Running MNIST on Cloud TPU (TF 2.x)

This tutorial contains a high-level description of the MNIST model, instructions on downloading the MNIST TensorFlow TPU code sample, and a guide to running the code on Cloud TPU.


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

The MNIST dataset contains a large number of images of hand-written digits in the range 0 to 9, as well as the labels identifying the digit in each image.

This tutorial trains a machine learning model to classify images based on the MNIST dataset. After training, the model classifies incoming images into 10 categories (0 to 9) based on what it's learned about handwritten images from the MNIST dataset. You can then send the model an image that it hasn't seen before, and the model identifies the digit in the image based on what the model has learned during training.

The MNIST dataset has been split into three parts:

  • 60,000 examples of training data
  • 10,000 examples of test data
  • 5,000 examples of validation data

You can find more information about the dataset at the MNIST database site.

The model has a mixture of seven layers:

  • 2 x convolution
  • 2 x max pooling
  • 2 x dense (fully connected)
  • 1 x dropout

Loss is computed via categorical cross entropy.

This version of the MNIST model uses the Keras API, a recommended way to build and run a machine learning model on a Cloud TPU.

Keras simplifies the model development process by hiding most of the low-level implementation, which also makes it easy to switch between TPU and other test platforms such as GPUs or CPUs.


  • Create a Cloud Storage bucket to hold your dataset and model output.
  • Run the training job.
  • Verify the output results.


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

This section provides information on setting up Cloud Storage bucket and a Compute Engine VM.

  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 us-central1 -b on gs://bucket-name

    This Cloud Storage bucket stores the data you use to train your model and the training results.

  5. Launch a Compute Engine VM and Cloud TPU using the ctpu up command.

    $ ctpu up --zone=us-central1-b  --tf-version=2.2 --name=mnist-tutorial
  6. The configuration you specified appears. Enter y to approve or n to cancel.

  7. When the ctpu up 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.

    gcloud compute ssh mnist-tutorial --zone=us-central1-b
    (vm)$ export TPU_NAME=mnist-tutorial

    As you continue these instructions, run each command that begins with (vm)$ in your VM session window.

  8. Install an extra package.

    The MNIST training application requires an extra package. Install it now:

    (vm)$ sudo pip3 install tensorflow-model-optimization>=0.1.3

Run the MNIST TPU model

The source code for the MNIST TPU model is available on GitHub.

Set up environment variables

Export the following variables. Replace bucket-name with your bucket name:

(vm)$ export STORAGE_BUCKET=gs://bucket-name
(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/mnist
(vm)$ export DATA_DIR=${STORAGE_BUCKET}/data
(vm)$ export PYTHONPATH="${PYTHONPATH}:/usr/share/models"

Train the model on Cloud TPU

  1. Change to directory that stores the model:

    (vm)$ cd /usr/share/models/official/vision/image_classification
  2. Run the MNIST training script:

    (vm)$ python3 \
      --tpu=${TPU_NAME} \
      --model_dir=${MODEL_DIR} \
      --data_dir=${DATA_DIR} \
      --train_epochs=10 \
      --distribution_strategy=tpu \
Parameter Description
tpu The name of the Cloud TPU. If not specified when setting up the Compute Engine VM and Cloud TPU, defaults to your username.
model_dir This is the directory that contains the model files. This tutorial uses a folder within the Cloud Storage bucket. You do not have to create this folder beforehand. The script creates the folder if it does not already exist.
data_dir This is the directory that contains files used for training.
download When specified (set to true), the script downloads and preprocesses the MNIST dataset, if it hasn't been downloaded already.

The training script runs in under 5 minutes on a v3-8 Cloud TPU and displays output similar to:

I1203 03:43:15.936553 140096948798912]
Run stats: {'loss': 0.11427700750786683, 'training_accuracy_top_1': 0.9657697677612305,
'accuracy_top_1': 0.9730902910232544, 'eval_loss': 0.08600160645114051}

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@projectname, showing you are in the Cloud Shell.

  2. In your Cloud Shell, run ctpu delete with the --name and --zone flags you used when you set up the Compute Engine VM and Cloud TPU. This deletes both your VM and your Cloud TPU.

    $ ctpu delete --name=mnist-tutorial --zone=us-central1-b
  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:

    $ ctpu status --name=mnist-tutorial --zone=us-central1-b
    2018/04/28 16:16:23 WARNING: Setting zone to "us-central1-b"
    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