Training ResNet on Cloud TPU

Objective: This tutorial shows you how to train the Tensorflow ResNet-50 model using a Cloud TPU device or Cloud TPU Pod slice (multiple TPU devices). You can apply the same pattern to other TPU-optimised image classification models that use TensorFlow and the ImageNet dataset.

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.

Warning: TPUEstimator is only supported by Tensorflow 1.x. If you are writing a model with Tensorflow 2.x, use Keras instead.

Warning: This tutorial uses a third-party dataset. Google provides no representation, warranty, or other guarantees about the validity, or any other aspects of this dataset.

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 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. 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.

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

4. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

5. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a 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.

Important: Set up all resources 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 ID.

   export PROJECT_ID=project-id
   

3. 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. Click Authorize at the bottom of the page to allow gcloud to make GCP API calls with your credentials.

4. 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:

   [e-mail]
   

5. 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.

6. Launch a Compute Engine VM and Cloud TPU using the gcloud command.

   Note: This tutorial uses TensorFlow 1.15.5 which is based on the Debian 10 image and defaults to Python 3.6. Earlier versions of TensorFlow 1.15.x were based on Debian 9 and default to Python 3.5.

   $ 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.

7. When prompted, press y to create your Cloud TPU resources.

   Note: The first time you run gcloud compute tpus execution-groups on a project it takes about 5 minutes to perform startup tasks such as SSH key propagation and API turnup.

   When the gcloud compute tpus execution-groups command has finished executing, verify that your shell prompt has changed from username@projectname 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 do so by running the following command:

   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.

Caution: For this tutorial, make sure you don't set the STORAGE_BUCKET environment variable to the path of the fake_imagenet dataset. You can read from gs://cloud-tpu-test-datasets but you can't write to it. As a result, you can't use it to write out training logs. Make sure the STORAGE_BUCKET environment variable is set to your own Cloud Storage bucket, as shown above.

Note: If you want to monitor the model's output and performance, follow the guide to setting up TensorBoard.

1. 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.

2. Set the Cloud TPU name variable.

    (vm)$ export TPU_NAME=resnet-tutorial
    (vm)$ export ACCELERATOR_TYPE=v3-8
   

3. Navigate to the model directory:

   (vm)$ cd /usr/share/tpu/models/official/resnet/
   

4. 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

Note: If you have already deleted your Compute Engine instance, create a new one following the steps in Set up your resources.

1. 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

2. Run the gcloud compute tpus execution-groups command, using the accelerator-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.

3. Update the TPU_NAME and ACCELERATOR_TYPE environment variables to specify a TPU pod name an accelerator type.

     (vm)$ export TPU_NAME=resnet-tutorial
     (vm)$ export ACCELERATOR_TYPE=v2-32
   

4. Update the MODEL_DIR directory to store the training data.

     (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/resnet-tutorial
   

5. 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 the v2-32.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.

1. 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
   

2. 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

   tpu-only

   Create the Cloud TPU only, does not create a Compute Engine.

   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.

   accelerator-type

   The type of the Cloud TPU to create.

3. Update the TPU_NAME environment variable.

    (vm)$ export TPU_NAME=resnet-eval
   

4. Run the model evaluation. This time, add the mode flag and set it to eval.

    (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, or predict.

   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.

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.

1. 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.

2. 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
   

3. 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
   

4. 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
   

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

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 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.