- Set Up the Environment and Prepare Data
a. Create a Google Cloud Project:
-
Create a new project on the Google Cloud Console.
-
Enable the AI Platform and Compute Engine APIs for your project.
b. Install the Required Tools:
-
Cloud SDK: Install the Google Cloud SDK.
-
Python Libraries: Install necessary libraries such as transformers, tensorflow, google-cloud-storage, etc.
pip install transformers tensorflow google-cloud-storage
c. Prepare Your Data:
-
Format your data: Ensure your dataset is in a format compatible with BERT, typically in a CSV or JSON format with text and labels.
-
Upload your data to a Cloud Storage bucket: This will allow the training job to access the data.
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:
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:
- Create a Dockerfile to set up the environment for your training job.
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"]
- Build and push the Docker image to Google Container Registry.
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:
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:
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:
model.save_pretrained('gs://your-bucket-name/bert_finetuned')
Additional Resources
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