AttributeError при использовании нескольких графических процессоров даже при использовании ddp

Question

Я получаю ошибку "Атрибут не найден" при использовании нескольких графических процессоров. Код отлично работает при использовании одного графического процессора. Я также использую ddp, как было предложено. Я использую pytorch lightning. Есть предложения?

Выслеживать:Traceback (most recent call last): File "/home/sohigre/STL/stl_bert_trial_lightning.py", line 245, in <module> trainer.fit(model) File "/home/sohigre/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 952, in fit self.ddp_train(task, model) File "/home/sohigre/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 500, in ddp_train self.optimizers, self.lr_schedulers, self.optimizer_frequencies = self.init_optimizers(model) File "/home/sohigre/anaconda3/lib/python3.7/site-packages/pytorch_lightning/trainer/optimizers.py", line 18, in init_optimizers optim_conf = model.configure_optimizers() File "/home/sohigre/STL/stl_bert_trial_lightning.py", line 178, in configure_optimizers total_steps = len(self.train_dataloader()) * self.max_epochs File "/home/sohigre/STL/stl_bert_trial_lightning.py", line 109, in train_dataloader return DataLoader(self.ds_train, batch_size=self.batch_size,num_workers=4) File "/home/sohigre/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 594, in __getattr__ type(self).__name__, name)) AttributeError: 'Abuse_lightning' object has no attribute 'ds_train'

Фрагмент кода:

    # download only (not called on every GPU, just the root GPU per node)
    df = pd.read_csv(self.filename)
    self.df_train, self.df_test = train_test_split(df, test_size=0.2, random_state=self.RANDOM_SEED)
    self.df_val, self.df_test = train_test_split(self.df_test, test_size=0.5, random_state=self.RANDOM_SEED)
    self.tokenizer = BertTokenizer.from_pretrained(self.PRE_TRAINED_MODEL_NAME)
    self.ds_train = AbuseDataset(reviews=self.df_train.comment.to_numpy(), targets=self.df_train.Score.to_numpy(),
                          tokenizer=self.tokenizer,max_len=self.max_len)
    self.ds_val = AbuseDataset(reviews=self.df_val.comment.to_numpy(), targets=self.df_val.Score.to_numpy(),
                          tokenizer=self.tokenizer,max_len=self.max_len)
    self.ds_test = AbuseDataset(reviews=self.df_test.comment.to_numpy(), targets=self.df_test.Score.to_numpy(),
                          tokenizer=self.tokenizer,max_len=self.max_len)

  @pl.data_loader
  def train_dataloader(self):
    
    return DataLoader(self.ds_train, batch_size=self.batch_size,num_workers=4)```