learner.fit doesn't show result after every epoch

Hi @kaushaltrivedi , I used:

learner.fit(epochs=6, 
			lr=6e-5, 
			validate=True. 	# Evaluate the model after each epoch
			schedule_type="warmup_cosine") 

However, that code onlys checks after the whole training, not after each epoch. What could I do? Thanks

[amin-nejad]

Had the same issue of no metrics being printed at all, seems like it’s because the default setting of the logger is to only print warning messages, not info messages. If you are using the root logger (e.g. logger = logging.getLogger()), before you create the object, run this line:

logging.basicConfig(level=logging.NOTSET)

If you are defining a custom logger yourself (e.g. logger = logging.getLogger("my-logger")), before you create the object, run this line:

logging.basicConfig(level=logging.NOTSET)

Now the training process, will print out the loss as well as any other metrics you passed to the learner object.

Alternatively, you can always view the training process either during or afterwards, using tensorboard. The training process creates a folder called tensorboard with all the events files in there.

[vondersam]

No, I didn’t get any validation metric results, not even after each epoch. This is the code I’m using.

databunch = BertDataBunch(data_dir=BERT_DATA_PATH/fold, 
                                      label_dir=LABEL_PATH, 
                                      tokenizer=args['bert_model'], 
                                      train_file=f'train{is_masked}.csv', 
                                      val_file=f'val{is_masked}.csv',
                                      test_data=None,
                                      text_col="text", 
                                      label_col=labels_index,
                                      batch_size_per_gpu=args['train_batch_size'], 
                                      max_seq_length=args['max_seq_length'], 
                                      multi_gpu=multi_gpu, 
                                      multi_label=True, 
                                      model_type='bert')
learner = BertLearner.from_pretrained_model(databunch, 
                                        pretrained_path=args['bert_model'], 
                                        metrics=metrics, 
                                        device=device, 
                                        logger=logger, 
                                        finetuned_wgts_path=None, 
                                        warmup_steps=500,
                                        output_dir=fold_dir,
                                        is_fp16=args['fp16'],
                                        loss_scale=args['loss_scale'],
                                        multi_gpu=multi_gpu,  
                                        multi_label=True,
                                        logging_steps=50)
learner.fit(args['num_train_epochs'], lr=args['learning_rate'], schedule_type="warmup_linear")
learner.save_model()

And this is what the logs look like