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.
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
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:
- 55,000 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 softmax.
This version of the MNIST model uses tf.estimator —a high-level TensorFlow API—which is the recommended way to build and run a machine learning model on a Cloud TPU.
The Tensorflow Estimator API 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.
Before you begin
Before starting this tutorial, check that your Google Cloud Platform project is correctly set up.
-
Sign in to your Google Account.
If you don't already have one, sign up for a new account.
-
Select or create a GCP project.
-
Make sure that billing is enabled for your project.
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.
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.
Go to the Cloud Storage page on the GCP Console.
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
Open a Cloud Shell window.
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.
Set up either a Cloud TPU device or a Pod slice:
TPU Device
Set up a Cloud TPU device:
$ ctpu up
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
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.
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
The MNIST dataset is hosted on the MNIST database site. Follow the instructions below to download and convert the data to the required format, and to upload the converted data to Cloud Storage.
Download and convert the MNIST data
The convert_to_records.py script downloads the data and converts it to the TFRecord format expected by the example MNIST model.
Use the following commands to run the script and decompress the files:
(vm)$ python /usr/share/tensorflow/tensorflow/examples/how_tos/reading_data/convert_to_records.py --directory=./data (vm)$ gunzip ./data/*.gz
Upload the data to Cloud Storage
Upload the data to your Cloud Storage bucket so that the TPU server can
access the data. When setting the variable in the commands below, replace
YOUR-BUCKET-NAME
with the name of your Cloud Storage bucket:
(vm)$ export STORAGE_BUCKET=gs://YOUR-BUCKET-NAME (vm)$ gsutil cp -r ./data ${STORAGE_BUCKET}
(Optional) Set up TensorBoard
TensorBoard offers a suite of tools designed to present TensorFlow data visually. When used for monitoring, TensorBoard can help identify bottlenecks in processing and suggest ways to improve performance.
If you don't need to monitor the model's output at this time, you can skip the TensorBoard set up steps.
If you want to monitor the model's output and performance, follow the guide to setting up TensorBoard.
Run the MNIST TPU model
The MNIST TPU model is pre-installed on your Compute Engine VM in the following directory:
/usr/share/models/official/mnist/
The source code for the MNIST TPU model is also available on GitHub. You can run the model on a Cloud TPU. Alternatively, see how to run the model on a local machine.
Running the model on Cloud TPU
In the following steps, a prefix of (vm)$
means you should run the command on
your Compute Engine VM:
Run the MNIST model:
(vm)$ python /usr/share/models/official/mnist/mnist_tpu.py \ --tpu=$TPU_NAME \ --data_dir=${STORAGE_BUCKET}/data \ --model_dir=${STORAGE_BUCKET}/output \ --use_tpu=True \ --iterations=500 \ --train_steps=2000
--tpu
specifies the name of the Cloud TPU. Note thatctpu
passes this name to the Compute Engine VM as an environment variable (TPU_NAME
).--data_dir
specifies the Cloud Storage path for training input.--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, themodel_dir
must be a Cloud Storage path (gs://...
). You can reuse an existing folder to load current checkpoint data and to store additional checkpoints.--iterations
specifies the number of training steps to run on the TPU on each call before returning control to python. If this number is too small (for example, less than 100) then this can result in excessive communication overhead which negatively impacts performance.--train_steps
specifies the total number of steps (batches) for training to run.
Running the model on a local (non-TPU) machine
To run the model on a non-TPU machine, omit --tpu
, and set the following flag:
--use_tpu=False
This causes the computation to land on a GPU if one is present. If no GPU is present, the computation falls back to the CPU.
What to expect
By default, the tf.estimator model reports loss value and step time in the following format:
INFO:tensorflow:Calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:TPU job name tpu_worker INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Init TPU system INFO:tensorflow:Start infeed thread controller INFO:tensorflow:Starting infeed thread controller. INFO:tensorflow:Start outfeed thread controller INFO:tensorflow:Starting outfeed thread controller. INFO:tensorflow:Enqueue next (500) batch(es) of data to infeed. INFO:tensorflow:Dequeue next (500) batch(es) of data from outfeed. INFO:tensorflow:Saving checkpoints for 500 into gs://ctpu-mnist-test/output/model.ckpt. INFO:tensorflow:loss = 0.08896458, step = 0 INFO:tensorflow:loss = 0.08896458, step = 0 INFO:tensorflow:Enqueue next (500) batch(es) of data to infeed. INFO:tensorflow:Dequeue next (500) batch(es) of data from outfeed. INFO:tensorflow:Enqueue next (500) batch(es) of data to infeed. INFO:tensorflow:Dequeue next (500) batch(es) of data from outfeed. INFO:tensorflow:global_step/sec: 242.829 INFO:tensorflow:examples/sec: 248715 INFO:tensorflow:Enqueue next (500) batch(es) of data to infeed. INFO:tensorflow:Dequeue next (500) batch(es) of data from outfeed. INFO:tensorflow:Saving checkpoints for 2000 into gs://ctpu-mnist-test/output/model.ckpt. INFO:tensorflow:Stop infeed thread controller INFO:tensorflow:Shutting down InfeedController thread. INFO:tensorflow:InfeedController received shutdown signal, stopping. INFO:tensorflow:Infeed thread finished, shutting down. INFO:tensorflow:Stop output thread controller INFO:tensorflow:Shutting down OutfeedController thread. INFO:tensorflow:OutfeedController received shutdown signal, stopping. INFO:tensorflow:Outfeed thread finished, shutting down. INFO:tensorflow:Shutdown TPU system. INFO:tensorflow:Loss for final step: 0.044236258.
Clean up
To avoid incurring charges to your GCP account for the resources used in this tutorial:
Disconnect from the Compute Engine VM:
(vm)$ exit
Your prompt should now be
user@projectname
, showing you are in the Cloud Shell.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: --
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.Run
gsutil
as shown, replacingYOUR-BUCKET-NAME
with the name of the Cloud Storage bucket you created for this tutorial:$ gsutil rm -r gs://YOUR-BUCKET-NAME
What's next
- Learn more about
ctpu
, including how to install it on a local machine. - Verify performance on a large-scale model by running the ResNet sample.
- Explore the TPU tools in TensorBoard.