BERT FineTuning with Cloud TPU: Sentence and Sentence-Pair Classification Tasks

This tutorial shows you how to train the Bidirectional Encoder Representations from Transformers (BERT) model on Cloud TPU.

BERT is a method of pre-training language representations. Pre-training refers to how BERT is first trained on a large source of text, such as Wikipedia. You can then apply the training results to other Natural Language Processing (NLP) tasks, such as question answering and sentiment analysis. With BERT and Cloud TPU, you can train a variety of NLP models in about 30 minutes.

For more information about BERT, see the following resources:

• Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Objectives

• Create a Cloud Storage bucket to hold your dataset and model output.
• Clone the BERT repository and other required files.
• Run the training job.
• Verify the output results.

Costs

This tutorial uses billable components of Google Cloud, including:

• Compute Engine
• Cloud TPU
• Cloud Storage

Use the pricing calculator to generate a cost estimate based on your projected usage. New Google Cloud users might be eligible for a free trial.

Before you begin

This section provides information on setting up Cloud Storage bucket and a Compute Engine VM.

Important: Set up your Compute Engine VM, your Cloud TPU node and your Cloud Storage bucket in the same region/zone to reduce network latency and network costs.

1. Open a Cloud Shell window.

   Open Cloud Shell

2. Create a variable for your project's name.

   export PROJECT_NAME=project-name
   

3. Configure gcloud command-line tool to use the project where you want to create Cloud TPU.

   gcloud config set project ${PROJECT_NAME}
   

4. Create a Cloud Storage bucket using the following command:

   Note: In the following command, replace bucket-name with the name you want to assign to your bucket.

   gsutil mb -p ${PROJECT_NAME} -c standard -l us-central1 -b on gs://bucket-name
   

   This Cloud Storage bucket stores the data you use to train your model and the training results.

   In order for the Cloud TPU to read and write to the storage bucket, the service account for your project needs read/write or Admin permissions on it. See the section on storage buckets for how to view and set those permissions.

5. Launch a Compute Engine VM and Cloud TPU using the ctpu up command.

   $ ctpu up --tpu-size=v3-8 \
    --machine-type=n1-standard-8 \
    --zone=us-central1-b \
    --tf-version=1.15 \
    --name=bert-tutorial
    [optional flag: --project]
   

   Note: If you have more than one project, you must specify the project name with the --project flag. If the Compute Engine VM name is not specified with the --name flag, it defaults to your username.

6. The configuration you specified appears. Enter y to approve or n to cancel.

7. When the ctpu up command has finished executing, verify that your shell prompt has changed from username@project to username@vm-name. This change shows that you are now logged into your Compute Engine VM.

   Note: If you are not connected to the Compute Engine instance, you can connect by running the following commands, replacing vm-name with your VM name. The VM name defaults to your username:

   gcloud compute ssh vm-name --zone=us-central1-b
   

   (vm)$ export TPU_NAME=bert-tutorial
   

   As you continue these instructions, run each command that begins with (vm)$ in your VM session window.

Clone the BERT repository

From your Compute Engine virtual machine (VM), clone the BERT repository.

(vm)$ git clone https://github.com/google-research/bert

Download download_glue_data.py

This tutorial uses the General Language Understanding Evaluation (GLUE) benchmark to evaluate and analyze the performance of the model. To use this benchmark, download the download_glue_data.py script using the following git clone command:

(vm)$ git clone https://gist.github.com/60c2bdb54d156a41194446737ce03e2e.git download_glue_data

Download the GLUE data

Next, run the download_glue_data.py on your Compute Engine VM.

(vm)$ python3 download_glue_data/download_glue_data.py --data_dir $HOME/glue_data --tasks all

Define parameter values

Next, define several parameter values that are required when you train and evaluate your model:

(vm)$ export STORAGE_BUCKET=gs://bucket-name

(vm)$ export PYTHONPATH="${PYTHONPATH}:/usr/share/tpu/models"
(vm)$ export BERT_BASE_DIR=gs://cloud-tpu-checkpoints/bert/uncased_L-12_H-768_A-12
(vm)$ export GLUE_DIR=$HOME/glue_data
(vm)$ export TASK_NAME=MRPC

Parameter	Description
STORAGE_BUCKET	This is the name of the Cloud Storage bucket that you created in the **Before you begin** section.
BERT_BASE_DIR	This is the directory that contains files used for training. This tutorial uses a folder within the Cloud Storage bucket. You do not have to create this folder beforehand. The script creates the folder if it does not already exist.
GLUE_DIR	This is the directory that contains the GLUE training data.
TASK_NAME	This is the type of training you want to perform. For this tutorial, use the value `MRPC`, which specifies the Microsoft Research Paraphrase Corpus.

Train the model

From your Compute Engine VM, run the following command.

python3 ./bert/run_classifier.py \
--task_name=${TASK_NAME} \
--do_train=true \
--do_eval=true \
--data_dir=${GLUE_DIR}/${TASK_NAME} \
--vocab_file=${BERT_BASE_DIR}/vocab.txt \
--bert_config_file=${BERT_BASE_DIR}/bert_config.json \
--init_checkpoint=${BERT_BASE_DIR}/bert_model.ckpt \
--max_seq_length=128 \
--train_batch_size=32 \
--learning_rate=2e-5 \
--num_train_epochs=3.0 \
--output_dir=${STORAGE_BUCKET}/${TASK_NAME}-output/ \
--use_tpu=True \
--tpu_name=${TPU_NAME}

Verify your results

The training should take less than 5 minutes. When the training completes, you should see results similar to the following:

***** Eval results *****
  eval_accuracy = 0.845588
  eval_loss = 0.64990824
  global_step = 343
  loss = 0.34979442

Clean up

To avoid incurring charges to your GCP account for the resources used in this topic:

1. Disconnect from the Compute Engine VM:

   (vm)$ exit
   

   Your prompt should now be user@projectname, showing you are in the Cloud Shell.

2. In your Cloud Shell, run ctpu delete with the --zone flag you used when you set up the Cloud TPU to delete your Compute Engine VM and your Cloud TPU:

   $ ctpu delete --zone=us-central1-b
   

   Important: If you set the TPU resources name when you ran ctpu up, you must specify that name with the --name flag when you run ctpu delete in order to shut down your TPU resources.

3. Run ctpu status to make sure you have no instances allocated to avoid unnecessary charges for TPU usage. The deletion might take several minutes. A response like the one below indicates there are no allocated instances:

   $ ctpu status --zone=us-central1-b
   

   2018/04/28 16:16:23 WARNING: Setting zone to "--zone=us-central1-b"
   No instances currently exist.
       Compute Engine VM:     --
       Cloud TPU:             --
   

4. Run gsutil as shown, replacing bucket-name with the name of the Cloud Storage bucket you created for this tutorial:

   $ gsutil rm -r gs://bucket-name
   

Note: For free storage limits and other pricing information, see the Cloud Storage pricing guide.