1. Set Up the Environment and Prepare Data
**a. Create a Google Cloud Project:**
1. **Create a new project** on the [Google Cloud Console](https://console.cloud.google.com/).
2. **Enable the AI Platform and Compute Engine APIs** for your project.
**b. Install the Required Tools:**
1. **Cloud SDK:** Install the [Google Cloud SDK](https://cloud.google.com/sdk/docs/install).
2. **Python Libraries:** Install necessary libraries such as `transformers`, `tensorflow`, `google-cloud-storage`, etc.
```bash
pip install transformers tensorflow google-cloud-storage
```
**c. Prepare Your Data:**
1. **Format your data**: Ensure your dataset is in a format compatible with BERT, typically in a CSV or JSON format with text and labels.
2. **Upload your data to a Cloud Storage bucket**: This will allow the training job to access the data.
```bash
gsutil cp path/to/your/dataset.csv gs://your-bucket-name/dataset.csv
```
### 2. Configure the Training Job
**a. Create a Training Script:**
Create a Python script to fine-tune BERT. An example script (fine_tune_bert.py) might look like this:
```python
import os
import tensorflow as tf
from transformers import TFBertForSequenceClassification, BertTokenizer
from google.cloud import storage
def load_data(file_path):
# Implement this function to load and preprocess your data
pass
def main():
# Set up tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
# Load data
train_data = load_data('gs://your-bucket-name/dataset.csv')
# Tokenize data
train_encodings = tokenizer(train_data['text'], truncation=True, padding=True)
train_labels = train_data['label']
# Prepare TensorFlow dataset
train_dataset = tf.data.Dataset.from_tensor_slices((
dict(train_encodings),
train_labels
))
# Compile model
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# Train model
model.fit(train_dataset.shuffle(1000).batch(32), epochs=3, batch_size=32)
# Save model
model.save_pretrained('gs://your-bucket-name/bert_finetuned')
if __name__ == "__main__":
main()
```
**b. Create a Docker Container:**
1. **Create a Dockerfile** to set up the environment for your training job.
```Dockerfile
FROM tensorflow/tensorflow:2.4.1-gpu
RUN pip install transformers google-cloud-storage
COPY fine_tune_bert.py /fine_tune_bert.py
CMD ["python", "/fine_tune_bert.py"]
```
2. **Build and push the Docker image** to Google Container Registry.
```bash
docker build -t gcr.io/your-project-id/bert-finetune .
docker push gcr.io/your-project-id/bert-finetune
```
### 3. Submit the Training Job
**a. Use `gcloud` to submit the training job:**
```bash
gcloud ai-platform jobs submit training bert_finetune_$(date +%Y%m%d_%H%M%S) \
--scale-tier BASIC_GPU \
--master-image-uri gcr.io/your-project-id/bert-finetune \
--region us-central1 \
-- \
--dataset_path=gs://your-bucket-name/dataset.csv \
--output_dir=gs://your-bucket-name/bert_finetuned
```
### 4. Handle Model Checkpoints and Export the Model
**a. Configure Checkpointing:**
Modify your training script to save checkpoints:
```python
checkpoint_path = 'gs://your-bucket-name/checkpoints'
ckpt_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
verbose=1)
# Include this callback in your model.fit() call
model.fit(train_dataset.shuffle(1000).batch(32),
epochs=3,
batch_size=32,
callbacks=[ckpt_callback])
```
**b. Export the Model:**
Ensure your model is saved in a format suitable for serving:
```python
model.save_pretrained('gs://your-bucket-name/bert_finetuned')
```
### Additional Resources
- [Google Cloud AI Platform Training Documentation](https://cloud.google.com/ai-platform/training/docs)
- [Transformers Documentation](https://huggingface.co/transformers/training.html)
- [BERT Fine-Tuning Tutorial](https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/pytorch/...)
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