(dl_model) steeve@hulk:~/Git/Practical-Deep-Learning-at-Scale-with-MLFlow/chapter01$ python first_dl.py 
### download IMDb data to local folder
/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/urllib3/connectionpool.py:1045: InsecureRequestWarning: Unverified HTTPS request is being made to host 'pl-flash-data.s3.amazonaws.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
  warnings.warn(
Using custom data configuration default-30f0d85b35c12b3d
Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/steeve/.cache/huggingface/datasets/csv/default-30f0d85b35c12b3d/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23...
Dataset csv downloaded and prepared to /home/steeve/.cache/huggingface/datasets/csv/default-30f0d85b35c12b3d/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23. Subsequent calls will reuse this data.
Parameter 'function'=functools.partial(<function TextDataSource._transform_label at 0x7fd7cd510160>, {'negative': 0, 'positive': 1}, 'sentiment') of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22500/22500 [00:01<00:00, 19285.39ex/s]
/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/flash/text/classification/data.py:170: FutureWarning: rename_column_ is deprecated and will be removed in the next major version of datasets. Use DatasetDict.rename_column instead.
  dataset_dict.rename_column_(target, "labels")
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:03<00:00,  6.62ba/s]
Using custom data configuration default-434f86cfa4832864
Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/steeve/.cache/huggingface/datasets/csv/default-434f86cfa4832864/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23...
Dataset csv downloaded and prepared to /home/steeve/.cache/huggingface/datasets/csv/default-434f86cfa4832864/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23. Subsequent calls will reuse this data.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2500/2500 [00:00<00:00, 19904.06ex/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  8.02ba/s]
Using custom data configuration default-4cdf6a7c2b47f824
Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/steeve/.cache/huggingface/datasets/csv/default-4cdf6a7c2b47f824/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23...
Dataset csv downloaded and prepared to /home/steeve/.cache/huggingface/datasets/csv/default-4cdf6a7c2b47f824/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23. Subsequent calls will reuse this data.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2500/2500 [00:00<00:00, 19868.95ex/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  6.43ba/s]
### define a text classifier 
Using 'prajjwal1/bert-tiny' provided by Hugging Face/transformers (https://github.com/huggingface/transformers).
Some weights of the model checkpoint at prajjwal1/bert-tiny were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias']
- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
### define the trainer
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
### fine tune the pretrained model to get a new model for sentiment classification
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]
### download IMDb data to local folder
### download IMDb data to local folder
Using custom data configuration default-51398f4182682e8c
Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/steeve/.cache/huggingface/datasets/csv/default-51398f4182682e8c/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23...
Using custom data configuration default-51398f4182682e8c
Dataset csv downloaded and prepared to /home/steeve/.cache/huggingface/datasets/csv/default-51398f4182682e8c/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23. Subsequent calls will reuse this data.
Reusing dataset csv (/home/steeve/.cache/huggingface/datasets/csv/default-51398f4182682e8c/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23)
Parameter 'function'=functools.partial(<function TextDataSource._transform_label at 0x7fee05bb3550>, {'negative': 0, 'positive': 1}, 'sentiment') of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  0%|                                                                                                                                 | 0/22500 [00:00<?, ?ex/s]Parameter 'function'=functools.partial(<function TextDataSource._transform_label at 0x7f42fadb8550>, {'negative': 0, 'positive': 1}, 'sentiment') of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22500/22500 [00:01<00:00, 18851.46ex/s]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22500/22500 [00:01<00:00, 17720.26ex/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:05<00:00,  4.23ba/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:05<00:00,  4.24ba/s]
Using custom data configuration default-8171013d43f1aea9
Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/steeve/.cache/huggingface/datasets/csv/default-8171013d43f1aea9/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23...
Dataset csv downloaded and prepared to /home/steeve/.cache/huggingface/datasets/csv/default-8171013d43f1aea9/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23. Subsequent calls will reuse this data.
  0%|                                                                                                                                  | 0/2500 [00:00<?, ?ex/s]Using custom data configuration default-8171013d43f1aea9
Reusing dataset csv (/home/steeve/.cache/huggingface/datasets/csv/default-8171013d43f1aea9/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23)
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2500/2500 [00:00<00:00, 17335.47ex/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2500/2500 [00:00<00:00, 15979.84ex/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  5.10ba/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  5.79ba/s]
Using custom data configuration default-b0b9cf190e372e5b
Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/steeve/.cache/huggingface/datasets/csv/default-b0b9cf190e372e5b/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23...
0 tables [00:00, ? tables/s]Using custom data configuration default-b0b9cf190e372e5b
Dataset csv downloaded and prepared to /home/steeve/.cache/huggingface/datasets/csv/default-b0b9cf190e372e5b/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23. Subsequent calls will reuse this data.
  0%|                                                                                                                                  | 0/2500 [00:00<?, ?ex/s]Reusing dataset csv (/home/steeve/.cache/huggingface/datasets/csv/default-b0b9cf190e372e5b/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23)
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2500/2500 [00:00<00:00, 19624.75ex/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2500/2500 [00:00<00:00, 15123.79ex/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  4.52ba/s]
### define a text classifier 
Using 'prajjwal1/bert-tiny' provided by Hugging Face/transformers (https://github.com/huggingface/transformers).
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  5.12ba/s]
### define a text classifier 
Using 'prajjwal1/bert-tiny' provided by Hugging Face/transformers (https://github.com/huggingface/transformers).
Some weights of the model checkpoint at prajjwal1/bert-tiny were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
### define the trainer
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
### fine tune the pretrained model to get a new model for sentiment classification
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 116, in spawn_main
    exitcode = _main(fd, parent_sentinel)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 125, in _main
    prepare(preparation_data)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 236, in prepare
    _fixup_main_from_path(data['init_main_from_path'])
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 287, in _fixup_main_from_path
    main_content = runpy.run_path(main_path,
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/runpy.py", line 265, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/runpy.py", line 97, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/steeve/Git/Practical-Deep-Learning-at-Scale-with-MLFlow/chapter01/first_dl.py", line 23, in <module>
    trainer.finetune(classifier_model, datamodule=datamodule, strategy="freeze")
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/flash/core/trainer.py", line 165, in finetune
    return super().fit(model, train_dataloader, val_dataloaders, datamodule)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 552, in fit
    self._run(model)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 922, in _run
    self._dispatch()
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 990, in _dispatch
    self.accelerator.start_training(self)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in start_training
    self.training_type_plugin.start_training(trainer)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py", line 158, in start_training
    mp.spawn(self.new_process, **self.mp_spawn_kwargs)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn
    return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 189, in start_processes
    process.start()
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/process.py", line 121, in start
    self._popen = self._Popen(self)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
    return Popen(process_obj)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__
    super().__init__(process_obj)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__
    self._launch(process_obj)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 42, in _launch
    prep_data = spawn.get_preparation_data(process_obj._name)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 154, in get_preparation_data
    _check_not_importing_main()
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 134, in _check_not_importing_main
    raise RuntimeError('''
RuntimeError: 
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.
Some weights of the model checkpoint at prajjwal1/bert-tiny were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']
- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at prajjwal1/bert-tiny and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
### define the trainer
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
### fine tune the pretrained model to get a new model for sentiment classification
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 116, in spawn_main
    exitcode = _main(fd, parent_sentinel)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 125, in _main
    prepare(preparation_data)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 236, in prepare
    _fixup_main_from_path(data['init_main_from_path'])
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 287, in _fixup_main_from_path
    main_content = runpy.run_path(main_path,
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/runpy.py", line 265, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/runpy.py", line 97, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/steeve/Git/Practical-Deep-Learning-at-Scale-with-MLFlow/chapter01/first_dl.py", line 23, in <module>
    trainer.finetune(classifier_model, datamodule=datamodule, strategy="freeze")
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/flash/core/trainer.py", line 165, in finetune
    return super().fit(model, train_dataloader, val_dataloaders, datamodule)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 552, in fit
    self._run(model)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 922, in _run
    self._dispatch()
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 990, in _dispatch
    self.accelerator.start_training(self)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in start_training
    self.training_type_plugin.start_training(trainer)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py", line 158, in start_training
    mp.spawn(self.new_process, **self.mp_spawn_kwargs)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn
    return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 189, in start_processes
    process.start()
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/process.py", line 121, in start
    self._popen = self._Popen(self)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
    return Popen(process_obj)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__
    super().__init__(process_obj)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__
    self._launch(process_obj)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 42, in _launch
    prep_data = spawn.get_preparation_data(process_obj._name)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 154, in get_preparation_data
    _check_not_importing_main()
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/multiprocessing/spawn.py", line 134, in _check_not_importing_main
    raise RuntimeError('''
RuntimeError: 
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.
Traceback (most recent call last):
  File "first_dl.py", line 23, in <module>
    trainer.finetune(classifier_model, datamodule=datamodule, strategy="freeze")
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/flash/core/trainer.py", line 165, in finetune
    return super().fit(model, train_dataloader, val_dataloaders, datamodule)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 552, in fit
    self._run(model)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 922, in _run
    self._dispatch()
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 990, in _dispatch
    self.accelerator.start_training(self)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in start_training
    self.training_type_plugin.start_training(trainer)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py", line 158, in start_training
    mp.spawn(self.new_process, **self.mp_spawn_kwargs)
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn
    return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 198, in start_processes
    while not context.join():
  File "/home/steeve/Anaconda3/envs/dl_model/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 149, in join
    raise ProcessExitedException(
torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with exit code 1