问当我在<< report_to = 'wandb‘>>中引入TrainingArguments时，模型停止了训练

EN

import wandb
wandb.login()
%env WANDB_LOG_MODEL=true

model = tr.BertForSequenceClassification.from_pretrained("/home/pc/minilm_model",num_labels=2)
model.to(device)

print("hello")

training_args = tr.TrainingArguments(
report_to = 'wandb',
output_dir='/home/pc/proj/results2', # output directory
num_train_epochs=10, # total number of training epochs
per_device_train_batch_size=16, # batch size per device during training
per_device_eval_batch_size=32, # batch size for evaluation
learning_rate=2e-5,
warmup_steps=1000, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
logging_dir='./logs', # directory for storing logs
logging_steps=1000,
evaluation_strategy="epoch",
save_strategy="no"
)

print("hello")

trainer = tr.Trainer(
model=model, # the instantiated  Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_data, # training dataset
eval_dataset=val_data, # evaluation dataset
compute_metrics=compute_metrics
)

Num examples = 12981
 Num Epochs = 20
 Instantaneous batch size per device = 16
 Total train batch size (w. parallel, distributed & accumulation) = 32
 Gradient Accumulation steps = 1
 Total optimization steps = 8120
Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"