The following section provides an example of fine-tuning a BERT model for sequence classification using the Hugging Face transformers library with TensorFlow. The dataset is downloaded into a mounted Parallelstore-backed volume, allowing the model training to directly read data from the volume.
Prerequisites
- Ensure your node has at least 8 GiB of memory available.
- Create a PersistentVolumeClaim requesting for a Parallelstore-backed volume.
Save the following YAML manifest (parallelstore-csi-job-example.yaml) for your model training Job.
apiVersion: batch/v1
kind: Job
metadata:
name: parallelstore-csi-job-example
spec:
template:
metadata:
annotations:
gke-parallelstore/cpu-limit: "0"
gke-parallelstore/memory-limit: "0"
spec:
securityContext:
runAsUser: 1000
runAsGroup: 100
fsGroup: 100
containers:
- name: tensorflow
image: jupyter/tensorflow-notebook@sha256:173f124f638efe870bb2b535e01a76a80a95217e66ed00751058c51c09d6d85d
command: ["bash", "-c"]
args:
- |
pip install transformers datasets
python - <<EOF
from datasets import load_dataset
dataset = load_dataset("glue", "cola", cache_dir='/data')
dataset = dataset["train"]
from transformers import AutoTokenizer
import numpy as np
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
tokenized_data = tokenizer(dataset["sentence"], return_tensors="np", padding=True)
tokenized_data = dict(tokenized_data)
labels = np.array(dataset["label"])
from transformers import TFAutoModelForSequenceClassification
from tensorflow.keras.optimizers import Adam
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")
model.compile(optimizer=Adam(3e-5))
model.fit(tokenized_data, labels)
EOF
volumeMounts:
- name: parallelstore-volume
mountPath: /data
volumes:
- name: parallelstore-volume
persistentVolumeClaim:
claimName: parallelstore-pvc
restartPolicy: Never
backoffLimit: 1
Apply the YAML manifest to the cluster.
kubectl apply -f parallelstore-csi-job-example.yaml
Check your data loading and model training progress with the following command:
POD_NAME=$(kubectl get pod | grep 'parallelstore-csi-job-example' | awk '{print $1}')
kubectl logs -f $POD_NAME -c tensorflow