Training ResNet on Cloud TPU

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.

  1. Sign in to your Google Account.

    If you don't already have one, sign up for a new account.

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

    Go to the project selector page

  3. Make sure that billing is enabled for your Google Cloud project. Learn how to confirm billing is enabled for your project.

  4. 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.

  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:

    gsutil mb -p ${PROJECT_NAME} -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 ctpu up 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.

  5. Launch the Compute Engine resources required for this using the ctpu up command.

    ctpu up --zone=europe-west4-a \
    --vm-only \
    --disk-size-gb=300 \
    --machine-type=n1-standard-8 \
    --tf-version=1.15.2 \
    --name=resnet-tutorial

    For more information on the CTPU utility, see CTPU Reference.

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

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. 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.

  1. Launch a Cloud TPU resource using the ctpu utility and set some environment variables used later on.

     (vm)$ ctpu up --tpu-only \
     --tf-version=1.15.2 \
     --name=resnet-tutorial
    
     (vm)$ export TPU_NAME=resnet-tutorial
     (vm)$ export ACCELERATOR_TYPE=v3-8
    
  2. Navigate to the model directory:

    (vm)$ cd /usr/share/tpu/models/official/resnet/
    
  3. Run the training script.

    For a single Cloud TPU device, the script trains the ResNet-50 model for 90 epochs and evaluates every fixed number of training steps. Using the specified flags, the model should train in about 10 hours.

    Since the training and evaluation is 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.

    Because you are training this model with fake data, you may want to reduce the time it takes the training script to complete. You can use the --train_steps argument to resnet_main.py to specify the number of training steps to use. For example, you can set train_steps to 1000 and the model will train for approximately 30 minutes.

     (vm)$ python3 resnet_main.py \
        --tpu=${TPU_NAME} \
        --data_dir=${DATA_DIR} \
        --model_dir=${MODEL_DIR} \
        --config_file=configs/cloud/${ACCELERATOR_TYPE}.yaml
    
    Parameter Description
    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 Specifies the Cloud Storage path for training input. It is set to the fake_imagenet dataset in this example.
    model_dir Specifies the directory where checkpoints and summaries are stored during model training. If the folder is missing, the program creates one. When using a Cloud TPU, the model_dir must be a Cloud Storage path (`gs://...`). You can reuse an existing folder to load current checkpoint data and to store additional checkpoints as long as the previous checkpoints were created using 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.

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

  1. Delete the Cloud TPU resource you created for training the model on a single device.

     (vm)$ ctpu delete --tpu-only --name=resnet-tutorial
    
  2. Run the ctpu up command, using the tpu-size parameter to specify the Pod slice you want to use. For example, the following command uses a v2-32 Pod slice.

      (vm)$ ctpu up --tpu-only --tpu-size=v2-32 --tf-version=1.15.2 --name=resnet-pod
    
  3. Update the TPU_NAME and ACCELERATOR_TYPE environment variables to specify a TPU pod name an accelerator type.

      (vm)$ export TPU_NAME=resnet-pod
      (vm)$ export ACCELERATOR_TYPE=v2-32
    
  4. Update the MODEL_DIR directory to store the training data.

      (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/resnet-pod
    
  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=${MODEL_DIR} \
        --config_file=configs/cloud/${ACCELERATOR_TYPE}.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)$ ctpu delete --tpu-only --name=resnet-pod
    
  2. Start a v2-8 Cloud TPU.

     (vm)$ ctpu up --tpu-only \
       --tf-version=1.15.2 \
       --name=resnet-eval
    
  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
    

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 Platform account for the resources used in this tutorial:

  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, 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=europe-west4-a
    
  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 more allocated instances:

    2018/04/28 16:16:23 WARNING: Setting zone to "europe-west4-a"
    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
    

What's next