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 the following billable components of Google Cloud:
- Compute Engine
- Cloud TPU
- Cloud Storage
To generate a cost estimate based on your projected usage,
use the pricing calculator.
Before you begin
This section provides information on setting up Cloud Storage bucket and a Compute Engine VM.
Open a Cloud Shell window.
Create a variable for your project's ID.
export PROJECT_ID=project-id
Configure Google Cloud CLI to use the project where you want to create Cloud TPU.
gcloud config set project ${PROJECT_ID}
The first time you run this command in a new Cloud Shell VM, an
Authorize Cloud Shell
page is displayed. ClickAuthorize
at the bottom of the page to allowgcloud
to make GCP API calls with your credentials.Create a Service Account for the Cloud TPU project.
gcloud beta services identity create --service tpu.googleapis.com --project $PROJECT_ID
The command returns a Cloud TPU Service Account with following format:
service-PROJECT_NUMBER@cloud-tpu.iam.gserviceaccount.com
Create a Cloud Storage bucket using the following command:
gsutil mb -p ${PROJECT_ID} -c standard -l us-central1 -b on gs://bucket-name
This Cloud Storage bucket stores the data you use to train your model and the training results.
In order for the Cloud TPU to read and write to the storage bucket, the Service Account for your project needs read/write or Admin permissions on it. See the section on storage buckets for how to view and set those permissions.
Launch a Compute Engine VM and Cloud TPU using the
gcloud compute tpus execution-groups
command.$ gcloud compute tpus execution-groups create \ --name=bert-tutorial \ --zone=us-central1-b \ --tf-version=1.15.5 \ --machine-type=n1-standard-8 \ --accelerator-type=v3-8
Command flag descriptions
name
- The name of the Cloud TPU to create.
zone
- The zone where you plan to create your Cloud TPU.
tf-version
- The version of Tensorflow
gcloud
installs on the VM. machine-type
- The machine type of the Compute Engine VM to create.
accelerator-type
- The type of the Cloud TPU to create.
For more information on the
gcloud
command, see the gcloud Reference.The configuration you specified appears. Enter y to approve or n to cancel.
When the
gcloud
command has finished executing, verify that your shell prompt has changed fromusername@project
tousername@vm-name
. This change shows that you are now logged into your Compute Engine VM.gcloud compute ssh bert-tutorial --zone=us-central1-b
As you continue these instructions, run each command that
begins with (vm)$
in your VM session window.
Define some environment variables
(vm)$ export STORAGE_BUCKET=gs://bucket-name
(vm)$ export TPU_NAME=bert-tutorial (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
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/7f702beb69199612a039004f42c9982e.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 ${TASK_NAME}
Train the model
From your Compute Engine VM, run the following command.
python3 ./bert/run_classifier.py \ --task_name=${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}
Command flag descriptions
task_name
- The task name. In this tutorial we are using the Microsoft Research Paraphrase Corpus (MSRPC) task.
do_train
- Perform model training.
do_eval
- Perform model evaluation.
data_dir
- The Cloud Storage path where training data are stored.
vocab_file
- The BERT vocabulary file.
bert_config_file
- The BERT configuration file.
init_checkpoint
- The path to the json file containing the initial checkpoint of the pre-trained BERT model.
max_seq_length
- The maximum text sequence length. BERT limits the maximum length of a tokenized text sequence to 512. You can set any sequence length equal to or below this value.
train_batch_size
- The training batch size.
learning_rate
- The learning rate.
num_train_epochs
- The number of epochs to train the model.
output_dir
- The training script output directory.
use_tpu
- Set to
true
to train on a Cloud TPU. tpu_name
- The name of the Cloud TPU to use for training.
Verify your results
The training should take less than 5 minutes. When the training completes, you should see results similar to the following:
I1109 21:55:34.984220 139985090225920 run_classifier.py:923] ***** Eval results ***** INFO:tensorflow: eval_accuracy = 0.8455882 I1109 21:55:34.984345 139985090225920 run_classifier.py:925] eval_accuracy = 0.8455882 INFO:tensorflow: eval_loss = 0.77791333 I1109 21:55:34.984572 139985090225920 run_classifier.py:925] eval_loss = 0.77791333 INFO:tensorflow: global_step = 343 I1109 21:55:34.984693 139985090225920 run_classifier.py:925] global_step = 343 INFO:tensorflow: loss = 0.88203496 I1109 21:55:34.984774 139985090225920 run_classifier.py:925] loss = 0.88203496
Clean up
To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.
Disconnect from the Compute Engine VM:
(vm)$ exit
Your prompt should now be
user@projectname
, showing you are in the Cloud Shell.In your Cloud Shell, use the
gcloud compute tpus execution-groups
command shown below to delete your Compute Engine VM and the Cloud TPU.$ gcloud compute tpus execution-groups delete bert-tutorial \ --zone=us-central1-b
Verify the resources have been deleted by running
gcloud compute tpus execution-groups list
. The deletion might take several minutes. A response like the one below indicates your instances have been successfully deleted.$ gcloud compute tpus execution-groups list \ --zone=us-central1-b
NAME STATUS
Delete your Cloud Storage bucket using
gsutil
as shown below. Replace bucket-name with the name of your Cloud Storage bucket.$ gsutil rm -r gs://bucket-name
What's next
The TensorFlow Cloud TPU tutorials generally train the model using a sample dataset. The results of this training are not usable for inference. To use a model for inference, you can train the data on a publicly available dataset or your own data set. TensorFlow models trained on Cloud TPUs generally require datasets to be in TFRecord format.
You can use the dataset conversion tool sample to convert an image classification dataset into TFRecord format. If you are not using an image classification model, you will have to convert your dataset to TFRecord format yourself. For more information, see TFRecord and tf.Example.
Hyperparameter tuning
To improve the model's performance with your dataset, you can tune the model's hyperparameters. You can find information about hyperparameters common to all TPU supported models on GitHub. Information about model-specific hyperparameters can be found in the source code for each model. For more information on hyperparameter tuning, see Overview of hyperparameter tuning, Using the Hyperparameter tuning service, and Tune hyperparameters.
Inference
Once you have trained your model you can use it for inference (also called prediction). AI Platform is a cloud-based solution for developing, training, and deploying machine learning models. Once a model is deployed, you can use the AI Platform Prediction service.
- Learn more about
gcloud
, including how to install it on a local machine. - Experiment with more TPU samples.
- Explore the TPU tools in TensorBoard.