yarl==1.20.0
|
| 253 |
+
bigframes==2.4.0
|
| 254 |
+
langcodes==3.5.0
|
| 255 |
+
pycairo==1.28.0
|
| 256 |
+
peft==0.15.2
|
| 257 |
+
wasabi==1.1.3
|
| 258 |
+
moviepy==1.0.3
|
| 259 |
+
certifi==2025.4.26
|
| 260 |
+
torchvision==0.21.0+cu124
|
| 261 |
+
tzdata==2025.2
|
| 262 |
+
google-cloud-bigquery==3.33.0
|
| 263 |
+
google-cloud-datastore==2.21.0
|
| 264 |
+
tiktoken==0.9.0
|
| 265 |
+
keras-hub==0.18.1
|
| 266 |
+
jupyterlab_pygments==0.3.0
|
| 267 |
+
prophet==1.1.6
|
| 268 |
+
google-cloud-bigquery-storage==2.31.0
|
| 269 |
+
google-auth-oauthlib==1.2.2
|
| 270 |
+
PyYAML==6.0.2
|
| 271 |
+
narwhals==1.40.0
|
| 272 |
+
google-genai==1.16.1
|
| 273 |
+
cachetools==5.5.2
|
| 274 |
+
jupyter-console==6.1.0
|
| 275 |
+
music21==9.3.0
|
| 276 |
+
jsonschema==4.23.0
|
| 277 |
+
pickleshare==0.7.5
|
| 278 |
+
cloudpickle==3.1.1
|
| 279 |
+
rapids-dask-dependency==25.2.0
|
| 280 |
+
PyWavelets==1.8.0
|
| 281 |
+
jsonpatch==1.33
|
| 282 |
+
ipython-genutils==0.2.0
|
| 283 |
+
google-auth==2.38.0
|
| 284 |
+
kagglehub==0.3.12
|
| 285 |
+
ucx-py-cu12==0.42.0
|
| 286 |
+
array_record==0.7.2
|
| 287 |
+
dlib==19.24.6
|
| 288 |
+
ipywidgets==7.7.1
|
| 289 |
+
ipyleaflet==0.19.2
|
| 290 |
+
alabaster==1.0.0
|
| 291 |
+
pydotplus==2.0.2
|
| 292 |
+
keyring==25.6.0
|
| 293 |
+
libucxx-cu12==0.42.0
|
| 294 |
+
sphinxcontrib-htmlhelp==2.1.0
|
| 295 |
+
fastprogress==1.0.3
|
| 296 |
+
pydata-google-auth==1.9.1
|
| 297 |
+
importlib_metadata==8.7.0
|
| 298 |
+
notebook==6.5.7
|
| 299 |
+
anyio==4.9.0
|
| 300 |
+
albucore==0.0.24
|
| 301 |
+
rpds-py==0.25.1
|
| 302 |
+
ale-py==0.11.0
|
| 303 |
+
pynvjitlink-cu12==0.6.0
|
| 304 |
+
shapely==2.1.1
|
| 305 |
+
jaraco.context==6.0.1
|
| 306 |
+
clarabel==0.10.0
|
| 307 |
+
fastai==2.7.19
|
| 308 |
+
catalogue==2.0.10
|
| 309 |
+
libcuvs-cu12==25.2.1
|
| 310 |
+
psutil==5.9.5
|
| 311 |
+
tensorflow_decision_forests==1.11.0
|
| 312 |
+
jsonpointer==3.0.0
|
| 313 |
+
preshed==3.0.9
|
| 314 |
+
nvidia-nvcomp-cu12==4.2.0.11
|
| 315 |
+
ml-dtypes==0.4.1
|
| 316 |
+
cvxopt==1.3.2
|
| 317 |
+
imutils==0.5.4
|
| 318 |
+
tsfresh==0.21.0
|
| 319 |
+
matplotlib==3.10.0
|
| 320 |
+
tcmlib==1.3.0
|
| 321 |
+
numba-cuda==0.2.0
|
| 322 |
+
annotated-types==0.7.0
|
| 323 |
+
protobuf==5.29.4
|
| 324 |
+
thinc==8.3.6
|
| 325 |
+
orbax-checkpoint==0.11.13
|
| 326 |
+
requests==2.32.3
|
| 327 |
+
cycler==0.12.1
|
| 328 |
+
gym==0.25.2
|
| 329 |
+
confection==0.1.5
|
| 330 |
+
text-unidecode==1.3
|
| 331 |
+
nbclient==0.10.2
|
| 332 |
+
jaraco.classes==3.4.0
|
| 333 |
+
libkvikio-cu12==25.2.1
|
| 334 |
+
astunparse==1.6.3
|
| 335 |
+
marisa-trie==1.2.1
|
| 336 |
+
beautifulsoup4==4.13.4
|
| 337 |
+
blobfile==3.0.0
|
| 338 |
+
dask==2024.12.1
|
| 339 |
+
tensorflow-hub==0.16.1
|
| 340 |
+
cuda-python==12.6.2.post1
|
| 341 |
+
ipyfilechooser==0.6.0
|
| 342 |
+
shellingham==1.5.4
|
| 343 |
+
imageio-ffmpeg==0.6.0
|
| 344 |
+
tbb==2022.1.0
|
| 345 |
+
google-cloud-translate==3.20.2
|
| 346 |
+
stumpy==1.13.0
|
| 347 |
+
portpicker==1.5.2
|
| 348 |
+
safetensors==0.5.3
|
| 349 |
+
pygit2==1.18.0
|
| 350 |
+
jupyter-leaflet==0.19.2
|
| 351 |
+
sklearn-compat==0.1.3
|
| 352 |
+
antlr4-python3-runtime==4.9.3
|
| 353 |
+
PyGObject==3.42.0
|
| 354 |
+
cryptography==43.0.3
|
| 355 |
+
db-dtypes==1.4.3
|
| 356 |
+
dataproc-spark-connect==0.7.4
|
| 357 |
+
folium==0.19.6
|
| 358 |
+
editdistance==0.8.1
|
| 359 |
+
omegaconf==2.3.0
|
| 360 |
+
cffi==1.17.1
|
| 361 |
+
rich==13.9.4
|
| 362 |
+
decorator==4.4.2
|
| 363 |
+
lazy_loader==0.4
|
| 364 |
+
ipyevents==2.0.2
|
| 365 |
+
xlrd==2.0.1
|
| 366 |
+
stanio==0.5.1
|
| 367 |
+
numpy==2.0.2
|
| 368 |
+
typer==0.15.3
|
| 369 |
+
charset-normalizer==3.4.2
|
| 370 |
+
packaging==24.2
|
| 371 |
+
SQLAlchemy==2.0.41
|
| 372 |
+
libpysal==4.13.0
|
| 373 |
+
tblib==3.1.0
|
| 374 |
+
PySocks==1.7.1
|
| 375 |
+
einops==0.8.1
|
| 376 |
+
xgboost==2.1.4
|
| 377 |
+
google-cloud-firestore==2.20.2
|
| 378 |
+
PyDrive2==1.21.3
|
| 379 |
+
nvidia-nvtx-cu12==12.4.127
|
| 380 |
+
duckdb==1.2.2
|
| 381 |
+
pillow==11.2.1
|
| 382 |
+
proglog==0.1.12
|
| 383 |
+
param==2.2.0
|
| 384 |
+
filelock==3.18.0
|
| 385 |
+
docstring_parser==0.16
|
| 386 |
+
plotly==5.24.1
|
| 387 |
+
uc-micro-py==1.0.3
|
| 388 |
+
google-cloud-iam==2.19.0
|
| 389 |
+
Flask==3.1.1
|
| 390 |
+
dask-cudf-cu12==25.2.2
|
| 391 |
+
python-utils==3.9.1
|
| 392 |
+
six==1.17.0
|
| 393 |
+
rsa==4.9.1
|
| 394 |
+
entrypoints==0.4
|
| 395 |
+
backcall==0.2.0
|
| 396 |
+
urllib3==2.4.0
|
| 397 |
+
Send2Trash==1.8.3
|
| 398 |
+
cupy-cuda12x==13.3.0
|
| 399 |
+
terminado==0.18.1
|
| 400 |
+
numba==0.60.0
|
| 401 |
+
jaraco.functools==4.1.0
|
| 402 |
+
requests-toolbelt==1.0.0
|
| 403 |
+
setproctitle==1.3.6
|
| 404 |
+
zstandard==0.23.0
|
| 405 |
+
jax-cuda12-pjrt==0.5.1
|
| 406 |
+
multiprocess==0.70.15
|
| 407 |
+
google-api-python-client==2.169.0
|
| 408 |
+
highspy==1.10.0
|
| 409 |
+
py-cpuinfo==9.0.0
|
| 410 |
+
et_xmlfile==2.0.0
|
| 411 |
+
regex==2024.11.6
|
| 412 |
+
httpimport==1.4.1
|
| 413 |
+
sentence-transformers==4.1.0
|
| 414 |
+
mkl==2025.0.1
|
| 415 |
+
nibabel==5.3.2
|
| 416 |
+
torchsummary==1.5.1
|
| 417 |
+
Farama-Notifications==0.0.4
|
| 418 |
+
toolz==0.12.1
|
| 419 |
+
mizani==0.13.5
|
| 420 |
+
atpublic==5.1
|
| 421 |
+
markdown-it-py==3.0.0
|
| 422 |
+
pylibcugraph-cu12==25.2.0
|
| 423 |
+
types-pytz==2025.2.0.20250516
|
| 424 |
+
wordcloud==1.9.4
|
| 425 |
+
spanner-graph-notebook==1.1.6
|
| 426 |
+
ipython==7.34.0
|
| 427 |
+
geemap==0.35.3
|
| 428 |
+
namex==0.0.9
|
| 429 |
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nvidia-nccl-cu12==2.21.5
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google-cloud-dataproc==5.18.1
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google-generativeai==0.8.5
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frozendict==2.4.6
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firebase-admin==6.8.0
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future==1.0.0
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multipledispatch==1.0.0
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prometheus_client==0.22.0
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sklearn-pandas==2.2.0
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ipytree==0.2.2
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tabulate==0.9.0
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torchdata==0.11.0
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branca==0.8.1
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google-cloud-bigquery-connection==1.18.2
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cons==0.4.6
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ply==3.11
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requests-oauthlib==2.0.0
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fastrlock==0.8.3
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rpy2==3.5.17
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torchao==0.10.0
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google==2.0.3
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sqlglot==25.20.2
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intel-openmp==2025.1.1
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sympy==1.13.1
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pyparsing==3.2.3
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python-slugify==8.0.4
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imageio==2.37.0
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tensorflow-io-gcs-filesystem==0.37.1
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pyasn1==0.6.1
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google-auth-httplib2==0.2.0
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grpcio-status==1.71.0
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sphinxcontrib-qthelp==2.0.0
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google-crc32c==1.7.1
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keras-nlp==0.18.1
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datascience==0.17.6
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ratelim==0.1.6
|
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parso==0.8.4
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blosc2==3.3.3
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spacy-loggers==1.0.5
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accelerate==1.7.0
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google-cloud-spanner==3.54.0
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linkify-it-py==2.0.3
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xarray-einstats==0.8.0
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spacy-legacy==3.0.12
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logical-unification==0.4.6
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jsonpickle==4.1.0
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dopamine_rl==4.1.2
|
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libcudf-cu12==25.2.1
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umap-learn==0.5.7
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soundfile==0.13.1
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scikit-image==0.25.2
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kiwisolver==1.4.8
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httplib2==0.22.0
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grpcio==1.71.0
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google-ai-generativelanguage==0.6.15
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yfinance==0.2.61
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plotnine==0.14.5
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python-snappy==0.7.3
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| 558 |
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earthengine-api==1.5.15
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Jinja2==3.1.6
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google-cloud-aiplatform==1.93.1
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libcuml-cu12==25.2.1
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|
| 563 |
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grpc-google-iam-v1==0.14.2
|
| 564 |
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audioread==3.0.1
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jax-cuda12-plugin==0.5.1
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| 566 |
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bqplot==0.12.45
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raft-dask-cu12==25.2.0
|
| 568 |
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httpx==0.28.1
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| 569 |
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tensorflow-text==2.18.1
|
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tensorflow-metadata==1.17.1
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jsonschema-specifications==2025.4.1
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cvxpy==1.6.5
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textblob==0.19.0
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PyJWT==2.10.1
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libclang==18.1.1
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referencing==0.36.2
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jupyter_kernel_gateway==2.5.2
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astropy-iers-data==0.2025.5.19.0.38.36
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ucxx-cu12==0.42.0
|
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pyasn1_modules==0.4.2
|
| 581 |
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pydot==3.0.4
|
| 582 |
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imbalanced-learn==0.13.0
|
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vega-datasets==0.9.0
|
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promise==2.3
|
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sphinxcontrib-applehelp==2.0.0
|
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jiter==0.10.0
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partd==1.4.2
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blis==1.3.0
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| 589 |
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colorlover==0.3.0
|
| 590 |
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triton==3.2.0
|
| 591 |
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tinycss2==1.4.0
|
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pyogrio==0.11.0
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Cython==3.0.12
|
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sphinxcontrib-jsmath==1.0.1
|
| 595 |
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optax==0.2.4
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| 596 |
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nbconvert==7.16.6
|
| 597 |
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absl-py==1.4.0
|
| 598 |
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gspread==6.2.1
|
| 599 |
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etils==1.12.2
|
| 600 |
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natsort==8.4.0
|
| 601 |
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opencv-contrib-python==4.11.0.86
|
| 602 |
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pandocfilters==1.5.1
|
| 603 |
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psycopg2==2.9.10
|
| 604 |
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h5netcdf==1.6.1
|
| 605 |
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pyomo==6.9.2
|
| 606 |
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itsdangerous==2.2.0
|
| 607 |
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wheel==0.45.1
|
| 608 |
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google-resumable-media==2.7.2
|
| 609 |
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pydantic==2.11.4
|
| 610 |
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traittypes==0.2.1
|
| 611 |
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keyrings.google-artifactregistry-auth==1.1.2
|
| 612 |
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autograd==1.8.0
|
| 613 |
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googledrivedownloader==1.1.0
|
| 614 |
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glob2==0.7
|
| 615 |
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tables==3.10.2
|
| 616 |
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frozenlist==1.6.0
|
| 617 |
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tzlocal==5.3.1
|
| 618 |
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pathlib==1.0.1
|
| 619 |
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nltk==3.9.1
|
| 620 |
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webencodings==0.5.1
|
| 621 |
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networkx==3.4.2
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| 622 |
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jupyterlab_widgets==3.0.15
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| 623 |
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pyshp==2.3.1
|
| 624 |
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pytz==2025.2
|
| 625 |
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more-itertools==10.7.0
|
| 626 |
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gspread-dataframe==4.0.0
|
| 627 |
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argon2-cffi-bindings==21.2.0
|
| 628 |
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cudf-cu12==25.2.1
|
| 629 |
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typing_extensions==4.13.2
|
| 630 |
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pyproject_hooks==1.2.0
|
| 631 |
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mdit-py-plugins==0.4.2
|
| 632 |
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optree==0.15.0
|
| 633 |
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GDAL==3.8.4
|
| 634 |
+
pyperclip==1.9.0
|
| 635 |
+
SecretStorage==3.3.3
|
| 636 |
+
diffusers==0.33.1
|
| 637 |
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python-apt==0.0.0
|
| 638 |
+
setuptools==75.2.0
|
| 639 |
+
types-setuptools==80.8.0.20250521
|
| 640 |
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pip==24.1.2
|
| 641 |
+
requirements-parser==0.9.0
|
| 642 |
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keyring==23.5.0
|
| 643 |
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lazr.restfulclient==0.14.4
|
| 644 |
+
httplib2==0.20.2
|
| 645 |
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blinker==1.4
|
| 646 |
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lazr.uri==1.0.6
|
| 647 |
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PyJWT==2.3.0
|
| 648 |
+
six==1.16.0
|
| 649 |
+
more-itertools==8.10.0
|
| 650 |
+
pyparsing==2.4.7
|
| 651 |
+
PyGObject==3.42.1
|
| 652 |
+
importlib-metadata==4.6.4
|
| 653 |
+
zipp==1.0.0
|
| 654 |
+
dbus-python==1.2.18
|
| 655 |
+
launchpadlib==1.10.16
|
| 656 |
+
python-apt==2.4.0+ubuntu4
|
| 657 |
+
distro==1.7.0
|
| 658 |
+
wadllib==1.3.6
|
| 659 |
+
Mako==1.1.3
|
| 660 |
+
oauthlib==3.2.0
|
| 661 |
+
MarkupSafe==2.0.1
|
| 662 |
+
SecretStorage==3.3.1
|
| 663 |
+
cryptography==3.4.8
|
| 664 |
+
Markdown==3.3.6
|
| 665 |
+
jeepney==0.7.1
|
| 666 |
+
backports.tarfile==1.2.0
|
| 667 |
+
typing_extensions==4.12.2
|
| 668 |
+
zipp==3.19.2
|
| 669 |
+
jaraco.functools==4.0.1
|
| 670 |
+
inflect==7.3.1
|
| 671 |
+
jaraco.text==3.12.1
|
| 672 |
+
importlib_metadata==8.0.0
|
| 673 |
+
wheel==0.43.0
|
| 674 |
+
importlib_resources==6.4.0
|
| 675 |
+
tomli==2.0.1
|
| 676 |
+
platformdirs==4.2.2
|
| 677 |
+
jaraco.context==5.3.0
|
| 678 |
+
typeguard==4.3.0
|
| 679 |
+
packaging==24.1
|
| 680 |
+
autocommand==2.2.2
|
| 681 |
+
jaraco.collections==5.1.0
|
| 682 |
+
more-itertools==10.3.0
|
wandb/run-20250529_003039-sam_fold_cloth_single/files/wandb-metadata.json
ADDED
|
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"os": "Linux-6.1.123+-x86_64-with-glibc2.35",
|
| 3 |
+
"python": "CPython 3.11.12",
|
| 4 |
+
"startedAt": "2025-05-29T00:30:39.110309Z",
|
| 5 |
+
"args": [
|
| 6 |
+
"--dataset.repo_id=girardijp/sam_fold_cloth_single",
|
| 7 |
+
"--policy.type=act",
|
| 8 |
+
"--output_dir=outputs/train/sam_fold_cloth_single",
|
| 9 |
+
"--job_name=sam_fold_cloth_single",
|
| 10 |
+
"--wandb.enable=true",
|
| 11 |
+
"--save_freq=50000",
|
| 12 |
+
"--resume=false",
|
| 13 |
+
"--wandb.run_id=sam_fold_cloth_single"
|
| 14 |
+
],
|
| 15 |
+
"program": "/root/lerobot/lerobot/scripts/train.py",
|
| 16 |
+
"root": "outputs/train/sam_fold_cloth_single",
|
| 17 |
+
"host": "d73b74453142",
|
| 18 |
+
"executable": "/usr/bin/python3",
|
| 19 |
+
"cpu_count": 6,
|
| 20 |
+
"cpu_count_logical": 12,
|
| 21 |
+
"gpu": "NVIDIA L4",
|
| 22 |
+
"gpu_count": 1,
|
| 23 |
+
"disk": {
|
| 24 |
+
"/": {
|
| 25 |
+
"total": "253055008768",
|
| 26 |
+
"used": "45488119808"
|
| 27 |
+
}
|
| 28 |
+
},
|
| 29 |
+
"memory": {
|
| 30 |
+
"total": "56865857536"
|
| 31 |
+
},
|
| 32 |
+
"cpu": {
|
| 33 |
+
"count": 6,
|
| 34 |
+
"countLogical": 12
|
| 35 |
+
},
|
| 36 |
+
"gpu_nvidia": [
|
| 37 |
+
{
|
| 38 |
+
"name": "NVIDIA L4",
|
| 39 |
+
"memoryTotal": "24152899584",
|
| 40 |
+
"cudaCores": 7424,
|
| 41 |
+
"architecture": "Ada"
|
| 42 |
+
}
|
| 43 |
+
],
|
| 44 |
+
"cudaVersion": "12.4"
|
| 45 |
+
}
|
wandb/run-20250529_003039-sam_fold_cloth_single/files/wandb-summary.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"train/episodes":767.7543186180422,"_step":100000,"train/grad_norm":3.9630794882774354,"train/dataloading_s":0.00031903395494737197,"train/update_s":0.4982985749646832,"train/epochs":14.217672567000783,"train/loss":0.07256632378324866,"_wandb":{"runtime":49932},"_runtime":49932.215825378,"_timestamp":1.7485285690454702e+09,"train/kld_loss":6.259605288505554e-05,"train/lr":1.0000000000000021e-05,"train/steps":100000,"train/samples":800000,"train/l1_loss":0.07024721056222916}
|
wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug-core.log
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"time":"2025-05-29T00:30:39.070483114Z","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmpo_l96_78/port-2917.txt","pid":2917,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false,"enable-dcgm-profiling":false}
|
| 2 |
+
{"time":"2025-05-29T00:30:39.077637325Z","level":"INFO","msg":"Will exit if parent process dies.","ppid":2917}
|
| 3 |
+
{"time":"2025-05-29T00:30:39.079812376Z","level":"INFO","msg":"server is running","addr":{"IP":"127.0.0.1","Port":32955,"Zone":""}}
|
| 4 |
+
{"time":"2025-05-29T00:30:39.099504267Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"127.0.0.1:58556"}
|
| 5 |
+
{"time":"2025-05-29T00:30:39.113033332Z","level":"INFO","msg":"handleInformInit: received","streamId":"sam_fold_cloth_single","id":"127.0.0.1:58556"}
|
| 6 |
+
{"time":"2025-05-29T00:30:39.438640766Z","level":"INFO","msg":"handleInformInit: stream started","streamId":"sam_fold_cloth_single","id":"127.0.0.1:58556"}
|
| 7 |
+
{"time":"2025-05-29T14:22:51.326024182Z","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"127.0.0.1:58556"}
|
| 8 |
+
{"time":"2025-05-29T14:22:51.326092787Z","level":"INFO","msg":"connection: closing","id":"127.0.0.1:58556"}
|
| 9 |
+
{"time":"2025-05-29T14:22:51.326192906Z","level":"INFO","msg":"connection: closed successfully","id":"127.0.0.1:58556"}
|
| 10 |
+
{"time":"2025-05-29T14:22:51.326119511Z","level":"INFO","msg":"server is shutting down"}
|
| 11 |
+
{"time":"2025-05-29T14:23:05.848243771Z","level":"ERROR","msg":"processOutgoingData: flush error","error":"write tcp 127.0.0.1:32955->127.0.0.1:58556: use of closed network connection","id":"127.0.0.1:58556"}
|
| 12 |
+
{"time":"2025-05-29T14:23:06.144086885Z","level":"INFO","msg":"handleInformTeardown: server shutdown complete","id":"127.0.0.1:58556"}
|
| 13 |
+
{"time":"2025-05-29T14:23:06.144108212Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"127.0.0.1:58556"}
|
| 14 |
+
{"time":"2025-05-29T14:23:06.144123943Z","level":"INFO","msg":"server is closed"}
|
wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug-internal.log
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"time":"2025-05-29T00:30:39.113350824Z","level":"INFO","msg":"stream: starting","core version":"0.19.11","symlink path":"outputs/train/sam_fold_cloth_single/wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug-core.log"}
|
| 2 |
+
{"time":"2025-05-29T00:30:39.438587436Z","level":"INFO","msg":"created new stream","id":"sam_fold_cloth_single"}
|
| 3 |
+
{"time":"2025-05-29T00:30:39.438634484Z","level":"INFO","msg":"stream: started","id":"sam_fold_cloth_single"}
|
| 4 |
+
{"time":"2025-05-29T00:30:39.43868417Z","level":"INFO","msg":"writer: Do: started","stream_id":"sam_fold_cloth_single"}
|
| 5 |
+
{"time":"2025-05-29T00:30:39.43869285Z","level":"INFO","msg":"sender: started","stream_id":"sam_fold_cloth_single"}
|
| 6 |
+
{"time":"2025-05-29T00:30:39.438753599Z","level":"INFO","msg":"handler: started","stream_id":"sam_fold_cloth_single"}
|
| 7 |
+
{"time":"2025-05-29T00:30:39.706088017Z","level":"INFO","msg":"Starting system monitor"}
|
| 8 |
+
{"time":"2025-05-29T14:22:51.326120472Z","level":"INFO","msg":"stream: closing","id":"sam_fold_cloth_single"}
|
| 9 |
+
{"time":"2025-05-29T14:22:51.326158611Z","level":"INFO","msg":"Stopping system monitor"}
|
| 10 |
+
{"time":"2025-05-29T14:22:51.326206792Z","level":"INFO","msg":"Stopped system monitor"}
|
| 11 |
+
{"time":"2025-05-29T14:23:05.898454175Z","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"}
|
| 12 |
+
{"time":"2025-05-29T14:23:06.143936063Z","level":"INFO","msg":"handler: closed","stream_id":"sam_fold_cloth_single"}
|
| 13 |
+
{"time":"2025-05-29T14:23:06.143980463Z","level":"INFO","msg":"writer: Close: closed","stream_id":"sam_fold_cloth_single"}
|
| 14 |
+
{"time":"2025-05-29T14:23:06.144016547Z","level":"INFO","msg":"sender: closed","stream_id":"sam_fold_cloth_single"}
|
| 15 |
+
{"time":"2025-05-29T14:23:06.144063686Z","level":"INFO","msg":"stream: closed","id":"sam_fold_cloth_single"}
|
wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug.log
ADDED
|
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
2025-05-29 00:30:39,101 INFO MainThread:2917 [wandb_setup.py:_flush():70] Current SDK version is 0.19.11
|
| 2 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_setup.py:_flush():70] Configure stats pid to 2917
|
| 3 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_setup.py:_flush():70] Loading settings from /root/.config/wandb/settings
|
| 4 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_setup.py:_flush():70] Loading settings from /content/wandb/settings
|
| 5 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_setup.py:_flush():70] Loading settings from environment variables
|
| 6 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:setup_run_log_directory():724] Logging user logs to outputs/train/sam_fold_cloth_single/wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug.log
|
| 7 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:setup_run_log_directory():725] Logging internal logs to outputs/train/sam_fold_cloth_single/wandb/run-20250529_003039-sam_fold_cloth_single/logs/debug-internal.log
|
| 8 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:init():852] calling init triggers
|
| 9 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:init():857] wandb.init called with sweep_config: {}
|
| 10 |
+
config: {'dataset': {'repo_id': 'girardijp/sam_fold_cloth_single', 'root': None, 'episodes': None, 'image_transforms': {'enable': False, 'max_num_transforms': 3, 'random_order': False, 'tfs': {'brightness': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'brightness': [0.8, 1.2]}}, 'contrast': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'contrast': [0.8, 1.2]}}, 'saturation': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'saturation': [0.5, 1.5]}}, 'hue': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'hue': [-0.05, 0.05]}}, 'sharpness': {'weight': 1.0, 'type': 'SharpnessJitter', 'kwargs': {'sharpness': [0.5, 1.5]}}}}, 'revision': None, 'use_imagenet_stats': True, 'video_backend': 'torchcodec'}, 'env': None, 'policy': {'type': 'act', 'n_obs_steps': 1, 'normalization_mapping': {'VISUAL': <NormalizationMode.MEAN_STD: 'MEAN_STD'>, 'STATE': <NormalizationMode.MEAN_STD: 'MEAN_STD'>, 'ACTION': <NormalizationMode.MEAN_STD: 'MEAN_STD'>}, 'input_features': {}, 'output_features': {}, 'device': 'cuda', 'use_amp': False, 'chunk_size': 100, 'n_action_steps': 100, 'vision_backbone': 'resnet18', 'pretrained_backbone_weights': 'ResNet18_Weights.IMAGENET1K_V1', 'replace_final_stride_with_dilation': False, 'pre_norm': False, 'dim_model': 512, 'n_heads': 8, 'dim_feedforward': 3200, 'feedforward_activation': 'relu', 'n_encoder_layers': 4, 'n_decoder_layers': 1, 'use_vae': True, 'latent_dim': 32, 'n_vae_encoder_layers': 4, 'temporal_ensemble_coeff': None, 'dropout': 0.1, 'kl_weight': 10.0, 'optimizer_lr': 1e-05, 'optimizer_weight_decay': 0.0001, 'optimizer_lr_backbone': 1e-05}, 'output_dir': 'outputs/train/sam_fold_cloth_single', 'job_name': 'sam_fold_cloth_single', 'resume': False, 'seed': 1000, 'num_workers': 4, 'batch_size': 8, 'steps': 100000, 'eval_freq': 20000, 'log_freq': 200, 'save_checkpoint': True, 'save_freq': 50000, 'use_policy_training_preset': True, 'optimizer': {'type': 'adamw', 'lr': 1e-05, 'weight_decay': 0.0001, 'grad_clip_norm': 10.0, 'betas': [0.9, 0.999], 'eps': 1e-08}, 'scheduler': None, 'eval': {'n_episodes': 50, 'batch_size': 50, 'use_async_envs': False}, 'wandb': {'enable': True, 'disable_artifact': False, 'project': 'lerobot', 'entity': None, 'notes': None, 'run_id': 'sam_fold_cloth_single', 'mode': None}, '_wandb': {}}
|
| 11 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:init():893] starting backend
|
| 12 |
+
2025-05-29 00:30:39,102 INFO MainThread:2917 [wandb_init.py:init():897] sending inform_init request
|
| 13 |
+
2025-05-29 00:30:39,110 INFO MainThread:2917 [backend.py:_multiprocessing_setup():101] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
|
| 14 |
+
2025-05-29 00:30:39,110 INFO MainThread:2917 [wandb_init.py:init():907] backend started and connected
|
| 15 |
+
2025-05-29 00:30:39,112 INFO MainThread:2917 [wandb_init.py:init():1005] updated telemetry
|
| 16 |
+
2025-05-29 00:30:39,113 INFO MainThread:2917 [wandb_init.py:init():1029] communicating run to backend with 90.0 second timeout
|
| 17 |
+
2025-05-29 00:30:39,703 INFO MainThread:2917 [wandb_init.py:init():1104] starting run threads in backend
|
| 18 |
+
2025-05-29 00:30:40,104 INFO MainThread:2917 [wandb_run.py:_console_start():2573] atexit reg
|
| 19 |
+
2025-05-29 00:30:40,104 INFO MainThread:2917 [wandb_run.py:_redirect():2421] redirect: wrap_raw
|
| 20 |
+
2025-05-29 00:30:40,104 INFO MainThread:2917 [wandb_run.py:_redirect():2490] Wrapping output streams.
|
| 21 |
+
2025-05-29 00:30:40,104 INFO MainThread:2917 [wandb_run.py:_redirect():2513] Redirects installed.
|
| 22 |
+
2025-05-29 00:30:40,109 INFO MainThread:2917 [wandb_init.py:init():1150] run started, returning control to user process
|
| 23 |
+
2025-05-29 14:22:51,325 INFO MsgRouterThr:2917 [mailbox.py:close():129] [no run ID] Closing mailbox, abandoning 2 handles.
|
wandb/run-20250529_003039-sam_fold_cloth_single/run-sam_fold_cloth_single.wandb
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:32e2515ae0b05d6654c594e98315489a8d67e987049287c28fca55e9e0923e8e
|
| 3 |
+
size 3498842
|