sebastiandavidlee-gr00t-legoDisassembly2Plate-yk14x

gr00t Model - phospho Training Pipeline

Error Traceback

We faced an issue while training your model.

Traceback (most recent call last):
  File "/root/src/helper.py", line 165, in predict
    trainer.train(timeout_seconds=timeout_seconds)
  File "/root/phosphobot/am/gr00t.py", line 1148, in train
    asyncio.run(
  File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run
    return runner.run(main)
           ^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run
    return self._loop.run_until_complete(task)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete
    return future.result()
           ^^^^^^^^^^^^^^^
  File "/root/phosphobot/am/gr00t.py", line 998, in run_gr00t_training
    raise RuntimeError(error_msg)
RuntimeError: Training process failed with exit code 1:
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/torch/nn/modules/linear.py", line 125, in forward
return F.linear(input, self.weight, self.bias)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 12.75 MiB is free. Process 29 has 79.23 GiB memory in use. Of the allocated memory 78.29 GiB is allocated by PyTorch, and 446.91 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
0%|          | 1/31000 [00:09<84:55:01,  9.86s/it]


The current batch size is too large for the GPU.
Please consider lowering it to fit in the memory.
We train on a 80GB A100 GPU.

Training parameters:

• Dataset: sebastiandavidlee/legoDisassembly2Plate
• Wandb run URL: None
• Epochs: 50
• Batch size: 49
• Training steps: None

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