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'dim_feedforward': 3200, 'feedforward_activation': 'relu', 'n_encoder_layers': 4, 'n_decoder_layers': 1, 'pre_norm': False, 'dropout': 0.1, 'use_vae': True, 'latent_dim': 32, 'n_vae_encoder_layers': 4, 'kl_weight': 10.0, 'temporal_ensemble_coeff': None}, 'environment_variables': {'OMP_NUM_THREADS': '32', 'HF_HOME': '/hfm/cache', 'TORCH_HOME': '/hfm/cache', 'HF_TOKEN': 'hf_...adRb', 'HF_LEROBOT_HOME': '/hfm/data/lerobot', 'WE_HOME': '/hfm', 'DATA_HOME': '/hfm/data', 'UV_CACHE_DIR': '/hfm/cache', 'WANDB_API_KEY': '90e...5c06', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'python', 'CUDA_VISIBLE_DEVICES': '6,7', 'WORLD_SIZE': '2', 'LOCAL_WORLD_SIZE': '2', 'RANK': '0', 'LOCAL_RANK': '0', 'MASTER_ADDR': 'nebula101', 'MASTER_PORT': '39399'}} +2026-01-23 22:48:54,448 INFO MainThread:3227745 [wandb_run.py:_finish():2287] finishing run jliu530-soochow-university/hfm/yuvvvmoo +2026-01-23 22:48:54,452 INFO MainThread:3227745 [wandb_run.py:_atexit_cleanup():2486] got exitcode: 0 +2026-01-23 22:48:54,452 INFO MainThread:3227745 [wandb_run.py:_restore():2468] restore +2026-01-23 22:48:54,453 INFO MainThread:3227745 [wandb_run.py:_restore():2474] restore done +2026-01-23 22:48:55,263 INFO MainThread:3227745 [wandb_run.py:_footer_sync_info():3862] logging synced files diff --git a/act/act-g1-real/act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230811/wandb/run-20260123_081132-yuvvvmoo/files/config.yaml b/act/act-g1-real/act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230811/wandb/run-20260123_081132-yuvvvmoo/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..72dded7a130433044e4923c089cb20cdd4a01620 --- /dev/null +++ b/act/act-g1-real/act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230811/wandb/run-20260123_081132-yuvvvmoo/files/config.yaml @@ -0,0 +1,457 @@ +_wandb: + value: + cli_version: 0.23.1 + e: + ll48ekgiyrd4ysguzqz0ktfublcn9te0: + args: + - data:gr00t + - data.transform.repack:gr00t-we + - data.transform.action-state:maxmin + - data.transform.model:act + - model:act + - --seed=2026 + - --exp=act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table + - --train.name=act-g1-real + - --log.report-to=wandb + - --wandb.entity=jliu530-soochow-university + - --train.data_parallel=ddp + - --train.mixed_precision=bf16 + - --train.train-batch-size=32 + - --train.gradient_accumulation_steps=1 + - --train.validation_steps=500 + - --train.val_num_batches=20 + - --train.max-training-steps=42000 + - --train.learning-rate=1e-4 + - --train.max-grad-norm=1.0 + - --train.lr_scheduler_kwargs.weight_decay=1e-6 + - --train.lr_scheduler_kwargs.betas + - "0.95" + - "0.999" + - --train.lr_scheduler_type=cosine + - --train.warmup-steps=1000 + - --train.warmup-ratio=None + - --train.checkpointing-steps=5000 + - --data.root_dir=/hfm/data/real_teleop_g1/lerobot + - --data.dataset_paths=Hold_lunch_bag_with_both_hands_and_squat_to_put_on_the_coffee_table + - --data.data_config=we.data.gr00t.data_config:WeUnitreeG1LocoDataConfig + - --data.transform.repack.pad-action-dim=36 + - --data.transform.repack.pad-state-dim=32 + - --data.transform.action-state.stat-path=dataset_statistics/g1-stats-Hold_lunch_bag_with_both_hands_and_squat_to_put_on_the_coffee_table.json + - --data.transform.action-state.stat-action-key=action + - --data.transform.action-state.stat-state-key=states + - --data.transform.action-state.normalize-state + - --data.transform.action-state.action-norm-type=bounds + - --data.transform.model.img-aug + - --data.action-chunk-size=100 + - --model.chunk-size=100 + - --model.n-action-steps=100 + - --model.action-dim=36 + - --model.state-dim=32 + - --model.use-vae + - --model.kl-weight=10.0 + codePath: scripts/train.py + codePathLocal: scripts/train.py + cpu_count: 128 + cpu_count_logical: 128 + cudaVersion: "12.9" + disk: + /: + total: "3934171283456" + used: "3649290387456" + email: jliu530@163.com + executable: /home/jliu/we_learn/.venv-pusht/bin/python3 + git: + commit: 00cea05aeb87e91ce3e0982bc9f6ca18b63adbed + remote: https://github.com/songlin/we_learn.git + gpu: NVIDIA A100-SXM4-80GB + gpu_count: 8 + gpu_nvidia: + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-02b51758-2aec-8c71-c751-92567fdb15ad + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-be645bb4-1ef0-15da-fc6b-53c5378650df + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-44868ccd-5809-61e2-f8cc-1dff8efc7eb1 + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-a12b8de3-fb12-2901-c3a2-1e0e2b050b7b + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-57787183-4904-b2c9-cf52-1c1af976b365 + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-e82d0fd0-9dcb-3d7c-9451-c4770ad6ddc3 + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-9a825fa3-60d0-3acf-6c79-67e9230ae24c + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-6fe02e5e-b886-9c02-e433-6237f3897dd8 + host: nebula101 + memory: + total: "1623176945664" + os: Linux-6.8.0-88-generic-x86_64-with-glibc2.39 + program: /home/jliu/we_learn/scripts/train.py + python: CPython 3.10.19 + root: /home/jliu/we_learn/.runs/act-g1-real/act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230811 + startedAt: "2026-01-23T08:11:32.735367Z" + writerId: ll48ekgiyrd4ysguzqz0ktfublcn9te0 + m: [] + python_version: 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OMP_NUM_THREADS: "32" + PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION: python + RANK: "0" + TORCH_HOME: /hfm/cache + UV_CACHE_DIR: /hfm/cache + WANDB_API_KEY: 90e...5c06 + WE_HOME: /hfm + WORLD_SIZE: "2" +eval: + value: false +exp: + value: act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table +log: + value: + log_freq: 100 + logging_dir: logs + report_to: wandb +model: + value: + action_dim: 36 + chunk_size: 100 + dim_feedforward: 3200 + dim_model: 512 + dropout: 0.1 + feedforward_activation: relu + kl_weight: 10 + latent_dim: 32 + n_action_steps: 100 + n_decoder_layers: 1 + n_encoder_layers: 4 + n_heads: 8 + n_obs_steps: 1 + n_vae_encoder_layers: 4 + pre_norm: false + state_dim: 32 + temporal_ensemble_coeff: null + use_vae: true +seed: + value: 2026 +timestamp: + value: "2601230811" +train: + value: + checkpointing_steps: 5000 + data_parallel: ddp + deepspeed_config: /home/jliu/we_learn/src/InternVLA/config/deepseeds/zero3.json + enable_gradient_checkpointing: true + enable_mixed_precision_training: true + gradient_accumulation_steps: 1 + hf_token: .hf_token + learning_rate: 0.0001 + lora: false + lr_scheduler_kwargs: + betas: + - 0.95 + - 0.999 + eps: 1e-08 + weight_decay: 1e-06 + lr_scheduler_type: cosine + max_checkpoints_to_keep: null + max_grad_norm: 1 + max_training_steps: 42000 + mixed_precision: bf16 + name: act-g1-real + num_train_epochs: null + num_workers: 8 + output_dir: .runs + overfit_single_batch: false + reduce_in_full_precision: true + resume_from_checkpoint: null + sharding_strategy: full-shard + skip_resumed_steps: false + train_batch_size: 32 + val_batch_size: 16 + val_num_batches: 20 + validation_steps: 500 + warmup_ratio: null + warmup_steps: 1000 +wandb: + value: + entity: jliu530-soochow-university + group: null + id: null + name: null + project: hfm + resume: allow diff --git a/act/act-g1-real/act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230811/wandb/run-20260123_081132-yuvvvmoo/files/output.log b/act/act-g1-real/act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230811/wandb/run-20260123_081132-yuvvvmoo/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..2883a649f4b53016a299e159ab4180a1958d4866 --- /dev/null +++ b/act/act-g1-real/act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230811/wandb/run-20260123_081132-yuvvvmoo/files/output.log @@ -0,0 +1,240 @@ +[2;36m[08:11:37 01/23][0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Saved configuration to ]8;id=805427;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=514750;file:///home/jliu/we_learn/scripts/train.py#129\[2m129[0m]8;;\ +[2;36m [0m 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act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601230811[0m [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> seed: [1;36m2026[0m ]8;id=844246;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=394625;file:///home/jliu/we_learn/scripts/train.py#185\[2m185[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> mixed precision: torch.bfloat16 ]8;id=356929;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=94020;file:///home/jliu/we_learn/scripts/train.py#186\[2m186[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> warmup steps: [1;36m1000[0m ]8;id=147732;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=919050;file:///home/jliu/we_learn/scripts/train.py#187\[2m187[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> validation steps: [1;36m500[0m ]8;id=25941;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=888976;file:///home/jliu/we_learn/scripts/train.py#188\[2m188[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> checkpoint steps: [1;36m5000[0m ]8;id=95089;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=418144;file:///home/jliu/we_learn/scripts/train.py#189\[2m189[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> max gradient norm: [1;36m1.0[0m ]8;id=122104;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=446109;file:///home/jliu/we_learn/scripts/train.py#190\[2m190[0m]8;;\ +/home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/albumentations/__init__.py:13: UserWarning: A new version of Albumentations is available: 2.0.8 (you have 1.4.18). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1. + check_for_updates() +Loading external config: we.data.gr00t.data_config.WeUnitreeG1LocoDataConfig +Initialized dataset Hold_lunch_bag_with_both_hands_and_squat_to_put_on_the_coffee_table with EmbodimentTag.NEW_EMBODIMENT +Loading external config: we.data.gr00t.data_config.WeUnitreeG1LocoDataConfig +Initialized dataset Hold_lunch_bag_with_both_hands_and_squat_to_put_on_the_coffee_table with EmbodimentTag.NEW_EMBODIMENT +[2;36m[08:11:38 01/23][0m[2;36m [0m[34mINFO [0m | >> |=> Num training samples: ]8;id=917958;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=514883;file:///home/jliu/we_learn/scripts/train.py#193\[2m193[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Training dataset size: [1;36m43[0m,[1;36m427[0m ]8;id=440366;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=591396;file:///home/jliu/we_learn/scripts/train.py#194\[2m194[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Val dataset size: [1;36m43[0m,[1;36m427[0m ]8;id=26608;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=603472;file:///home/jliu/we_learn/scripts/train.py#196\[2m196[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Initialize optimizers and schedulers[33m...[0m ]8;id=738892;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=548632;file:///home/jliu/we_learn/scripts/train.py#199\[2m199[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m ***** Running training ***** ]8;id=450268;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=492873;file:///home/jliu/we_learn/scripts/train.py#203\[2m203[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num training examples = [1;36m43427[0m ]8;id=928955;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=127940;file:///home/jliu/we_learn/scripts/train.py#204\[2m204[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Max training Epochs = [1;36m62[0m ]8;id=749231;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=974614;file:///home/jliu/we_learn/scripts/train.py#205\[2m205[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Total optimization steps = [1;36m42000[0m ]8;id=322570;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=574775;file:///home/jliu/we_learn/scripts/train.py#206\[2m206[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num steps Per Epoch = [1;36m679[0m ]8;id=843992;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=927413;file:///home/jliu/we_learn/scripts/train.py#207\[2m207[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Effective training epochs = [1;36m123.71[0m ]8;id=282104;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=9727;file:///home/jliu/we_learn/scripts/train.py#208\[2m208[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Global train batch size [1m([0mw. parallel, distributed & accumulation[1m)[0m = [1;36m64[0m ]8;id=480486;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=842580;file:///home/jliu/we_learn/scripts/train.py#209\[2m209[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Device train batch size = [1;36m32[0m ]8;id=209544;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=272745;file:///home/jliu/we_learn/scripts/train.py#210\[2m210[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Gradient Accumulation steps = [1;36m1[0m ]8;id=866305;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=521307;file:///home/jliu/we_learn/scripts/train.py#211\[2m211[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num processes 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mode 100644 index 0000000000000000000000000000000000000000..552d6b40c1bf3734308336f8f19d3ce85477055f --- /dev/null +++ b/act/act-g1-real/act-hold-lunch-bag-with-both-hands-and-squat-to-put-on-the-coffee-table.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230811/wandb/run-20260123_081132-yuvvvmoo/files/requirements.txt @@ -0,0 +1,238 @@ +aiosignal==1.4.0 +transforms3d==0.4.2 +annotated-doc==0.0.4 +hf_transfer==0.1.9 +nvidia-cusolver-cu12==11.7.3.90 +pathspec==0.12.1 +gast==0.7.0 +pydantic==2.10.6 +antlr4-python3-runtime==4.9.3 +tensorflow-graphics==2021.12.3 +python-dotenv==1.2.1 +diffusers==0.30.2 +mergedeep==1.3.4 +Farama-Notifications==0.0.4 +mypy==1.18.2 +google-pasta==0.2.0 +deepdiff==8.6.1 +simplejpeg==1.9.0 +python-dateutil==2.9.0.post0 +nvidia-curand-cu12==10.3.9.90 +torchaudio==2.8.0 +pyyaml-include==1.4.1 +eval_type_backport==0.2.2 +typing_extensions==4.15.0 +array_record==0.8.1 +h5py==3.14.0 +cmake==4.1.0 +platformdirs==4.4.0 +importlib_resources==6.5.2 +params_proto==2.13.2 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--train.validation_steps=500 + - --train.val_num_batches=20 + - --train.max-training-steps=42000 + - --train.learning-rate=1e-4 + - --train.max-grad-norm=1.0 + - --train.lr_scheduler_kwargs.weight_decay=1e-6 + - --train.lr_scheduler_kwargs.betas + - "0.95" + - "0.999" + - --train.lr_scheduler_type=cosine + - --train.warmup-steps=1000 + - --train.warmup-ratio=None + - --train.checkpointing-steps=5000 + - --data.root_dir=/hfm/data/real_teleop_g1/lerobot + - --data.dataset_paths=Pick_bottle_and_turn_and_pour_into_cup + - --data.data_config=we.data.gr00t.data_config:WeUnitreeG1LocoDataConfig + - --data.transform.repack.pad-action-dim=36 + - --data.transform.repack.pad-state-dim=32 + - --data.transform.action-state.stat-path=dataset_statistics/g1-stats-Pick_bottle_and_turn_and_pour_into_cup.json + - --data.transform.action-state.stat-action-key=action + - --data.transform.action-state.stat-state-key=states + - --data.transform.action-state.normalize-state + - 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+ os: Linux-6.8.0-88-generic-x86_64-with-glibc2.39 + program: /home/jliu/we_learn/scripts/train.py + python: CPython 3.10.19 + root: /home/jliu/we_learn/.runs/act-g1-real/act-pick-bottle-and-turn-and-pour-into-cup.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230537 + startedAt: "2026-01-23T05:38:04.801352Z" + writerId: 637thf1soxyfo9ydb2ixk28lpk2uaa9z + m: [] + python_version: 3.10.19 + t: + "1": + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 71 + "2": + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 51 + - 71 + - 80 + - 83 + "3": + - 2 + - 13 + - 61 + "4": 3.10.19 + "5": 0.23.1 + "6": 4.44.2 + "12": 0.23.1 + "13": linux-x86_64 +auto_tag_run: + value: false +data: + value: + action_chunk_size: 100 + balance_dataset_weights: true + balance_trajectory_weights: true + data_config: we.data.gr00t.data_config:WeUnitreeG1LocoDataConfig + dataset_paths: + - Pick_bottle_and_turn_and_pour_into_cup + embodiment_tag: new_embodiment + preload_all: false + root_dir: /hfm/data/real_teleop_g1/lerobot + transform: + action_state: + action_max: + - -0.1213064193725586 + - 0.9210000038146973 + - 1.2908918857574463 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.11635252088308334 + - 0.11013627797365189 + - -0.4304533302783966 + - 1.4093444347381592 + - 1.7463293075561523 + - 1.2701730728149414 + - 1.7463293075561523 + - 0.7063568830490112 + - 0.5787747502326965 + - 0.2658364176750183 + - 1.36533784866333 + - 0.7970759272575378 + - 0.050355639308691025 + - 0.6250148415565491 + - 1.0034496784210205 + - 0.6385414600372314 + - 0.8838359117507935 + - 1.3011894226074219 + - 0.9103013873100281 + - 0.6697143316268921 + - 1.300451397895813 + - 0.1827869415283203 + - 0.12582087516784668 + - 0.7600905299186707 + - 0.7456391453742981 + - 0.3499999940395355 + - 0.5 + - 0.5 + - 0 + action_min: + - -1.0481975078582764 + - 0.33770662546157837 + - 0.5833444595336914 + - -0.9677509069442749 + - -1.5101021528244019 + - -0.9662452340126038 + - 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annotation.human.task_description + pad_action_dim: 36 + pad_state_dim: 32 + state_key: state + video_backend: decord +debug: + value: false +environment_variables: + value: + CUDA_VISIBLE_DEVICES: 6,7 + DATA_HOME: /hfm/data + HF_HOME: /hfm/cache + HF_LEROBOT_HOME: /hfm/data/lerobot + HF_TOKEN: hf_...adRb + LOCAL_RANK: "0" + LOCAL_WORLD_SIZE: "2" + MASTER_ADDR: nebula101 + MASTER_PORT: "39375" + OMP_NUM_THREADS: "32" + PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION: python + RANK: "0" + TORCH_HOME: /hfm/cache + UV_CACHE_DIR: /hfm/cache + WANDB_API_KEY: 90e...5c06 + WE_HOME: /hfm + WORLD_SIZE: "2" +eval: + value: false +exp: + value: act-pick-bottle-and-turn-and-pour-into-cup +log: + value: + log_freq: 100 + logging_dir: logs + report_to: wandb +model: + value: + action_dim: 36 + chunk_size: 100 + dim_feedforward: 3200 + dim_model: 512 + dropout: 0.1 + feedforward_activation: relu + kl_weight: 10 + latent_dim: 32 + n_action_steps: 100 + n_decoder_layers: 1 + n_encoder_layers: 4 + n_heads: 8 + n_obs_steps: 1 + n_vae_encoder_layers: 4 + pre_norm: false + state_dim: 32 + temporal_ensemble_coeff: null + use_vae: true +seed: + value: 2026 +timestamp: + value: "2601230537" +train: + value: + checkpointing_steps: 5000 + data_parallel: ddp + deepspeed_config: /home/jliu/we_learn/src/InternVLA/config/deepseeds/zero3.json + enable_gradient_checkpointing: true + enable_mixed_precision_training: true + gradient_accumulation_steps: 1 + hf_token: .hf_token + learning_rate: 0.0001 + lora: false + lr_scheduler_kwargs: + betas: + - 0.95 + - 0.999 + eps: 1e-08 + weight_decay: 1e-06 + lr_scheduler_type: cosine + max_checkpoints_to_keep: null + max_grad_norm: 1 + max_training_steps: 42000 + mixed_precision: bf16 + name: act-g1-real + num_train_epochs: null + num_workers: 8 + output_dir: .runs + overfit_single_batch: false + reduce_in_full_precision: true + resume_from_checkpoint: null + sharding_strategy: full-shard + skip_resumed_steps: false + train_batch_size: 32 + val_batch_size: 16 + val_num_batches: 20 + validation_steps: 500 + warmup_ratio: null + warmup_steps: 1000 +wandb: + value: + entity: jliu530-soochow-university + group: null + id: null + name: null + project: hfm + resume: allow diff --git a/act/act-g1-real/act-pick-bottle-and-turn-and-pour-into-cup.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230537/wandb/run-20260123_053804-3s5i6mk8/files/output.log b/act/act-g1-real/act-pick-bottle-and-turn-and-pour-into-cup.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230537/wandb/run-20260123_053804-3s5i6mk8/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..c0d2de65aca5f488fed5424dd74591f7ecf3914f --- /dev/null +++ b/act/act-g1-real/act-pick-bottle-and-turn-and-pour-into-cup.g1-real.cosin.lr1.0e-04.b64.gpus2.2601230537/wandb/run-20260123_053804-3s5i6mk8/files/output.log @@ -0,0 +1,306 @@ +[2;36m[05:38:05 01/23][0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Saved configuration to 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]8;id=329497;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=220436;file:///home/jliu/we_learn/scripts/train.py#184\[2m184[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> seed: [1;36m2026[0m ]8;id=844246;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=394625;file:///home/jliu/we_learn/scripts/train.py#185\[2m185[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> mixed precision: torch.bfloat16 ]8;id=356929;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=94020;file:///home/jliu/we_learn/scripts/train.py#186\[2m186[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> warmup steps: [1;36m1000[0m ]8;id=147732;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=919050;file:///home/jliu/we_learn/scripts/train.py#187\[2m187[0m]8;;\ +[2;36m[05:38:06 01/23][0m[2;36m [0m[34mINFO [0m | >> |=> validation steps: [1;36m500[0m ]8;id=25941;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=888976;file:///home/jliu/we_learn/scripts/train.py#188\[2m188[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> checkpoint steps: [1;36m5000[0m ]8;id=95089;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=418144;file:///home/jliu/we_learn/scripts/train.py#189\[2m189[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> max gradient norm: [1;36m1.0[0m ]8;id=122104;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=446109;file:///home/jliu/we_learn/scripts/train.py#190\[2m190[0m]8;;\ +/home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/albumentations/__init__.py:13: UserWarning: A new version of Albumentations is available: 2.0.8 (you have 1.4.18). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1. + check_for_updates() +Loading external config: we.data.gr00t.data_config.WeUnitreeG1LocoDataConfig +Initialized dataset Pick_bottle_and_turn_and_pour_into_cup with EmbodimentTag.NEW_EMBODIMENT +Loading external config: we.data.gr00t.data_config.WeUnitreeG1LocoDataConfig +Initialized dataset Pick_bottle_and_turn_and_pour_into_cup with EmbodimentTag.NEW_EMBODIMENT +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num training samples: ]8;id=917958;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=514883;file:///home/jliu/we_learn/scripts/train.py#193\[2m193[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Training dataset size: [1;36m77[0m,[1;36m584[0m ]8;id=440366;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=591396;file:///home/jliu/we_learn/scripts/train.py#194\[2m194[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Val dataset size: [1;36m77[0m,[1;36m584[0m ]8;id=26608;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=603472;file:///home/jliu/we_learn/scripts/train.py#196\[2m196[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Initialize optimizers and schedulers[33m...[0m ]8;id=738892;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=548632;file:///home/jliu/we_learn/scripts/train.py#199\[2m199[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m ***** Running training ***** ]8;id=450268;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=492873;file:///home/jliu/we_learn/scripts/train.py#203\[2m203[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num training examples = [1;36m77584[0m ]8;id=928955;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=127940;file:///home/jliu/we_learn/scripts/train.py#204\[2m204[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Max training Epochs = [1;36m35[0m ]8;id=749231;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=974614;file:///home/jliu/we_learn/scripts/train.py#205\[2m205[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Total optimization steps = [1;36m42000[0m ]8;id=322570;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=574775;file:///home/jliu/we_learn/scripts/train.py#206\[2m206[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num steps Per Epoch = [1;36m1213[0m ]8;id=843992;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=927413;file:///home/jliu/we_learn/scripts/train.py#207\[2m207[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Effective training epochs = [1;36m69.25[0m ]8;id=282104;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=9727;file:///home/jliu/we_learn/scripts/train.py#208\[2m208[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Global train batch size [1m([0mw. parallel, distributed & 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[1;36m2.20[0m.[1;36m0[0m available. ]8;id=459749;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/datasets/config.py\[2mconfig.py[0m]8;;\[2m:[0m]8;id=722818;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/datasets/config.py#112\[2m112[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Accelerator runs in: .runs/act-g1-real/act-pick-bottle-and-turn-and-pour-into-cup.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601230537[0m ]8;id=966756;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=191940;file:///home/jliu/we_learn/scripts/train.py#219\[2m219[0m]8;;\ +Traing steps: 24%|███████████████████████ | 10000/42000 [44:14<2:19:49, 3.81it/s, loss=0.0889, lr=8.9e-05] + +[2;36m[06:00:48 01/23][0m[2;36m [0m[34mINFO [0m | >> Saving current state to 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'TORCH_HOME': '/hfm/cache', 'HF_TOKEN': 'hf_...adRb', 'HF_LEROBOT_HOME': '/hfm/data/lerobot', 'WE_HOME': '/hfm', 'DATA_HOME': '/hfm/data', 'UV_CACHE_DIR': '/hfm/cache', 'WANDB_API_KEY': '90e...5c06', 'PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION': 'python', 'CUDA_VISIBLE_DEVICES': '6,7', 'WORLD_SIZE': '2', 'LOCAL_WORLD_SIZE': '2', 'RANK': '0', 'LOCAL_RANK': '0', 'MASTER_ADDR': 'nebula101', 'MASTER_PORT': '34911'}} +2026-01-24 01:19:03,088 INFO MainThread:98829 [wandb_run.py:_finish():2287] finishing run jliu530-soochow-university/hfm/7kwywvv2 +2026-01-24 01:19:03,090 INFO MainThread:98829 [wandb_run.py:_atexit_cleanup():2486] got exitcode: 0 +2026-01-24 01:19:03,090 INFO MainThread:98829 [wandb_run.py:_restore():2468] restore +2026-01-24 01:19:03,091 INFO MainThread:98829 [wandb_run.py:_restore():2474] restore done +2026-01-24 01:19:03,888 INFO MainThread:98829 [wandb_run.py:_footer_sync_info():3862] logging synced files diff --git 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--exp=act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin + - --train.name=act-g1-real + - --log.report-to=wandb + - --wandb.entity=jliu530-soochow-university + - --train.data_parallel=ddp + - --train.mixed_precision=bf16 + - --train.train-batch-size=32 + - --train.gradient_accumulation_steps=1 + - --train.validation_steps=500 + - --train.val_num_batches=20 + - --train.max-training-steps=42000 + - --train.learning-rate=1e-4 + - --train.max-grad-norm=1.0 + - --train.lr_scheduler_kwargs.weight_decay=1e-6 + - --train.lr_scheduler_kwargs.betas + - "0.95" + - "0.999" + - --train.lr_scheduler_type=cosine + - --train.warmup-steps=1000 + - --train.warmup-ratio=None + - --train.checkpointing-steps=5000 + - --data.root_dir=/hfm/data/real_teleop_g1/lerobot + - --data.dataset_paths=Pull_the_tray_out_of_chips_can_and_throw_the_can_into_trash_bin + - --data.data_config=we.data.gr00t.data_config:WeUnitreeG1LocoDataConfig + - --data.transform.repack.pad-action-dim=36 + - --data.transform.repack.pad-state-dim=32 + - --data.transform.action-state.stat-path=dataset_statistics/g1-stats-Pull_the_tray_out_of_chips_can_and_throw_the_can_into_trash_bin.json + - --data.transform.action-state.stat-action-key=action + - --data.transform.action-state.stat-state-key=states + - --data.transform.action-state.normalize-state + - --data.transform.action-state.action-norm-type=bounds + - --data.transform.model.img-aug + - --data.action-chunk-size=100 + - --model.chunk-size=100 + - --model.n-action-steps=100 + - --model.action-dim=36 + - --model.state-dim=32 + - --model.use-vae + - --model.kl-weight=10.0 + codePath: scripts/train.py + codePathLocal: scripts/train.py + cpu_count: 128 + cpu_count_logical: 128 + cudaVersion: "12.9" + disk: + /: + total: "3934171283456" + used: "3658406711296" + email: jliu530@163.com + executable: /home/jliu/we_learn/.venv-pusht/bin/python3 + git: + commit: 00cea05aeb87e91ce3e0982bc9f6ca18b63adbed + remote: https://github.com/songlin/we_learn.git + gpu: NVIDIA A100-SXM4-80GB + gpu_count: 8 + gpu_nvidia: + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-02b51758-2aec-8c71-c751-92567fdb15ad + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-be645bb4-1ef0-15da-fc6b-53c5378650df + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-44868ccd-5809-61e2-f8cc-1dff8efc7eb1 + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-a12b8de3-fb12-2901-c3a2-1e0e2b050b7b + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-57787183-4904-b2c9-cf52-1c1af976b365 + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-e82d0fd0-9dcb-3d7c-9451-c4770ad6ddc3 + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-9a825fa3-60d0-3acf-6c79-67e9230ae24c + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-6fe02e5e-b886-9c02-e433-6237f3897dd8 + host: nebula101 + memory: + total: "1623176945664" + os: Linux-6.8.0-88-generic-x86_64-with-glibc2.39 + program: /home/jliu/we_learn/scripts/train.py + python: CPython 3.10.19 + root: /home/jliu/we_learn/.runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.0e-04.b64.gpus2.2601231421 + startedAt: "2026-01-23T14:21:44.782937Z" + writerId: wrh35kkonuqqygq6esxzapgv6wxd956y + m: [] + python_version: 3.10.19 + t: + "1": + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 71 + "2": + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 51 + - 71 + - 80 + - 83 + "3": + - 2 + - 13 + - 61 + "4": 3.10.19 + "5": 0.23.1 + "6": 4.44.2 + "12": 0.23.1 + "13": linux-x86_64 +auto_tag_run: + value: false +data: + value: + action_chunk_size: 100 + balance_dataset_weights: true + balance_trajectory_weights: true + data_config: we.data.gr00t.data_config:WeUnitreeG1LocoDataConfig + dataset_paths: + - Pull_the_tray_out_of_chips_can_and_throw_the_can_into_trash_bin + embodiment_tag: new_embodiment + preload_all: false + root_dir: /hfm/data/real_teleop_g1/lerobot + transform: + action_state: + action_max: + - 0.16955414414405823 + - 0.9210000038146973 + - 1.476342797279358 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.12308759987354279 + - 0.016431454569101334 + - -0.4424695372581482 + - 1.2837177515029907 + - 1.7463293075561523 + - 1.17170250415802 + - 1.7463293075561523 + - 1.5402921438217163 + - 1.605297565460205 + - 1.066504955291748 + - 1.2896910905838013 + - 1.9362354278564453 + - 0.7514232397079468 + - 1.3668304681777954 + - 1.035903811454773 + - 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]8;id=122104;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=446109;file:///home/jliu/we_learn/scripts/train.py#190\[2m190[0m]8;;\ +/home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/albumentations/__init__.py:13: UserWarning: A new version of Albumentations is available: 2.0.8 (you have 1.4.18). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1. + check_for_updates() +Loading external config: we.data.gr00t.data_config.WeUnitreeG1LocoDataConfig +Initialized dataset Pull_the_tray_out_of_chips_can_and_throw_the_can_into_trash_bin with EmbodimentTag.NEW_EMBODIMENT +Loading external config: we.data.gr00t.data_config.WeUnitreeG1LocoDataConfig +Initialized dataset Pull_the_tray_out_of_chips_can_and_throw_the_can_into_trash_bin with EmbodimentTag.NEW_EMBODIMENT +[2;36m[14:21:47 01/23][0m[2;36m [0m[34mINFO [0m | >> |=> Num training samples: ]8;id=917958;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=514883;file:///home/jliu/we_learn/scripts/train.py#193\[2m193[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Training dataset size: [1;36m102[0m,[1;36m536[0m ]8;id=440366;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=591396;file:///home/jliu/we_learn/scripts/train.py#194\[2m194[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Val dataset size: [1;36m102[0m,[1;36m536[0m ]8;id=26608;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=603472;file:///home/jliu/we_learn/scripts/train.py#196\[2m196[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Initialize optimizers and schedulers[33m...[0m ]8;id=738892;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=548632;file:///home/jliu/we_learn/scripts/train.py#199\[2m199[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m ***** Running training ***** ]8;id=450268;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=492873;file:///home/jliu/we_learn/scripts/train.py#203\[2m203[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num training examples = [1;36m102536[0m ]8;id=928955;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=127940;file:///home/jliu/we_learn/scripts/train.py#204\[2m204[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Max training Epochs = [1;36m27[0m ]8;id=749231;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=974614;file:///home/jliu/we_learn/scripts/train.py#205\[2m205[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Total optimization steps = [1;36m42000[0m ]8;id=322570;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=574775;file:///home/jliu/we_learn/scripts/train.py#206\[2m206[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num steps Per Epoch = [1;36m1603[0m ]8;id=843992;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=927413;file:///home/jliu/we_learn/scripts/train.py#207\[2m207[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Effective training epochs = [1;36m52.40[0m ]8;id=282104;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=9727;file:///home/jliu/we_learn/scripts/train.py#208\[2m208[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Global train batch size [1m([0mw. parallel, distributed & accumulation[1m)[0m = [1;36m64[0m ]8;id=480486;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=842580;file:///home/jliu/we_learn/scripts/train.py#209\[2m209[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Device train batch size = [1;36m32[0m ]8;id=209544;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=272745;file:///home/jliu/we_learn/scripts/train.py#210\[2m210[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Gradient Accumulation steps = [1;36m1[0m ]8;id=866305;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=521307;file:///home/jliu/we_learn/scripts/train.py#211\[2m211[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num processes [1m([0mGPUs[1m)[0m = [1;36m2[0m ]8;id=35253;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=871789;file:///home/jliu/we_learn/scripts/train.py#212\[2m212[0m]8;;\ +[2;36m[14:21:49 01/23][0m[2;36m [0m[34mINFO [0m | >> TensorFlow version [1;36m2.20[0m.[1;36m0[0m available. ]8;id=459749;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/datasets/config.py\[2mconfig.py[0m]8;;\[2m:[0m]8;id=722818;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/datasets/config.py#112\[2m112[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Accelerator runs in: ]8;id=966756;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=191940;file:///home/jliu/we_learn/scripts/train.py#219\[2m219[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m [2m [0m +Traing steps: 36%|██████████████████████████████████████▏ | 15000/42000 [5:56:44<4:39:24, 1.61it/s, loss=0.116, lr=7.4e-05] + +[2;36m[16:31:23 01/23][0m[2;36m [0m[34mINFO [0m | >> Saving current state to ]8;id=680052;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/accelerator.py\[2maccelerator.py[0m]8;;\[2m:[0m]8;id=279409;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/accelerator.py#3644\[2m3644[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoint [2m [0m +[2;36m [0m s/ckpt_5000 [2m [0m +[2;36m[16:31:27 01/23][0m[2;36m [0m[34mINFO [0m | >> Model weights saved in ]8;id=898249;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=431290;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#114\[2m114[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_5000/model.safetensors [2m [0m +[2;36m[16:31:28 01/23][0m[2;36m [0m[34mINFO [0m | >> Optimizer state saved in ]8;id=224;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=473149;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#121\[2m121[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_5000/optimizer.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Scheduler state saved in ]8;id=639771;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=832328;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#128\[2m128[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_5000/scheduler.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Sampler state for dataloader [1;36m0[0m saved in ]8;id=788377;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=572992;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#145\[2m145[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_5000/sampler.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Sampler state for dataloader [1;36m1[0m saved in ]8;id=374604;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=162278;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#145\[2m145[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_5000/sampler_1.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Random states saved in ]8;id=806908;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=971194;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#176\[2m176[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_5000/random_states_0.pkl [2m [0m +Saved state to .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.0e-04.b64.gpus2.2601231421/checkpoints/ckpt_5000 +[2;36m[18:40:44 01/23][0m[2;36m [0m[33mWARNING [0m | >> [1m[[0m*[1m][0m Checkpoint [1;36m10000[0m already exists, skipping save. ]8;id=983054;file:///home/jliu/we_learn/src/we/learn/trainers/act_g1_real.py\[2mact_g1_real.py[0m]8;;\[2m:[0m]8;id=141415;file:///home/jliu/we_learn/src/we/learn/trainers/act_g1_real.py#177\[2m177[0m]8;;\ +Saved state to None +[2;36m[20:18:33 01/23][0m[2;36m [0m[34mINFO [0m | >> Saving current state to ]8;id=137590;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/accelerator.py\[2maccelerator.py[0m]8;;\[2m:[0m]8;id=627191;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/accelerator.py#3644\[2m3644[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoint [2m [0m +[2;36m [0m s/ckpt_15000 [2m [0m +[2;36m[20:18:37 01/23][0m[2;36m [0m[34mINFO [0m | >> Model weights saved in 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.runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_15000/optimizer.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Scheduler state saved in ]8;id=432054;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=409371;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#128\[2m128[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_15000/scheduler.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Sampler state for dataloader [1;36m0[0m saved in ]8;id=206251;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=394271;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#145\[2m145[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_15000/sampler.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Sampler state for dataloader [1;36m1[0m saved in ]8;id=77000;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=53870;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#145\[2m145[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_15000/sampler_1.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Random states saved in ]8;id=979940;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=806914;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#176\[2m176[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601231421[0m/checkpoin [2m [0m +[2;36m [0m ts/ckpt_15000/random_states_0.pkl [2m [0m +Saved state to .runs/act-g1-real/act-pull-the-tray-out-of-chips-can-and-throw-the-can-into-trash-bin.g1-real.cosin.lr1.0e-04.b64.gpus2.2601231421/checkpoints/ckpt_15000 +[2;36m[21:50:16 01/23][0m[2;36m [0m[34mINFO [0m 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OMP_NUM_THREADS: "32" + PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION: python + RANK: "0" + TORCH_HOME: /hfm/cache + UV_CACHE_DIR: /hfm/cache + WANDB_API_KEY: 90e...5c06 + WE_HOME: /hfm + WORLD_SIZE: "2" +eval: + value: false +exp: + value: act-rotate-to-pour-ham-into-plate-and-push-the-cart-forward +log: + value: + log_freq: 100 + logging_dir: logs + report_to: wandb +model: + value: + action_dim: 36 + chunk_size: 100 + dim_feedforward: 3200 + dim_model: 512 + dropout: 0.1 + feedforward_activation: relu + kl_weight: 10 + latent_dim: 32 + n_action_steps: 100 + n_decoder_layers: 1 + n_encoder_layers: 4 + n_heads: 8 + n_obs_steps: 1 + n_vae_encoder_layers: 4 + pre_norm: false + state_dim: 32 + temporal_ensemble_coeff: null + use_vae: true +seed: + value: 2026 +timestamp: + value: "2601240510" +train: + value: + checkpointing_steps: 5000 + data_parallel: ddp + deepspeed_config: /home/jliu/we_learn/src/InternVLA/config/deepseeds/zero3.json + enable_gradient_checkpointing: true + enable_mixed_precision_training: true + gradient_accumulation_steps: 1 + hf_token: .hf_token + learning_rate: 0.0001 + lora: false + lr_scheduler_kwargs: + betas: + - 0.95 + - 0.999 + eps: 1e-08 + weight_decay: 1e-06 + lr_scheduler_type: cosine + max_checkpoints_to_keep: null + max_grad_norm: 1 + max_training_steps: 42000 + mixed_precision: bf16 + name: act-g1-real + num_train_epochs: null + num_workers: 8 + output_dir: .runs + overfit_single_batch: false + reduce_in_full_precision: true + resume_from_checkpoint: null + sharding_strategy: full-shard + skip_resumed_steps: false + train_batch_size: 32 + val_batch_size: 16 + val_num_batches: 20 + validation_steps: 500 + warmup_ratio: null + warmup_steps: 1000 +wandb: + value: + entity: jliu530-soochow-university + group: null + id: null + name: null + project: hfm + resume: allow diff --git a/act/act-g1-real/act-rotate-to-pour-ham-into-plate-and-push-the-cart-forward.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240510/wandb/run-20260124_051018-nfks4wzd/files/output.log b/act/act-g1-real/act-rotate-to-pour-ham-into-plate-and-push-the-cart-forward.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240510/wandb/run-20260124_051018-nfks4wzd/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..f89e61d94b47ec7e1e6927d1e24dcb69e7b73150 --- /dev/null +++ b/act/act-g1-real/act-rotate-to-pour-ham-into-plate-and-push-the-cart-forward.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240510/wandb/run-20260124_051018-nfks4wzd/files/output.log @@ -0,0 +1,250 @@ +[2;36m[05:10:20 01/24][0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Saved configuration to ]8;id=805427;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=514750;file:///home/jliu/we_learn/scripts/train.py#129\[2m129[0m]8;;\ +[2;36m [0m 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]8;id=95089;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=418144;file:///home/jliu/we_learn/scripts/train.py#189\[2m189[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> max gradient norm: [1;36m1.0[0m ]8;id=122104;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=446109;file:///home/jliu/we_learn/scripts/train.py#190\[2m190[0m]8;;\ +/home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/albumentations/__init__.py:13: UserWarning: A new version of Albumentations is available: 2.0.8 (you have 1.4.18). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1. + check_for_updates() +Loading external config: we.data.gr00t.data_config.WeUnitreeG1LocoDataConfig +Initialized dataset Rotate_to_pour_ham_into_plate_and_push_the_cart_forward with EmbodimentTag.NEW_EMBODIMENT +Loading external config: we.data.gr00t.data_config.WeUnitreeG1LocoDataConfig +Initialized dataset Rotate_to_pour_ham_into_plate_and_push_the_cart_forward with EmbodimentTag.NEW_EMBODIMENT +[2;36m[05:10:21 01/24][0m[2;36m [0m[34mINFO [0m | >> |=> Num training samples: ]8;id=917958;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=514883;file:///home/jliu/we_learn/scripts/train.py#193\[2m193[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Training dataset size: [1;36m113[0m,[1;36m702[0m ]8;id=440366;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=591396;file:///home/jliu/we_learn/scripts/train.py#194\[2m194[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Val dataset size: [1;36m113[0m,[1;36m702[0m ]8;id=26608;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=603472;file:///home/jliu/we_learn/scripts/train.py#196\[2m196[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Initialize optimizers and schedulers[33m...[0m ]8;id=738892;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=548632;file:///home/jliu/we_learn/scripts/train.py#199\[2m199[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m ***** Running training ***** ]8;id=450268;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=492873;file:///home/jliu/we_learn/scripts/train.py#203\[2m203[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num training examples = [1;36m113702[0m ]8;id=928955;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=127940;file:///home/jliu/we_learn/scripts/train.py#204\[2m204[0m]8;;\ +[2;36m [0m[2;36m 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100644 index 0000000000000000000000000000000000000000..552d6b40c1bf3734308336f8f19d3ce85477055f --- /dev/null +++ b/act/act-g1-real/act-rotate-to-pour-ham-into-plate-and-push-the-cart-forward.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240510/wandb/run-20260124_051018-nfks4wzd/files/requirements.txt @@ -0,0 +1,238 @@ +aiosignal==1.4.0 +transforms3d==0.4.2 +annotated-doc==0.0.4 +hf_transfer==0.1.9 +nvidia-cusolver-cu12==11.7.3.90 +pathspec==0.12.1 +gast==0.7.0 +pydantic==2.10.6 +antlr4-python3-runtime==4.9.3 +tensorflow-graphics==2021.12.3 +python-dotenv==1.2.1 +diffusers==0.30.2 +mergedeep==1.3.4 +Farama-Notifications==0.0.4 +mypy==1.18.2 +google-pasta==0.2.0 +deepdiff==8.6.1 +simplejpeg==1.9.0 +python-dateutil==2.9.0.post0 +nvidia-curand-cu12==10.3.9.90 +torchaudio==2.8.0 +pyyaml-include==1.4.1 +eval_type_backport==0.2.2 +typing_extensions==4.15.0 +array_record==0.8.1 +h5py==3.14.0 +cmake==4.1.0 +platformdirs==4.4.0 +importlib_resources==6.5.2 +params_proto==2.13.2 +toml==0.10.2 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MainThread:2118520 [wandb_run.py:_atexit_cleanup():2486] got exitcode: 0 +2026-01-25 03:14:36,577 INFO MainThread:2118520 [wandb_run.py:_restore():2468] restore +2026-01-25 03:14:36,577 INFO MainThread:2118520 [wandb_run.py:_restore():2474] restore done +2026-01-25 03:14:37,499 INFO MainThread:2118520 [wandb_run.py:_footer_sync_info():3862] logging synced files diff --git a/act/act-g1-real/act-rotate-to-pour-ham-into-plate-and-push-the-cart-forward.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240510/wandb/run-20260124_051018-nfks4wzd/run-nfks4wzd.wandb b/act/act-g1-real/act-rotate-to-pour-ham-into-plate-and-push-the-cart-forward.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240510/wandb/run-20260124_051018-nfks4wzd/run-nfks4wzd.wandb new file mode 100644 index 0000000000000000000000000000000000000000..a9c711c6ccb36883189eb64fd03ebd8565079e0e --- /dev/null +++ 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--exp=act-spray-the-bowl-and-wipe-it-and-stack-it-up + - --train.name=act-g1-real + - --log.report-to=wandb + - --wandb.entity=jliu530-soochow-university + - --train.data_parallel=ddp + - --train.mixed_precision=bf16 + - --train.train-batch-size=32 + - --train.gradient_accumulation_steps=1 + - --train.validation_steps=500 + - --train.val_num_batches=20 + - --train.max-training-steps=42000 + - --train.learning-rate=1e-4 + - --train.max-grad-norm=1.0 + - --train.lr_scheduler_kwargs.weight_decay=1e-6 + - --train.lr_scheduler_kwargs.betas + - "0.95" + - "0.999" + - --train.lr_scheduler_type=cosine + - --train.warmup-steps=1000 + - --train.warmup-ratio=None + - --train.checkpointing-steps=5000 + - --data.root_dir=/hfm/data/real_teleop_g1/lerobot + - --data.dataset_paths=Spray_the_bowl_and_wipe_it_and_stack_it_up + - --data.data_config=we.data.gr00t.data_config:WeUnitreeG1LocoDataConfig + - --data.transform.repack.pad-action-dim=36 + - --data.transform.repack.pad-state-dim=32 + - --data.transform.action-state.stat-path=dataset_statistics/g1-stats-Spray_the_bowl_and_wipe_it_and_stack_it_up.json + - --data.transform.action-state.stat-action-key=action + - --data.transform.action-state.stat-state-key=states + - --data.transform.action-state.normalize-state + - --data.transform.action-state.action-norm-type=bounds + - --data.transform.model.img-aug + - --data.action-chunk-size=100 + - --model.chunk-size=100 + - --model.n-action-steps=100 + - --model.action-dim=36 + - --model.state-dim=32 + - --model.use-vae + - --model.kl-weight=10.0 + codePath: scripts/train.py + codePathLocal: scripts/train.py + cpu_count: 128 + cpu_count_logical: 128 + cudaVersion: "12.9" + disk: + /: + total: "3934171283456" + used: "3708926939136" + email: jliu530@163.com + executable: /home/jliu/we_learn/.venv-pusht/bin/python3 + git: + commit: df0aa6074e8b16d0e2be002a9a9c1971edf209ec + remote: https://github.com/songlin/we_learn.git + gpu: NVIDIA A100-SXM4-80GB + gpu_count: 8 + gpu_nvidia: + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-02b51758-2aec-8c71-c751-92567fdb15ad + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-be645bb4-1ef0-15da-fc6b-53c5378650df + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-44868ccd-5809-61e2-f8cc-1dff8efc7eb1 + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-a12b8de3-fb12-2901-c3a2-1e0e2b050b7b + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-57787183-4904-b2c9-cf52-1c1af976b365 + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-e82d0fd0-9dcb-3d7c-9451-c4770ad6ddc3 + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-9a825fa3-60d0-3acf-6c79-67e9230ae24c + - architecture: Ampere + cudaCores: 6912 + memoryTotal: "85899345920" + name: NVIDIA A100-SXM4-80GB + uuid: GPU-6fe02e5e-b886-9c02-e433-6237f3897dd8 + host: nebula101 + memory: + total: "1623176945664" + os: Linux-6.8.0-88-generic-x86_64-with-glibc2.39 + program: /home/jliu/we_learn/scripts/train.py + python: CPython 3.10.19 + root: /home/jliu/we_learn/.runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240214 + startedAt: "2026-01-24T02:14:47.097099Z" + writerId: pxst4lz06gbj2juelnidvbk8rsuiffs1 + m: [] + python_version: 3.10.19 + t: + "1": + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 71 + "2": + - 1 + - 2 + - 3 + - 11 + - 41 + - 49 + - 51 + - 71 + - 80 + - 83 + "3": + - 2 + - 13 + - 61 + "4": 3.10.19 + "5": 0.23.1 + "6": 4.44.2 + "12": 0.23.1 + "13": linux-x86_64 +auto_tag_run: + value: false +data: + value: + action_chunk_size: 100 + balance_dataset_weights: true + balance_trajectory_weights: true + data_config: we.data.gr00t.data_config:WeUnitreeG1LocoDataConfig + dataset_paths: + - Spray_the_bowl_and_wipe_it_and_stack_it_up + embodiment_tag: new_embodiment + preload_all: false + root_dir: /hfm/data/real_teleop_g1/lerobot + transform: + action_state: + action_max: + - 0.1852411925792694 + - 0.9147700667381287 + - 1.6026862859725952 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.0010000000474974513 + - 0.4500204622745514 + - 0.36118876934051514 + - -0.4222349226474762 + - 1.5717962980270386 + - 1.7463293075561523 + - 1.5103485584259033 + - 1.7463293075561523 + - 1.1773991584777832 + - 1.8751757144927979 + - 0.9741659760475159 + - 1.4004517793655396 + - 1.4179662466049194 + - 0.46019142866134644 + - 0.9530820250511169 + - 0.9330370426177979 + - 0.7938688397407532 + - 0.8228927850723267 + - 1.3041672706604004 + - 0.33489832282066345 + - 1.3568487167358398 + - 1.0455472469329834 + - 0.2588168680667877 + - 0.37684208154678345 + - 0.5529751181602478 + - 0.7785804867744446 + - 0 + - 0 + - 0 + - 0 + action_min: + - -1.0481975078582764 + - -0.27842938899993896 + - 0.3644271194934845 + - -1.5717962980270386 + - -1.7463293075561523 + - -1.5717962980270386 + - -1.7463293075561523 + - -0.9726980328559875 + - -0.9210000038146973 + - -1.434515118598938 + - -0.0010000000474974513 + - -0.0010000000474974513 + - -0.0010000000474974513 + - -0.0010000000474974513 + - -1.6587185859680176 + - -0.26730066537857056 + - -1.0476018190383911 + - -1.0471998453140259 + - -1.5517717599868774 + - -1.6144294738769531 + - -1.4627567529678345 + - -1.578611969947815 + - -0.84627765417099 + - -0.56448894739151 + - -0.7843337059020996 + - -1.6328412294387817 + - -1.4047861099243164 + - -1.5124512910842896 + - -0.25610852241516113 + - -0.25414812564849854 + - -0.6856858730316162 + - 0.6628404259681702 + - 0 + - 0 + - 0 + - 0 + action_norm_masks: + - true + - true + - true + - true + - true + - true + - false + action_norm_type: bounds + normalize_state: true + pad_action_dim: null + pad_state_dim: null + stat_action_key: action + stat_path: dataset_statistics/g1-stats-Spray_the_bowl_and_wipe_it_and_stack_it_up.json + stat_state_key: states + state_max: + - 0.058098502457141876 + - 0.8378645777702332 + - 1.24748694896698 + - 0.02642994001507759 + - -0.02432899735867977 + - 0.052110809832811356 + - -0.03269302845001221 + - 0.03115934133529663 + - -0.25718754529953003 + - -0.3639061152935028 + - 1.085662603378296 + - 1.7141422033309937 + - 0.9538694620132446 + - 1.6335294246673584 + - 0.9592653512954712 + - 1.3684067726135254 + - 0.581366777420044 + - 1.3705159425735474 + - 1.3769994974136353 + - 0.4176492989063263 + - 0.7442338466644287 + - 0.8120271563529968 + - 0.05709284916520119 + - 0.7760385274887085 + - 1.2864466905593872 + - 0.12685301899909973 + - 0.33431577682495117 + - 0.8156329393386841 + - 0.2588168680667877 + - 0.37684208154678345 + - 0.5529751181602478 + - 0.7785804867744446 + state_min: + - -1.0181907415390015 + - -0.21951374411582947 + - 0.3024940490722656 + - -1.4405375719070435 + - -1.7201621532440186 + - -1.419105887413025 + - -1.718001127243042 + - -0.9075884222984314 + - -0.8965954184532166 + - -1.0760161876678467 + - 0.021206939592957497 + - 0.03394134342670441 + - 0.023895293474197388 + - 0.02167615294456482 + - -1.6115427017211914 + - -0.14754977822303772 + - -1.022362232208252 + - -0.9756118059158325 + - -0.9642987251281738 + - -1.1772382259368896 + - -1.020795226097107 + - -1.4939295053482056 + - -0.776901364326477 + - -0.36998897790908813 + - -0.688661515712738 + - -1.1983625888824463 + - -1.1220992803573608 + - -1.1137467622756958 + - -0.25610852241516113 + - -0.25414812564849854 + - -0.6856858730316162 + - 0.6628404259681702 + use_norm_mask: false + model: + center_crop: + size: + - 224 + - 224 + color_jitter: + brightness: 0.2 + contrast: + - 0.8 + - 1.2 + hue: 0.05 + saturation: + - 0.8 + - 1.2 + img_aug: true + normalize: + mean: + - 0.485 + - 0.456 + - 0.406 + std: + - 0.229 + - 0.224 + - 0.225 + resize: + size: + - 256 + - 480 + repack: + action_key: action + conditions: [] + dataset_name: we-g1-teleop + image_keys: + - video + instruction_key: annotation.human.task_description + pad_action_dim: 36 + pad_state_dim: 32 + state_key: state + video_backend: decord +debug: + value: false +environment_variables: + value: + CUDA_VISIBLE_DEVICES: 6,7 + DATA_HOME: /hfm/data + HF_HOME: /hfm/cache + HF_LEROBOT_HOME: /hfm/data/lerobot + HF_TOKEN: hf_...adRb + LOCAL_RANK: "0" + LOCAL_WORLD_SIZE: "2" + MASTER_ADDR: nebula101 + MASTER_PORT: "42463" + OMP_NUM_THREADS: "32" + PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION: python + RANK: "0" + TORCH_HOME: /hfm/cache + UV_CACHE_DIR: /hfm/cache + WANDB_API_KEY: 90e...5c06 + WE_HOME: /hfm + WORLD_SIZE: "2" +eval: + value: false +exp: + value: act-spray-the-bowl-and-wipe-it-and-stack-it-up +log: + value: + log_freq: 100 + logging_dir: logs + report_to: wandb +model: + value: + action_dim: 36 + chunk_size: 100 + dim_feedforward: 3200 + dim_model: 512 + dropout: 0.1 + feedforward_activation: relu + kl_weight: 10 + latent_dim: 32 + n_action_steps: 100 + n_decoder_layers: 1 + n_encoder_layers: 4 + n_heads: 8 + n_obs_steps: 1 + n_vae_encoder_layers: 4 + pre_norm: false + state_dim: 32 + temporal_ensemble_coeff: null + use_vae: true +seed: + value: 2026 +timestamp: + value: "2601240214" +train: + value: + checkpointing_steps: 5000 + data_parallel: ddp + deepspeed_config: /home/jliu/we_learn/src/InternVLA/config/deepseeds/zero3.json + enable_gradient_checkpointing: true + enable_mixed_precision_training: true + gradient_accumulation_steps: 1 + hf_token: .hf_token + learning_rate: 0.0001 + lora: false + lr_scheduler_kwargs: + betas: + - 0.95 + - 0.999 + eps: 1e-08 + weight_decay: 1e-06 + lr_scheduler_type: cosine + max_checkpoints_to_keep: null + max_grad_norm: 1 + max_training_steps: 42000 + mixed_precision: bf16 + name: act-g1-real + num_train_epochs: null + num_workers: 8 + output_dir: .runs + overfit_single_batch: false + reduce_in_full_precision: true + resume_from_checkpoint: null + sharding_strategy: full-shard + skip_resumed_steps: false + train_batch_size: 32 + val_batch_size: 16 + val_num_batches: 20 + validation_steps: 500 + warmup_ratio: null + warmup_steps: 1000 +wandb: + value: + entity: jliu530-soochow-university + group: null + id: null + name: null + project: hfm + resume: allow diff --git a/act/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240214/wandb/run-20260124_021447-qcb38sob/files/output.log b/act/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240214/wandb/run-20260124_021447-qcb38sob/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..60b8ea6afe0691cb279f364166d8ee79746f00b8 --- /dev/null +++ b/act/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240214/wandb/run-20260124_021447-qcb38sob/files/output.log @@ -0,0 +1,247 @@ +[2;36m[02:14:48 01/24][0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Saved configuration to .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m ]8;id=805427;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=514750;file:///home/jliu/we_learn/scripts/train.py#129\[2m129[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Training configurations: ]8;id=2657;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=644039;file:///home/jliu/we_learn/scripts/train.py#182\[2m182[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> training task: [32m'act-g1-real'[0m ]8;id=471497;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=12046;file:///home/jliu/we_learn/scripts/train.py#183\[2m183[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> run name: act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m ]8;id=329497;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=220436;file:///home/jliu/we_learn/scripts/train.py#184\[2m184[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> seed: [1;36m2026[0m ]8;id=844246;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=394625;file:///home/jliu/we_learn/scripts/train.py#185\[2m185[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> mixed precision: torch.bfloat16 ]8;id=356929;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=94020;file:///home/jliu/we_learn/scripts/train.py#186\[2m186[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> warmup steps: [1;36m1000[0m ]8;id=147732;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=919050;file:///home/jliu/we_learn/scripts/train.py#187\[2m187[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> validation steps: [1;36m500[0m ]8;id=25941;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=888976;file:///home/jliu/we_learn/scripts/train.py#188\[2m188[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> checkpoint steps: [1;36m5000[0m ]8;id=95089;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=418144;file:///home/jliu/we_learn/scripts/train.py#189\[2m189[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> max gradient norm: [1;36m1.0[0m ]8;id=122104;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=446109;file:///home/jliu/we_learn/scripts/train.py#190\[2m190[0m]8;;\ +/home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/albumentations/__init__.py:13: UserWarning: A new version of Albumentations is available: 2.0.8 (you have 1.4.18). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1. + check_for_updates() +Loading external config: we.data.gr00t.data_config.WeUnitreeG1LocoDataConfig +Initialized dataset Spray_the_bowl_and_wipe_it_and_stack_it_up with EmbodimentTag.NEW_EMBODIMENT +Loading external config: we.data.gr00t.data_config.WeUnitreeG1LocoDataConfig +Initialized dataset Spray_the_bowl_and_wipe_it_and_stack_it_up with EmbodimentTag.NEW_EMBODIMENT +[2;36m[02:14:49 01/24][0m[2;36m [0m[34mINFO [0m | >> |=> Num training samples: ]8;id=917958;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=514883;file:///home/jliu/we_learn/scripts/train.py#193\[2m193[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Training dataset size: [1;36m103[0m,[1;36m235[0m ]8;id=440366;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=591396;file:///home/jliu/we_learn/scripts/train.py#194\[2m194[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Val dataset size: [1;36m103[0m,[1;36m235[0m ]8;id=26608;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=603472;file:///home/jliu/we_learn/scripts/train.py#196\[2m196[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Initialize optimizers and schedulers[33m...[0m ]8;id=738892;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=548632;file:///home/jliu/we_learn/scripts/train.py#199\[2m199[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m ***** Running training ***** ]8;id=450268;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=492873;file:///home/jliu/we_learn/scripts/train.py#203\[2m203[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num training examples = [1;36m103235[0m ]8;id=928955;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=127940;file:///home/jliu/we_learn/scripts/train.py#204\[2m204[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Max training Epochs = [1;36m27[0m ]8;id=749231;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=974614;file:///home/jliu/we_learn/scripts/train.py#205\[2m205[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Total optimization steps = [1;36m42000[0m ]8;id=322570;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=574775;file:///home/jliu/we_learn/scripts/train.py#206\[2m206[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num steps Per Epoch = [1;36m1614[0m ]8;id=843992;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=927413;file:///home/jliu/we_learn/scripts/train.py#207\[2m207[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Effective training epochs = [1;36m52.04[0m ]8;id=282104;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=9727;file:///home/jliu/we_learn/scripts/train.py#208\[2m208[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Global train batch size [1m([0mw. parallel, distributed & accumulation[1m)[0m = [1;36m64[0m ]8;id=480486;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=842580;file:///home/jliu/we_learn/scripts/train.py#209\[2m209[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Device train batch size = [1;36m32[0m ]8;id=209544;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=272745;file:///home/jliu/we_learn/scripts/train.py#210\[2m210[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Gradient Accumulation steps = [1;36m1[0m ]8;id=866305;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=521307;file:///home/jliu/we_learn/scripts/train.py#211\[2m211[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> |=> Num processes [1m([0mGPUs[1m)[0m = [1;36m2[0m ]8;id=35253;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=871789;file:///home/jliu/we_learn/scripts/train.py#212\[2m212[0m]8;;\ +[2;36m[02:14:50 01/24][0m[2;36m [0m[34mINFO [0m | >> TensorFlow version [1;36m2.20[0m.[1;36m0[0m available. ]8;id=459749;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/datasets/config.py\[2mconfig.py[0m]8;;\[2m:[0m]8;id=722818;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/datasets/config.py#112\[2m112[0m]8;;\ +[2;36m [0m[2;36m [0m[34mINFO [0m | >> [1m[[0m*[1m][0m Accelerator runs in: .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m ]8;id=966756;file:///home/jliu/we_learn/scripts/train.py\[2mtrain.py[0m]8;;\[2m:[0m]8;id=191940;file:///home/jliu/we_learn/scripts/train.py#219\[2m219[0m]8;;\ +Traing steps: 24%|█████████████████████████▍ | 10000/42000 [1:28:05<5:05:02, 1.75it/s, loss=0.0807, lr=8.9e-05] + +[2;36m[02:54:26 01/24][0m[2;36m [0m[34mINFO [0m | >> Saving current state to ]8;id=680052;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/accelerator.py\[2maccelerator.py[0m]8;;\[2m:[0m]8;id=279409;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/accelerator.py#3644\[2m3644[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_5000 [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Model weights saved in ]8;id=898249;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=431290;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#114\[2m114[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_5000/model.safetens [2m [0m +[2;36m [0m ors [2m [0m +[2;36m[02:54:27 01/24][0m[2;36m [0m[34mINFO [0m | >> Optimizer state saved in ]8;id=224;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=473149;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#121\[2m121[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_5000/optimizer.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Scheduler state saved in ]8;id=639771;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=832328;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#128\[2m128[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_5000/scheduler.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Sampler state for dataloader [1;36m0[0m saved in ]8;id=788377;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=572992;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#145\[2m145[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_5000/sampler.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Sampler state for dataloader [1;36m1[0m saved in ]8;id=374604;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=162278;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#145\[2m145[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_5000/sampler_1.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Random states saved in ]8;id=806908;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=971194;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#176\[2m176[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_5000/random_states_ [2m [0m +[2;36m [0m 0.pkl [2m [0m +Saved state to .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240214/checkpoints/ckpt_5000 +[2;36m[03:42:57 01/24][0m[2;36m [0m[34mINFO [0m | >> Saving current state to ]8;id=755470;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/accelerator.py\[2maccelerator.py[0m]8;;\[2m:[0m]8;id=983054;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/accelerator.py#3644\[2m3644[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_10000 [2m [0m +[2;36m[03:42:59 01/24][0m[2;36m [0m[34mINFO [0m | >> Model weights saved in ]8;id=616494;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=137590;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#114\[2m114[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_10000/model.safeten [2m [0m +[2;36m [0m sors [2m [0m +[2;36m[03:43:00 01/24][0m[2;36m [0m[34mINFO [0m | >> Optimizer state saved in ]8;id=304800;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=88534;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#121\[2m121[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_10000/optimizer.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Scheduler state saved in ]8;id=141920;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=821494;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#128\[2m128[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_10000/scheduler.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Sampler state for dataloader [1;36m0[0m saved in ]8;id=532364;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=432054;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#145\[2m145[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_10000/sampler.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Sampler state for dataloader [1;36m1[0m saved in ]8;id=846104;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=206251;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#145\[2m145[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_10000/sampler_1.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Random states saved in ]8;id=930895;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=77000;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#176\[2m176[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_10000/random_states [2m [0m +[2;36m [0m _0.pkl [2m [0m +Saved state to .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240214/checkpoints/ckpt_10000 +[2;36m[04:58:19 01/24][0m[2;36m [0m[34mINFO [0m | >> Saving current state to ]8;id=370569;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/accelerator.py\[2maccelerator.py[0m]8;;\[2m:[0m]8;id=979940;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/accelerator.py#3644\[2m3644[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-0[0m [2m [0m +[2;36m [0m [1;36m4[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_15000 [2m [0m +[2;36m[04:58:22 01/24][0m[2;36m [0m[34mINFO [0m | >> Model weights saved in ]8;id=535180;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=499147;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#114\[2m114[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-[0m [2m [0m +[2;36m [0m [1;36m04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_15000/model.safetensors [2m [0m +[2;36m[04:58:23 01/24][0m[2;36m [0m[34mINFO [0m | >> Optimizer state saved in ]8;id=341880;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=958353;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#121\[2m121[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-[0m [2m [0m +[2;36m [0m [1;36m04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_15000/optimizer.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Scheduler state saved in ]8;id=599385;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=648655;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#128\[2m128[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-[0m [2m [0m +[2;36m [0m [1;36m04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_15000/scheduler.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Sampler state for dataloader [1;36m0[0m saved in ]8;id=544854;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=217040;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#145\[2m145[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-[0m [2m [0m +[2;36m [0m [1;36m04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_15000/sampler.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Sampler state for dataloader [1;36m1[0m saved in ]8;id=627278;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=244660;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#145\[2m145[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-[0m [2m [0m +[2;36m [0m [1;36m04[0m.b64.gpus2.[1;36m2601240214[0m/checkpoints/ckpt_15000/sampler_1.bin [2m [0m +[2;36m [0m[2;36m [0m[34mINFO [0m | >> Random states saved in ]8;id=418546;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py\[2mcheckpointing.py[0m]8;;\[2m:[0m]8;id=35811;file:///home/jliu/we_learn/.venv-pusht/lib/python3.10/site-packages/accelerate/checkpointing.py#176\[2m176[0m]8;;\ +[2;36m [0m .runs/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.[1;36m0e-[0m [2m [0m +[2;36m [0m 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0000000000000000000000000000000000000000..552d6b40c1bf3734308336f8f19d3ce85477055f --- /dev/null +++ b/act/act-g1-real/act-spray-the-bowl-and-wipe-it-and-stack-it-up.g1-real.cosin.lr1.0e-04.b64.gpus2.2601240214/wandb/run-20260124_021447-qcb38sob/files/requirements.txt @@ -0,0 +1,238 @@ +aiosignal==1.4.0 +transforms3d==0.4.2 +annotated-doc==0.0.4 +hf_transfer==0.1.9 +nvidia-cusolver-cu12==11.7.3.90 +pathspec==0.12.1 +gast==0.7.0 +pydantic==2.10.6 +antlr4-python3-runtime==4.9.3 +tensorflow-graphics==2021.12.3 +python-dotenv==1.2.1 +diffusers==0.30.2 +mergedeep==1.3.4 +Farama-Notifications==0.0.4 +mypy==1.18.2 +google-pasta==0.2.0 +deepdiff==8.6.1 +simplejpeg==1.9.0 +python-dateutil==2.9.0.post0 +nvidia-curand-cu12==10.3.9.90 +torchaudio==2.8.0 +pyyaml-include==1.4.1 +eval_type_backport==0.2.2 +typing_extensions==4.15.0 +array_record==0.8.1 +h5py==3.14.0 +cmake==4.1.0 +platformdirs==4.4.0 +importlib_resources==6.5.2 +params_proto==2.13.2 +toml==0.10.2 +xxhash==3.5.0 +simple-parsing==0.1.7 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