WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

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Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.bias', 'vocab_projector.weight', 'vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight']
- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
<flow FSDPFlow step train[1] (input: 1)> failed:
    Internal error
Traceback (most recent call last):
  File "/metaflow/metaflow/cli.py", line 1172, in main
    start(auto_envvar_prefix="METAFLOW", obj=state)
  File "/metaflow/metaflow/_vendor/click/core.py", line 829, in __call__
    return self.main(args, kwargs)
  File "/metaflow/metaflow/_vendor/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/metaflow/metaflow/_vendor/click/core.py", line 1259, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/metaflow/metaflow/_vendor/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, ctx.params)
  File "/metaflow/metaflow/_vendor/click/core.py", line 610, in invoke
    return callback(args, kwargs)
  File "/metaflow/metaflow/_vendor/click/decorators.py", line 21, in new_func
    return f(get_current_context(), args, kwargs)
  File "/metaflow/metaflow/cli.py", line 581, in step
    task.run_step(
  File "/metaflow/metaflow/task.py", line 586, in run_step
    self._exec_step_function(step_func)
  File "/metaflow/metaflow/task.py", line 60, in _exec_step_function
    step_function()
  File "/metaflow/metaflow/plugins/parallel_decorator.py", line 34, in _step_func_with_setup
    step_func()
  File "/metaflow/gpu_profile.py", line 148, in func
    f(s)
  File "/metaflow/custom_decorators.py", line 32, in wrapper
    return function(args, kwargs)
  File "/metaflow/fsdp_flow.py", line 247, in train
    trainer.fit(
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 603, in fit
    call._call_and_handle_interrupt(
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 38, in _call_and_handle_interrupt
    return trainer_fn(args, kwargs)
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 645, in _fit_impl
    self._run(model, ckpt_path=self.ckpt_path)
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1098, in _run
    results = self._run_stage()
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1177, in _run_stage
    self._run_train()
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1190, in _run_train
    self._run_sanity_check()
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1255, in _run_sanity_check
    val_loop._reload_evaluation_dataloaders()
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 234, in _reload_evaluation_dataloaders
    self.trainer.reset_val_dataloader()
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1635, in reset_val_dataloader
    self.num_val_batches, self.val_dataloaders = self._data_connector._reset_eval_dataloader(
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 377, in _reset_eval_dataloader
    dataloaders = [self._prepare_dataloader(dl, mode=mode) for dl in dataloaders if dl is not None]
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 377, in <listcomp>
    dataloaders = [self._prepare_dataloader(dl, mode=mode) for dl in dataloaders if dl is not None]
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 283, in _prepare_dataloader
    sampler = self._resolve_sampler(dataloader, shuffle=shuffle, mode=mode)
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 300, in _resolve_sampler
    sampler = self._get_distributed_sampler(
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 339, in _get_distributed_sampler
    sampler = cls(dataloader.sampler, kwargs)
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/lightning_lite/utilities/distributed.py", line 300, in __init__
    super().__init__(_DatasetSamplerWrapper(sampler), args, kwargs)
  File "/metaflow/metaflow_FSDPFlow_linux-64_0194a13432df777baea5577cb5634714a2a36c5e/lib/python3.10/site-packages/torch/utils/data/distributed.py", line 73, in __init__
    raise ValueError(
ValueError: Invalid rank 1, rank should be in the interval [0, 0]