Run a calculation on a Cloud TPU VM by using TensorFlow

Run a calculation on a Cloud TPU VM by using TensorFlow

This quickstart shows you how to create a Cloud TPU, install TensorFlow and run a simple calculation on a Cloud TPU. 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

Important: You can use this quickstart with either the TPU VM or the TPU Node configuration. The two VM architectures are described in System Architecture. The gcloud commands you use depend on the TPU configuration you are using. In this quickstart, each gcloud command is shown in a tabbed section. Choose the tab for the TPU configuration you want to use and the web page shows the appropriate gcloud command. Unless you know you need to use TPU Nodes, we recommend using TPU VMs.

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

Create a Cloud TPU VM or Node with gcloud

Launch a Compute Engine VM and Cloud TPU using the gcloud command. The command you use depends on whether you are using a TPU VM or a TPU node. For more information on the two VM architecture, see System Architecture. For more information on the gcloud command, see the gcloud reference.

TPU VM

Note: When launching the TPU VM, you can either use the default TPU software version shown in the command or you can refer to Cloud TPU software versions to specify another supported TPU software version.

$ gcloud alpha compute tpus tpu-vm create tpu-name \
--zone=europe-west4-a \
--accelerator-type=v3-8 \
--version=tpu-vm-tf-2.8.0

Command flag descriptions

zone

The zone where you plan to create your Cloud TPU.

accelerator-type

The type of the Cloud TPU to create.

version

The Cloud TPU software version. This defaults to the latest supported TPU software version.

TPU Node

$ gcloud compute tpus execution-groups create \
--name=tpu-name \
--zone=europe-west4-a \
--disk-size=300 \
--machine-type=n1-standard-16 \
--tf-version=2.8.0 \
--accelerator-type=v3-8

Command flag descriptions

project

Your GCP project ID

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 command.

machine-type

The machine type of the Compute Engine VM to create.

tf-version

The version of TensorFlow gcloud installs on the VM.

accelerator-type

The type of the Cloud TPU to create.

Note: If you have more than one project, you must specify the project ID with the --project flag.

Connect to your Cloud TPU VM

When using TPU VMs, you must explicitly SSH into your TPU VM. When using TPU Nodes, you should be automatically SSHed into your Compute EngineVM. If you are not automatically connected, use the following command.

TPU VM

$ gcloud alpha compute tpus tpu-vm ssh tpu-name \
  --zone europe-west4-a

TPU Node

$ gcloud compute ssh tpu-name \
    --zone=europe-west4-a

Run a simple example using tensorflow

TPU VM

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

Note: This example shows how to run code on a single TPU (for example v2-8 or v3-8). To run code on a larger TPU slice (for example v2-32+ or v3-32+), see Training on TPU pods.

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(tpu='local')
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)

TPU Node

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

Note: For single TPUs, Slices, and Pods, pass your TPU name to TPUClusterResolver().

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

tpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='your-tpu-name')  # TPU detection
print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])

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

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

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 simple computation on a each core of a TPU. The output displays the output from each TPU core.

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.

   TPU VM

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

   TPU Node

   $ gcloud compute tpus execution-groups delete tpu-name \
   --zone=europe-west4-a
   

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

   TPU VM

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

   TPU Node

   $ gcloud compute tpus execution-groups list --zone=europe-west4-a
   

What's next

For more information about Cloud TPU, see:

• Cloud TPU User's Guide
• Cloud TPU System Architecture