Running Inception on Cloud TPU

This tutorial shows you how to train the Inception model on Cloud TPU.

Disclaimer

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.

Model description

Inception v3 is a widely-used image recognition model that can attain significant accuracy. The model is the culmination of many ideas developed by multiple researchers over the years. It is based on the original paper: "Rethinking the Inception Architecture for Computer Vision" by Szegedy, et. al.

The model has a mixture of symmetric and asymmetric building blocks, including:

• convolutions
• average pooling
• max pooling
• concats
• dropouts
• fully connected layers

Loss is computed via Softmax.

The following picture shows the model at a high level:

You can find more information about the model at GitHub.

The model is built using the high-level Estimator API.

This API greatly simplifies model creation by encapsulating most low-level functions, allowing users to focus on model development, not the inner workings of the underlying hardware that runs things.

Before you begin

Before starting this tutorial, check that your Google Cloud Platform 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. Select or create a GCP project.

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

   Go to the Manage resources page

3. Make sure that billing is enabled for your project.

   Learn how to enable billing

This walkthrough uses billable components of Google Cloud Platform. 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 unecessary charges.

Set up your resources

This section provides information on setting up Cloud Storage storage, VM, and Cloud TPU resources for tutorials.

Important: Be sure to set up all resources in the same region/zone.

Create a Cloud Storage bucket

You need a Cloud Storage bucket to store 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 you create must reside in the same region as your virtual machine (VM) and your Cloud TPU device or Cloud TPU slice (multiple TPU devices) do.

1. Go to the Cloud Storage page on the GCP Console.

   Go to the Cloud Storage page

2. Create a new bucket, specifying the following options:

   • A unique name of your choosing.
   • Default storage class: Regional
   • Location: If you want to use a Cloud TPU device, accept the default presented. If you want to use a Cloud TPU Pod slice, you must specify a region where Cloud TPU Pods are available.

Use the ctpu tool

This section demonstrates using the Cloud TPU provisioning tool (ctpu) for creating and managing Cloud TPU project resources. The resources are comprised of a virtual machine (VM) and a Cloud TPU resource that have the same name. These resources must reside in the same region/zone as the bucket you just created.

Run ctpu up to create resources

1. Open a Cloud Shell window.

   Open Cloud Shell

2. Run ctpu up and specify options for either a Cloud TPU device or Pod slice:

   You can use flags to change the following options:

   • --name - name of your Cloud TPU resource and your VM.
   • --zone - region and zone of the physical assets. The zone must be the same for the VM and Cloud TPU. The bucket must be in the same region.
   • --project name - name of an existing project.
   • --tpu_size - version and size of the Cloud TPU. The default is one device with 8 cores.
   • --disk-size-gb - disk size. Use only if your dataset requires more than the default 250GB.
   • --machine-type - virtual machine (VM) memory per CPU.
   • --preemptible - interruptable, but lower cost Cloud TPU.

3. Set up either a Cloud TPU device or a Pod slice:

TPU Device

Set up a Cloud TPU device:

$ ctpu up

Note: If you have more than one project, you must specify the project name.

The following configuration message appears:

ctpu will use the following configuration:

Name: [your TPU's name]
Zone: [your project's zone]
GCP Project: [your project's name]
TensorFlow Version: 1.12
VM:
 Machine Type: [your machine type]
 Disk Size: [your disk size]
 Preemptible: [true or false]
Cloud TPU:
 Size: [your TPU size]
 Preemptible: [true or false]

OK to create your Cloud TPU resources with the above configuration? [Yn]:

Press y to create your Cloud TPU resources.

TPU Pod

Set up a Cloud TPU slice with 32 Cloud TPU cores, 8 CPUs, a 500GB disk on the VM and the zone you are working in:

$ ctpu up --zone=us-central1-a --tpu-size=v2-32 --disk-size-gb=500 --machine-type n1-standard-8 --preemptible

Note: If you have more than one project, you must specify the project name.

The following configuration message appears:

ctpu will use the following configuration:

Name: [your TPU's name]
Zone: [your project's zone]
GCP Project: [your project's name]
TensorFlow Version: 1.12
VM:
 Machine Type: [your machine type]
 Disk Size: [your disk size]
 Preemptible: [true or false]
Cloud TPU:
 Size: [your TPU size]
 Preemptible: [true or false]

OK to create your Cloud TPU resources with the above configuration? [Yn]:

Press y to create your Cloud TPU resources.

Note: The first time you run ctpu up on a project it takes about 5 minutes to perform startup tasks such as SSH key propagation and API turnup.

The ctpu up command creates a virtual machine (VM) and Cloud TPU services.

From this point on, a prefix of (vm)$ means you should run the command on the Compute Engine VM instance.

Verify your Compute Engine VM

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

Get the data

Set up the following environment variable, replacing YOUR-BUCKET-NAME with the name of your Cloud Storage bucket:

(vm)$ export STORAGE_BUCKET=gs://YOUR-BUCKET-NAME

The training application expects your training data to be accessible in Cloud Storage. The training application also uses your Cloud Storage bucket to store checkpoints during training.

There are two datasets you can use, a randomly-generated fake dataset or the full ImageNet dataset. A DATA_DIR environment variable described below is used to specify which dataset to train on.

Note that the fake 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.

The fake dataset is at this location on Cloud Storage:

gs://cloud-tpu-test-datasets/fake_imagenet

Note: For this tutorial, make sure you don't set the STORAGE_BUCKET environment variable to the path of the fake 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.

(Optional) Set up TensorBoard

TensorBoard offers a suite of tools designed to present TensorFlow data visually. When used for monitoring, TensorBoard can help identify bottlenecks in processing and suggest ways to improve performance.

If you don't need to monitor the model's output at this time, you can skip the TensorBoard setup steps.

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

Run the model

You are now ready to train and evaluate the Inception v3 model using ImageNet data.

The Inception v3 model is pre-installed on your Compute Engine VM, in the /usr/share/tpu/models/experimental/inception/ directory.

In the following steps, a prefix of (vm)$ means you should run the command on your Compute Engine VM:

1. Set up a DATA_DIR environment variable containing one of the following values:

   • If you are using the fake dataset:

     (vm)$ export DATA_DIR=gs://cloud-tpu-test-datasets/fake_imagenet
     

   • If you have uploaded a set of training data to your Cloud Storage bucket:

     (vm)$ export DATA_DIR=${STORAGE_BUCKET}/data
     

2. Run the Inception v3 model:

   (vm)$ python /usr/share/tpu/models/experimental/inception/inception_v3.py \
       --tpu=$TPU_NAME \
       --learning_rate=0.165 \
       --train_steps=250000 \
       --iterations=500 \
       --use_tpu=True \
       --use_data=real \
       --mode=train_and_eval \
       --train_steps_per_eval=2000 \
       --data_dir=${DATA_DIR} \
       --model_dir=${STORAGE_BUCKET}/inception

   • --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).
   • --use_data specifies which type of data the program must use during training, either fake or real. The default value is fake.
   • --data_dir specifies the Cloud Storage path for training input. The application ignores this parameter when you're using fake data.
   • --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.

What to expect

Inception v3 operates on 299x299 images. The default training batchsize is 1024, which means that each iteration operates on 1024 of those images.

You can use the --mode flag to select one of three modes of operation: train, eval, and train_and_eval:

• --mode=train or --mode=eval specifies either a training-only or an evaluation-only job.
• --mode=train_and_eval specifies a hybrid job that does both training and evaluation.

Train-only jobs run for the specified number of steps defined in train_steps and can go through the entire training set, if desired.

Train_and_eval jobs cycle though training and evaluation segments. Each training cycle runs for train_steps_per_eval and is followed by an evaluation job (using the weights that have been trained up to that point).

The number of training cycles is defined by the floor function of train_steps divided by train_steps_per_eval.

floor(train_steps / train_steps_per_eval)

Note: The train_steps_per_eval flag must be a multiple of iterations.

By default, Estimator API-based models report loss values every certain number of steps. The reporting format is along the lines of:

step = 15440, loss = 12.6237

Discussion: TPU-specific modifications to the model

The specific modifications required to get Estimator API-based models ready for TPUs are surprisingly minimal. The program imports the following libraries:

from google.third_party.tensorflow.contrib.tpu.python.tpu import tpu_config
from google.third_party.tensorflow.contrib.tpu.python.tpu import tpu_estimator
from google.third_party.tensorflow.contrib.tpu.python.tpu import tpu_optimizer

The CrossShardOptimizer function wraps the optimizer, as in:

if FLAGS.use_tpu:
  optimizer = tpu_optimizer.CrossShardOptimizer(optimizer)

The function that defines the model returns an Estimator specification using:

return tpu_estimator.TPUEstimatorSpec(
    mode=mode, loss=loss, train_op=train_op, eval_metrics=eval_metrics)

The main function defines an Estimator-compatible configuration using:

run_config = tpu_config.RunConfig(
    master=tpu_grpc_url,
    evaluation_master=tpu_grpc_url,
    model_dir=FLAGS.model_dir,
    save_checkpoints_secs=FLAGS.save_checkpoints_secs,
    save_summary_steps=FLAGS.save_summary_steps,
    session_config=tf.ConfigProto(
        allow_soft_placement=True,
        log_device_placement=FLAGS.log_device_placement),
    tpu_config=tpu_config.TPUConfig(
        iterations_per_loop=iterations,
        num_shards=FLAGS.num_shards,
        per_host_input_for_training=per_host_input_for_training))

The program uses this defined configuration and a model definition function to create an Estimator object:

inception_classifier = tpu_estimator.TPUEstimator(
    model_fn=inception_model_fn,
    use_tpu=FLAGS.use_tpu,
    config=run_config,
    params=params,
    train_batch_size=FLAGS.train_batch_size,
    eval_batch_size=eval_batch_size,
    batch_axis=(batch_axis, 0))

Train-only jobs need only to call the train function:

inception_classifier.train(
    input_fn=imagenet_train.input_fn, steps=FLAGS.train_steps)

Evaluation-only jobs get their data from available checkpoints and wait until a new one becomes available:

for checkpoint in get_next_checkpoint():
  eval_results = inception_classifier.evaluate(
      input_fn=imagenet_eval.input_fn,
      steps=eval_steps,
      hooks=eval_hooks,
      checkpoint_path=checkpoint)

When you choose the option train_and_eval, the training and the evaluation jobs run in parallel. During evaluation, trainable variables are loaded from the latest available checkpoint. Training and evaluation cycles repeat as you specify in the flags::

for cycle in range(FLAGS.train_steps // FLAGS.train_steps_per_eval):
  inception_classifier.train(
      input_fn=imagenet_train.input_fn, steps=FLAGS.train_steps_per_eval)

  eval_results = inception_classifier.evaluate(
      input_fn=imagenet_eval.input_fn, steps=eval_steps, hooks=eval_hooks)

Clean up

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

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 [optional: --zone]
   

   The operation may take a few moments. A message like the one below indicates there are no more allocated instances:

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

3. Run ctpu status with the --zone flag you used when you set up the Cloud TPU. This checks that your instance was deleted so you can avoid unnecessary charges for TPU usage.

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

   $ gsutil rm -r gs://YOUR-BUCKET-NAME
   

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

Inception v4

The Inception v4 model is a deep neural network model that uses Inception v3 building blocks to achieve higher accuracy than Inception v3. It is described in the paper "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning" by Szegedy et. al.

The Inception v4 model is pre-installed on your Compute Engine VM, in the /usr/share/tpu/models/experimental/inception/ directory.

In the following steps, a prefix of (vm)$ means you should run the command on your Compute Engine VM:

1. If you have TensorBoard running in your Cloud Shell tab, you need another tab to work in. Open another tab in your Cloud Shell, and use ctpu in the new shell to connect to your Compute Engine VM:

   $ ctpu up

2. Set up a DATA_DIR environment variable containing one of the following values:

   • If you are using the fake dataset:

     (vm)$ export DATA_DIR=gs://cloud-tpu-test-datasets/fake_imagenet
     

   • If you have uploaded a set of training data to your Cloud Storage bucket:

     (vm)$ export DATA_DIR=${STORAGE_BUCKET}/data
     

3. Run the Inception v4 model:

   (vm)$ python /usr/share/tpu/models/experimental/inception/inception_v4.py \
       --tpu=$TPU_NAME \
       --learning_rate=0.36 \
       --train_steps=1000000 \
       --iterations=500 \
       --use_tpu=True \
       --use_data=real \
       --train_batch_size=256 \
       --mode=train_and_eval \
       --train_steps_per_eval=2000 \
       --data_dir=${DATA_DIR} \
       --model_dir=${STORAGE_BUCKET}/inception

   • --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).
   • --use_data specifies which type of data the program must use during training, either fake or real. The default value is fake.
   • --train_batch_size specifies the train batch size to be 256. As the Inception v4 model is larger than Inception v3, it must be run at a smaller batch size per TPU core.
   • --data_dir specifies the Cloud Storage path for training input. The application ignores this parameter when you're using fake data.
   • --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.

What's next

• Go in depth with an advanced view of Inception v3 on Cloud TPU.
• Learn more about ctpu, including how to install it on a local machine.
• Explore the TPU tools in TensorBoard.