In case you encounter an OOM issue while training with BERT in the future, you might want to consider implementing mixed-precision training or gradient checkpointing. These techniques can significantly reduce memory usage while maintaining the training speed, especially for models with large memory footprints.

Load your BERT model and tokenizer

model = BertForSequenceClassification.from_pretrained('bert-base-uncased') tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

Sample data (replace with your own dataset loading logic)

texts = ["Sample text 1", "Sample text 2", "Sample text 3"] labels = [0, 1, 0]

Tokenize and convert to PyTorch tensors

tokenized_texts = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') labels = torch.tensor(labels)

Create a DataLoader for handling batches

dataset = TensorDataset(tokenized_texts['input_ids'], tokenized_texts['attention_mask'], labels) batch_size = 4

Example of using mixed-precision training with APEX

model, optimizer = amp.initialize(model, optimizer, opt_level="O1")

Example of gradient accumulation

accumulation_steps = 2 loss_fn = torch.nn.CrossEntropyLoss()

`for epoch in range(5): total_loss = 0 for i, (input_ids, attention_mask, label) in enumerate(DataLoader(dataset, batch_size=batch_size)): with amp.autocast(): outputs = model(input_ids, attention_mask=attention_mask) loss = loss_fn(outputs.logits, label)

Perform gradient accumulation

` loss = loss / accumulation_steps with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward()

    if (i + 1) % accumulation_steps == 0 or i == len(dataset) - 1:
        optimizer.step()
        optimizer.zero_grad()

    total_loss += loss.item()

print(f"Epoch {epoch + 1}, Loss: {total_loss / len(dataset)}")`

amzn / zero-shot-rlhr

Intermittent CUDA - OOM issue. #3