The model in this tutorial is based on Deep Residual Learning for Image Recognition, which first introduces the residual network (ResNet) architecture. The tutorial uses the 50-layer variant, ResNet-50, and demonstrates training the model using TPUEstimator. The ResNet-50 model is pre-installed on your Compute Engine VM.
Objectives
- Create a Cloud Storage bucket to hold your dataset and model output.
- Prepare a test version of the ImageNet dataset, referred to as the fake_imagenet dataset.
- 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
Before starting this tutorial, check that your Google Cloud project is correctly set up.
-
Sign in to your Google Account.
If you don't already have one, sign up for a new account.
-
In the Google Cloud Console, on the project selector page, select or create a Google Cloud project.
-
Make sure that billing is enabled for your Cloud project. Learn how to confirm that billing is enabled for your project.
This walkthrough uses billable components of Google Cloud. Check the Cloud TPU pricing page to estimate your costs. Be sure to clean up resources you create when you've finished with them to avoid unnecessary charges.
Set up your resources
This section provides information on setting up Cloud Storage, VM, and Cloud TPU resources for tutorials.
Open a Cloud Shell window.
Create a variable for your project's ID.
export PROJECT_ID=project-id
Configure
gcloud
command-line tool 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 ${PROJECT_ID} -c standard -l europe-west4 -b on gs://bucket-name
This Cloud Storage bucket stores the data you use to train your model and the training results. The
gcloud compute tpus execution-groups
tool used in this tutorial sets up default permissions for the Cloud TPU Service Account. If you want finer-grain permissions, review the access level permissions.The bucket location must be in the same region as your virtual machine (VM) and your TPU node. VMs and TPU nodes are located in specific zones, which are subdivisions within a region.
Launch a Compute Engine VM and Cloud TPU using the
gcloud
command.$ gcloud compute tpus execution-groups create \ --vm-only \ --name=resnet-tutorial \ --zone=europe-west4-a \ --disk-size=300 \ --machine-type=n1-standard-8 \ --tf-version=1.15.5
Command flag descriptions
vm-only
- Create a VM only. By default the
gcloud compute tpus execution-groups
command creates a VM and a Cloud TPU. name
- The name of the Cloud TPU to create.
zone
- The zone where you plan to create your Cloud TPU.
disk-size
- The size of the hard disk in GB of the VM created by the
gcloud compute tpus execution-groups
command. machine-type
- The machine type of the Compute Engine VM to create.
tf-version
- The version of Tensorflow
ctpu
installs on the VM.
For more information on the
gcloud
command, see the gcloud Reference.When prompted, press y to create your Cloud TPU resources.
When the
gcloud compute tpus execution-groups
command has finished executing, verify that your shell prompt has changed fromusername@projectname
tousername@vm-name
. This change shows that you are now logged into your Compute Engine VM.gcloud compute ssh resnet-tutorial --zone=europe-west4-a
From this point on, a prefix of (vm)$
means you should run the command on the
Compute Engine VM instance.
Configure storage, model, and data paths
Set up the following environment variables, replacing bucket-name with the name of your Cloud Storage bucket:
(vm)$ export STORAGE_BUCKET=gs://bucket-name (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/resnet (vm)$ export DATA_DIR=gs://cloud-tpu-test-datasets/fake_imagenet (vm)$ export PYTHONPATH="${PYTHONPATH}:/usr/share/tpu/models"
The training application expects your training data to be accessible in Cloud Storage. The training application uses your Cloud Storage bucket to store checkpoints during training.
Train and evaluate the ResNet model with fake_imagenet
ImageNet is an image database. The images in the database are organized into a hierarchy, each node of the hierarchy contains hundreds and thousands of images.
This tutorial uses a demonstration version of the full ImageNet dataset, referred to as fake_imagenet. This demonstration version allows you to test the tutorial, while reducing the storage and time requirements typically associated with running a model against the full ImageNet database.
The fake_imagenet dataset is at this location on Cloud Storage:
gs://cloud-tpu-test-datasets/fake_imagenet
The fake_imagenet dataset is only useful for understanding how to use a Cloud TPU and validating end-to-end performance. The accuracy numbers and saved model will not be meaningful.
For information on how to download and process the full ImageNet dataset, see Downloading, preprocessing, and uploading the ImageNet dataset.
Launch a Cloud TPU resource using the ctpu utility.
(vm)$ gcloud compute tpus execution-groups create \ --tpu-only \ --accelerator-type=v3-8 \ --name=resnet-tutorial \ --zone=europe-west4-a \ --tf-version=1.15.5
Command flag descriptions
tpu-only
- Creates the Cloud TPU without creating a VM. By default the
gcloud compute tpus execution-groups
command creates a VM and a Cloud TPU. accelerator-type
- The type of the Cloud TPU to create.
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
ctpu
installs on the VM.
Set the Cloud TPU name variable.
(vm)$ export TPU_NAME=resnet-tutorial (vm)$ export ACCELERATOR_TYPE=v3-8
Navigate to the model directory:
(vm)$ cd /usr/share/tpu/models/official/resnet/
Run the training script.
For a single Cloud TPU device, the script trains the ResNet-50 model for 90 epochs and evaluates the results after each training step. The number of training steps is set with the
train_steps
flag. Using the script command line below, the model should train in about 15 minutes.Since the training and evaluation is done on the fake_imagenet dataset, the training and evaluation results do not reflect the results that would be generated if training and evaluation was performed on a real dataset.
If you run this script on a real dataset, use the
train_steps
flag to specify the number of training steps. See the.yaml
files in the/usr/share/tpu/models/official/resnet/configs/cloud
directory to get an idea about how many training steps to use.(vm)$ python3 resnet_main.py \ --tpu=${TPU_NAME} \ --data_dir=${DATA_DIR} \ --model_dir=${MODEL_DIR} \ --train_steps=500 \ --config_file=configs/cloud/${ACCELERATOR_TYPE}.yaml
Command flag descriptions
tpu
- Specifies the name of the Cloud TPU. Note that
ctpu
passes this name to the Compute Engine VM as an environment variable (TPU_NAME
). data_dir
- The Cloud Storage path of training input. It is set to the fake_imagenet dataset in this example.
model_dir
- The Cloud Storage bucket where checkpoints and summaries are stored during training. You can use an existing folder to load previously generated checkpoints created on a TPU of the same size and TensorFlow version.
config_file
- Specifies the YAML configuration file to use during training. The
name of this file corresponds to the type of TPU used. For example,
v2-8.yaml
.
The training script output should look like this:
Eval results at step 500: { 'top_1_accuracy': 0.0010579427, 'top_5_accuracy': 0.005391439, 'global_step': 500, 'loss': 8.253192 }
At this point, you can either conclude this tutorial and clean up your GCP resources, or you can further explore running the model on Cloud TPU Pods.
Scaling your model with Cloud TPU Pods
You can get results faster by scaling your model with Cloud TPU Pods. The fully supported ResNet-50 model can work with the following Pod slices:
- v2-32
- v2-128
- v2-256
- v2-512
- v3-32
- v3-128
- v3-256
- v3-512
- v3-1024
- v3-2048
When working with Cloud TPU Pods, you first train the model using a Pod, then use a single Cloud TPU device to evaluate the model.
Training with Cloud TPU Pods
Delete the Cloud TPU resource you created for training the model on a single device.
(vm)$ gcloud compute tpus execution-groups delete resnet-tutorial \ --zone=europe-west4-a \ --tpu-only
Run the
gcloud compute tpus execution-groups
command, using theaccelerator-type
parameter to specify the Pod slice you want to use. For example, the following command uses a v3-32 Pod slice.(vm)$ gcloud compute tpus execution-groups create --name=resnet-tutorial \ --accelerator-type=v2-32 \ --zone=europe-west4-a \ --tf-version=1.15.5 \ --tpu-only
Command flag descriptions
name
- The name of the Cloud TPU to create.
accelerator-type
- The type 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. tpu-only
- Create a Cloud TPU only. By default the
gcloud
command creates a VM and a Cloud TPU.
Update the
TPU_NAME
andACCELERATOR_TYPE
environment variables to specify a TPU pod name an accelerator type.(vm)$ export TPU_NAME=resnet-tutorial (vm)$ export ACCELERATOR_TYPE=v2-32
Update the
MODEL_DIR
directory to store the training data.(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/resnet-tutorial
Train the model, updating the
config_file
parameter to use the configuration file that corresponds with the Pod slice you want to use. For example, the training script uses thev2-32.yaml
configuration file.The script trains the model on the fake_imagnet dataset to 35 epochs. This takes approximately 90 minutes to run on a v3-128 Cloud TPU.
(vm)$ python3 resnet_main.py \ --tpu=${TPU_NAME} \ --data_dir=${DATA_DIR} \ --model_dir=${MODEL_DIR} \ --train_steps=500 \ --config_file=configs/cloud/${ACCELERATOR_TYPE}.yaml
Command flag descriptions
tpu
- The name of your Cloud TPU.
data_dir
- The Cloud Storage path of training input. It is set to the fake_imagenet dataset in this example.
model_dir
- The Cloud Storage bucket where checkpoints and summaries are stored during training. You can use an existing folder to load previously generated checkpoints created on a TPU of the same size and TensorFlow version.
train_steps
- The number of steps to use for training.
config_file
- Specifies the YAML configuration file to use during training. The
name of this file corresponds to the type of TPU used. For example,
v2-8.yaml
.
Evaluating the model
In this step, you use Cloud TPU to evaluate the above trained model against the fake_imagenet validation data.
Delete the Cloud TPU resource you created to train the model.
(vm)$ gcloud compute tpus execution-groups delete resnet-tutorial \ --zone=europe-west4-a \ --tpu-only
Create a v3-8 Cloud TPU pod resource.
(vm)$ gcloud compute tpus execution-groups create \ --tpu-only \ --name=resnet-tutorial \ --zone=europe-west4-a \ --tf-version=1.15.5 \ --accelerator-type=v2-8
Command flag descriptions
Update the
TPU_NAME
environment variable.(vm)$ export TPU_NAME=resnet-eval
Run the model evaluation. This time, add the
mode
flag and set it toeval
.(vm)$ python3 resnet_main.py \ --tpu=${TPU_NAME} \ --data_dir=${DATA_DIR} \ --model_dir=${MODEL_DIR} \ --mode=eval \ --config_file=configs/cloud/${ACCELERATOR_TYPE}.yaml
Command flag descriptions
tpu
- The Cloud TPU to use for training.
data_dir
- The Cloud Storage path where the training data is stored. It is set to the fake_imagenet dataset in this example.
model_dir
- The Cloud Storage path where checkpoints and summaries are stored during model training. You can reuse an existing folder to load previously generated checkpoints and to store additional checkpoints as long as the previous checkpoints were created using a Cloud TPU of the same size and TensorFlow version.
mode
- One of
train
,eval
,train_and_eval
, orpredict
. config_file
- Specifies the YAML configuration file to use during training. The
name of this file corresponds to the type of TPU used. For example,
v2-8.yaml
.
This generates output similar to the following:
Eval results: { 'loss': 8.255788, 'top_1_accuracy': 0.0009969076, 'global_step': 0, 'top_5_accuracy': 0.005126953 }. Elapsed seconds: 76
Since the training and evaluation was done on the fake_imagenet dataset, the output results do not reflect actual output that would appear if the training and evaluation was performed on a real dataset.
Cleaning 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 instance, if you have not already done so:
(vm)$ exit
Your prompt should now be
username@project
, showing you are in the Cloud Shell.In your Cloud Shell, use the following command to delete your Compute Engine VM and Cloud TPU:
$ gcloud compute tpus execution-groups delete resnet-tutorial \ --zone=europe-west4-a
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=europe-west4-a
You should see an empty list of TPUs like the following:
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
In this tutorial you have trained the RESNET model using a sample dataset. The results of this training are (in most cases) 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. Models trained on Cloud TPUs 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
ctpu
, including how to install it on a local machine. - Explore the TPU tools in TensorBoard.
- See how to train ResNet with Cloud TPU and GKE.
- Walk through the tutorial for the RetinaNet object detection model.
- Run the TensorFlow SqueezeNet model on Cloud TPU, using the above instructions as your starting point. The model architectures for SqueezeNet and ResNet-50 are similar. You can use the same data and the same command-line flags to train the model.