Run a calculation on a Cloud TPU VM using TensorFlow

This quickstart shows you how to create a Cloud TPU and run a calculation on a Cloud TPU using TensorFlow. For a more in depth tutorial showing you how to train a model on a Cloud TPU see one of the Cloud TPU Tutorials.

Before you begin

Before you follow this quickstart, you must create a Google Cloud account, install the Google Cloud CLI CLI, and configure the gcloud command. For more information, see Set up an account and a Cloud TPU project.

Create a Cloud TPU VM with gcloud

Create a Cloud TPU using the gcloud command.

  $ gcloud compute tpus tpu-vm create tpu-name
    --zone=europe-west4-a
    --accelerator-type=v3-8
    --version=tpu-vm-tf-2.17.0-pjrt
    --project=your-gcp-project-name

Command flag descriptions

tpu-name
The name of the Cloud TPU to create.
zone
The zone where you plan to create your Cloud TPU.
accelerator-type
The accelerator type specifies the version and size of the Cloud TPU you want to create. For more information about supported accelerator types for each TPU version, see TPU versions.
version
The TPU runtime version. The version used in this quickstart has TensorFlow preinstalled.
project
The name of the Google Cloud CLI project where you are creating your Cloud TPU.

For more information about the gcloud command, see the gcloud reference.

Connect to your Cloud TPU VM

Connect to your TPU VM using SSH:

  $ gcloud compute tpus tpu-vm ssh tpu-name 

      --zone europe-west4-a 

      --project=your-gcp-project-name

Run an example using TensorFlow

Once you are connected to the TPU VM, set the following environment variable.

  (vm)$ export TPU_NAME=local

When creating your TPU, if you set the --version parameter to a version ending with -pjrt, set the following environment variables to enable the PJRT runtime: 

  (vm)$ export NEXT_PLUGGABLE_DEVICE_USE_C_API=true
  (vm)$ export TF_PLUGGABLE_DEVICE_LIBRARY_PATH=/lib/libtpu.so

Create a file named tpu-test.pyin the current directory and copy and paste the following script into it.

import tensorflow as tf
print("Tensorflow version " + tf.__version__)

@tf.function
def add_fn(x,y):
  z = x + y
  return z

cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(cluster_resolver)
strategy = tf.distribute.TPUStrategy(cluster_resolver)

x = tf.constant(1.)
y = tf.constant(1.)
z = strategy.run(add_fn, args=(x,y))
print(z)

Run this script with the following command:

(vm)$ python3 tpu-test.py

This script performs a computation on a each TensorCore of a TPU. The output will look similar to the following:

PerReplica:{
  0: tf.Tensor(2.0, shape=(), dtype=float32),
  1: tf.Tensor(2.0, shape=(), dtype=float32),
  2: tf.Tensor(2.0, shape=(), dtype=float32),
  3: tf.Tensor(2.0, shape=(), dtype=float32),
  4: tf.Tensor(2.0, shape=(), dtype=float32),
  5: tf.Tensor(2.0, shape=(), dtype=float32),
  6: tf.Tensor(2.0, shape=(), dtype=float32),
  7: tf.Tensor(2.0, shape=(), dtype=float32)
}

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

  1. Disconnect from the Compute Engine instance, if you have not already done so:

    (vm)$ exit

    Your prompt should now be username@projectname, showing you are in the Cloud Shell.

  2. Delete your Cloud TPU.

      $ gcloud compute tpus tpu-vm delete tpu-name 
    
    --zone=europe-west4-a

  3. Verify the resources have been deleted by running gcloud compute tpus tpu-vm list. The deletion might take several minutes.

      $ gcloud compute tpus tpu-vm list --zone=europe-west4-a

What's next

For more information about Cloud TPU, see: