Training ResNet on Cloud TPU (TF 2.x)

This tutorial shows you how to train a Keras ResNet model on Cloud TPU using tf.distribute.TPUStrategy.

If you are not familiar with Cloud TPU, it is strongly recommended that you go through the quickstart to learn how to create a TPU and a Compute Engine VM.

Warning: 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.

Objectives

• Create a Cloud Storage bucket to hold your dataset and model output.
• Prepare a fake imagenet dataset that is similar to the ImageNet dataset.
• Run the training job.
• Verify the output results.

Costs

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

Before starting this tutorial, check that your Google Cloud 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. In the Google Cloud Console, on the project selector page, select or create a Google Cloud project.

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

   Go to the project selector page

3. Make sure that billing is enabled for your Cloud project. Learn how to confirm that billing is enabled for your project.

This walkthrough uses billable components of Google Cloud. 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 unnecessary charges.

Set up your resources

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

Important: Set up all resources (Compute Engine VM, Cloud TPU, and Cloud Storage bucket) in the same region/zone to reduce network latency and network costs. VMs and TPU nodes are located in specific zones, which are subdivisions within a region.

1. Open a Cloud Shell window.

   Open Cloud Shell

2. Create a variable for your project's ID.

   export PROJECT_ID=project-id
   

3. Configure gcloud command-line tool to use the project where you want to create Cloud TPU.

   gcloud config set project ${PROJECT_ID}
   

   The first time you run this command in a new Cloud Shell VM, an Authorize Cloud Shell page is displayed. Click Authorize at the bottom of the page to allow gcloud to make GCP API calls with your credentials.

4. Create a Service Account for the Cloud TPU project.

   gcloud beta services identity create --service tpu.googleapis.com --project $PROJECT_ID
   

   The command returns a Cloud TPU Service Account with following format:

   [e-mail]
   

5. Create a Cloud Storage bucket using the following command:

   gsutil mb -p ${PROJECT_ID} -c standard -l europe-west4 -b on gs://bucket-name
   

   This Cloud Storage bucket stores 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 you set up in the previous step. If you want finer-grain permissions, review the access level permissions.

   The bucket location must be in the same region as your Compute Engine (VM) and your Cloud TPU node.

6. Launch the Compute Engine VM resource using the ctpu up command.

   ctpu up --project=${PROJECT_ID} \
    --zone=europe-west4-a \
    --vm-only \
    --name=resnet-tutorial \
    --disk-size-gb=300 \
    --machine-type=n1-standard-16 \
    --tf-version=2.4.0

   Command flag descriptions

   project

   Your GCP project ID

   zone

   The zone where you plan to create your Cloud TPU.

   vm-only

   Create a VM only. By default the ctpu up command creates a VM and a Cloud TPU.

   name

   The name of the Cloud TPU to create.

   disk-size-gb

   The size of the hard disk in GB of the VM created by the ctpu up command.

   machine-type

   The machine type of the Compute Engine VM to create.

   tf-version

   The version of Tensorflow ctpu installs on the VM.

   For more information on the CTPU utility, see the CTPU Reference.

7. When prompted, 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.

   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.

   Note: If you are not connected to the Compute Engine instance, you can connect by running the following command:

   gcloud compute ssh resnet-tutorial --zone=europe-west4-a
   

   As you continue these instructions, run each command that begins with (vm)$ in your Compute Engine instance.

Set Cloud Storage bucket variables

Set up the following environment variables, replacing bucket-name with the name of your Cloud Storage bucket:

(vm)$ export STORAGE_BUCKET=gs://bucket-name

(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/resnet-2x
(vm)$ export DATA_DIR=gs://cloud-tpu-test-datasets/fake_imagenet
(vm)$ export PYTHONPATH="$PYTHONPATH:/usr/share/models/"

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.

Train and evaluate the ResNet model with fake_imagenet

ImageNet is an image database. The images in the database are organized into a hierarchy, with each node of the hierarchy depicted by hundreds and thousands of images.

This tutorial uses a demonstration version of the full ImageNet dataset, referred to as fake_imagenet. This demonstration version allows you to test the tutorial, while reducing the storage and time requirements typically associated with running a model against the full ImageNet database.

The fake_imagenet dataset is at this location on Cloud Storage:

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

The fake_imagenet 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.

For information on how to download and process the full ImageNet dataset, see Downloading, preprocessing, and uploading the ImageNet dataset.

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

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

1. Launch a Cloud TPU resource using the ctpu utility.

   (vm)$ ctpu up --project=${PROJECT_ID} \
    --tpu-only \
    --tpu-size=v3-8  \
    --name=resnet-tutorial \
    --zone=europe-west4-a \
    --tf-version=2.4.0
   

   Command flag descriptions

   tpu-only

   Creates the Cloud TPU without creating a VM. By default the ctpu up command creates a VM and a Cloud TPU.

   project

   Your GCP project ID

   tpu-size

   The type of the Cloud TPU to create.

   name

   The name of the Cloud TPU to create.

   zone

   The zone where you plan to create your Cloud TPU.

   tf-version

   The version of Tensorflow ctpu installs on the VM.

   For more information on the CTPU utility, see the CTPU Reference.

2. Set the Cloud TPU name variable. This will either be a name you specified with the --name parameter to ctpu up or the default, your username:

   (vm)$ export TPU_NAME=resnet-tutorial
   

3. The ResNet training script requires an extra package. Install it now:

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

4. Navigate to the ResNet-50 model directory:

   (vm)$ cd /usr/share/models/official/vision/image_classification/resnet/
   

   Note: If you are running TensorFlow 2.1, use the path: /usr/share/models/official/vision/image_classification/

5. Run the training script. This uses a fake_imagenet dataset and trains ResNet for one epoch.

   (vm)$ python3 resnet_ctl_imagenet_main.py \
    --tpu=${TPU_NAME} \
    --model_dir=${MODEL_DIR} \
    --data_dir=${DATA_DIR} \
    --batch_size=1024 \
    --steps_per_loop=500 \
    --train_epochs=1 \
    --use_synthetic_data=false \
    --dtype=fp32 \
    --enable_eager=true \
    --enable_tensorboard=true \
    --distribution_strategy=tpu \
    --log_steps=50 \
    --single_l2_loss_op=true \
    --use_tf_function=true
   

   Command flag descriptions

   tpu

   The name of your TPU.

   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 as long as the previous checkpoints were created using TPU of the same size and TensorFlow version.

   data_dir

   The Cloud Storage path of training input. It is set to the fake_imagenet dataset in this example.

   batch_size

   The training batch size.

   steps_per_loop

   The number of training steps to run before saving state to the CPU. A training step is the processing of one batch of examples. This includes both a forward pass and back propagation.

   train_epochs

   The number of times to train the model using the entire dataset.

   use_synthetic_data

   Whether to use synthetic data for training.

   dtype

   The data type to use for training.

   enable_eager

   Enable TensorFlow eager execution.

   enable_tensorboard

   Enable TensorBoard.

   distribution_strategy

   To train the ResNet model on a TPU, set distribution_strategy to tpu.

   log_steps

   The number of training steps to take before logging timing information such as examples per second.

   single_l2_loss_op

   Calculate L2_loss on concatenated weights, instead of using Keras per-layer L2 loss.

   use_tf_function

   Wrap the train and test steps inside a tf.function.

This will train ResNet for 1 epoch and will complete on a v3-8 TPU node in under 10 minutes. At the end of the training, output similar to the following appears:

I1107 20:28:57.561836 140033625347520 resnet_ctl_imagenet_main.py:222] Training 1 epochs, each epoch has 1251 steps, total steps: 1251; Eval 48 steps
I1107 20:34:09.638025 140033625347520 resnet_ctl_imagenet_main.py:358] Training loss: 0.6292637, accuracy: 0.99680257 at epoch 1
I1107 20:34:21.682796 140033625347520 resnet_ctl_imagenet_main.py:372] Test loss: 3.8977659, accuracy: 0.0% at epoch: 1
I1107 20:34:22.028973 140033625347520 resnet_ctl_imagenet_main.py:392]
Run stats:
{'train_loss': 0.6292637, 'train_acc': 0.99680257, 'eval_acc': 0.0, 'step_timestamp_log':
['BatchTimestamp <batch_index: 1, timestamp: 1573158554.11>'],
'train_finish_time': 1573158861.683073, 'eval_loss': 3.8977659>}

To train the ResNet to convergence, run it for 90 epochs as shown in the following script. Training and evaluation are done together. Each epoch has 1251 steps for a total of 112590 training steps and 48 evaluation steps.

(vm)$ python3 resnet_ctl_imagenet_main.py \
    --tpu=${TPU_NAME} \
    --model_dir=${MODEL_DIR} \
    --data_dir=${DATA_DIR} \
    --batch_size=1024 \
    --steps_per_loop=500 \
    --train_epochs=90 \
    --use_synthetic_data=false \
    --dtype=fp32 \
    --enable_eager=true \
    --enable_tensorboard=true \
    --distribution_strategy=tpu \
    --log_steps=50 \
    --single_l2_loss_op=true \
    --use_tf_function=true

Command flag descriptions

tpu

The name of your TPU.

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 as long as the previous checkpoints were created using a Cloud TPU of the same size and TensorFlow version.

data_dir

The Cloud Storage path of training input. It is set to the fake_imagenet dataset in this example.

batch_size

The training batch size.

steps_per_loop

The number of training steps to run before saving state to the CPU. A training step is the processing of one batch of examples. This includes both a forward pass and back propagation.

train_epochs

The number of times to train the model using the entire dataset.

use_synthetic_data

Whether to use synthetic data for training.

dtype

The data type to use for training.

enable_eager

Enable TensorFlow eager execution.

enable_tensorboard

Enable TensorBoard.

distribution_strategy

To train the ResNet model on a TPU, set distribution_strategy to tpu.

log_steps

The number of training steps to take before logging timing information such as examples per second.

single_l2_loss_op

Calculate L2_loss on concatenated weights, instead of using Keras per-layer L2 loss.

use_tf_function

Wrap the train and test steps inside a tf.function.

Since the training and evaluation was done on the fake_imagenet dataset, the output results do not reflect actual output that would appear if the training and evaluation was performed on a real dataset.

At this point, you can either conclude this tutorial and clean up your GCP resources, or you can further explore running the model on Cloud TPU Pods.

Scaling your model with Cloud TPU Pods

You can get results faster by scaling your model with Cloud TPU Pods. The fully supported ResNet-50 model can work with the following Pod slices:

• v2-32
• v3-32

With Cloud TPU Pods, training and evaluation are done together.

Training with Cloud TPU Pods

1. Delete the Cloud TPU resource you created for training the model on a single device.

   (vm)$ ctpu delete --project=${PROJECT_ID} \
    --zone=europe-west4-a \
    --tpu-only \
    --name=resnet-tutorial

   Command flag descriptions

   project

   Your GCP project ID

   zone

   The zone where you plan to create your Cloud TPU.

   tpu-only

   Deletes the Cloud TPU.

   name

   The name of the Cloud TPU to create.

   disk-size-gb

   The size of the hard disk in GB of the VM created by the ctpu up command.

2. After the Cloud TPU has been deleted, create a new Cloud TPU Pod. Run the ctpu up command, using the tpu-size parameter to specify the Pod slice you want to use. For example, the following command uses a v3-32 Pod slice.

   (vm)$ ctpu up --project=${PROJECT_ID} \
     --zone=europe-west4-a \
     --tpu-only \
     --name=resnet-tutorial \
     --tpu-size=v3-32
   

   Command flag descriptions

   project

   Your GCP project ID

   zone

   The zone where you plan to create your Cloud TPU.

   tpu-only

   Creates the Cloud TPU only. By default the ctpu up command creates both a VM and a Cloud TPU.

   name

   The name of the Cloud TPU to create.

   tpu-size

   The type of the Cloud TPU to create.

   For more information on the CTPU utility, see the CTPU Reference.

3. Set some required environment variables:

   (vm)$ export TPU_NAME=resnet-tutorial
   (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/resnet-2x-pod
   

4. Navigate to the script directory:

   (vm)$ cd /usr/share/models/official/vision/image_classification/resnet
   

   Note: if you are running TensorFlow 2.1, use the path: /usr/share/models/official/vision/image_classification/

5. Train the model.

   (vm)$ python3 resnet_ctl_imagenet_main.py \
     --tpu=${TPU_NAME} \
     --model_dir=${MODEL_DIR} \
     --data_dir=${DATA_DIR} \
     --batch_size=4096 \
     --steps_per_loop=500 \
     --train_epochs=1 \
     --use_synthetic_data=false \
     --dtype=fp32 \
     --enable_eager=true \
     --enable_tensorboard=true \
     --distribution_strategy=tpu \
     --log_steps=50 \
     --single_l2_loss_op=true \
     --use_tf_function=true
    

   Command flag descriptions

   tpu

   The name of your TPU.

   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 as long as the previous checkpoints were created using Cloud TPU of the same size and TensorFlow version.

   data_dir

   The Cloud Storage path of training input. It is set to the fake_imagenet dataset in this example.

   batch_size

   The training batch size.

   steps_per_loop

   The number of training steps to run before saving state to the CPU. A training step is the processing of one batch of examples. This includes both a forward pass and back propagation.

   train_epochs

   The number of times to train the model using the entire dataset.

   use_synthetic_data

   Whether to use synthetic data for training.

   dtype

   The data type to use for training.

   enable_eager

   Enables TensorFlow eager execution.

   enable_tensorboard

   Enables TensorBoard.

   distribution_strategy

   To train the ResNet model on a TPU, set distribution_strategy to tpu.

   log_steps

   The number of training steps to take before logging timing information such as examples per second.

   single_l2_loss_op

   Calculate L2_loss on concatenated weights, instead of using Keras per-layer L2 loss.

   use_tf_function

   Wrap the train and test steps inside a tf.function.

The procedure trains the model on the fake_imagenet dataset to 1 epoch (312 total training steps and 12 evaluation steps). This training takes approximately 2 minutes on a v3-32 Cloud TPU. When the training and evaluation complete, a message similar to the following appears:

1107 22:45:19.821746 140317155378624 resnet_ctl_imagenet_main.py:358] Training loss: 0.22576721, accuracy: 0.838141 at epoch 1
I1107 22:45:33.892045 140317155378624 resnet_ctl_imagenet_main.py:372] Test loss: 0.26673648, accuracy: 0.0% at epoch: 1
I1107 22:45:34.851322 140317155378624 resnet_ctl_imagenet_main.py:392] Run stats:
{'train_loss': 0.22576721, 'train_acc': 0.838141, 'eval_acc': 0.0, 'step_timestamp_log': ['BatchTimestamp<batch_index: 1, timestamp: 1573166574.67>'], 'train_finish_time': 1573166733.892282, 'eval_loss': 0.26673648}

Cleaning up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

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 --zone flag you used when you set up the Compute Engine VM and Cloud TPU. This deletes both your VM and your Cloud TPU.

   $ ctpu delete --project=${PROJECT_ID} \
     --zone=europe-west4-a \
     --name=resnet-tutorial
   

   Important: If you set the TPU resources name when you ran ctpu up, you must specify that name with the --name flag when you run ctpu delete in order to shut down your TPU resources.

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 --project=${PROJECT_ID} \
     --zone=europe-west4-a

   2018/04/28 16:16:23 WARNING: Setting zone to "europe-west4-a"
   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
   

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

What's next

In this tutorial you have trained the RESNET model using a sample dataset. The results of this training are (in most cases) not usable for inference. To use a model for inference you can train the data on a publicly available dataset or your own data set. Models trained on Cloud TPUs require datasets to be in TFRecord format.

You can use the dataset conversion tool sample to convert an image classification dataset into TFRecord format. If you are not using an image classification model, you will have to convert your dataset to TFRecord format yourself. For more information, see TFRecord and tf.Example

Hyperparameter tuning

To improve the model's performance with your dataset, you can tune the model's hyperparameters. You can find information about hyperparameters common to all TPU supported models on GitHub. Information about model-specific hyperparameters can be found in the source code for each model. For more information on hyperparameter tuning, see Overview of hyperparameter tuning, Using the Hyperparameter tuning service and Tune hyperparameters.

Inference

Once you have trained your model you can use it for inference (also called prediction). AI Platform is a cloud-based solution for developing, training, and deploying machine learning models. Once a model is deployed, you can use the AI Platform Prediction service.

• Learn how to train and evaluate using your own data in place of the fake_imagenet or ImageNet datasets by following the dataset conversion tutorial. The tutorial explains how to use the image classification data converter example script to convert a raw dataset for image classification into TFRecords usable by Cloud TPU Tensorflow models.
• Run a Cloud TPU colab that demonstrates how to run an image classification model using your own image data.
• Explore the other Cloud TPU tutorials.
• Learn to use the TPU monitoring tools in TensorBoard.
• See how to train ResNet with Cloud TPU and GKE.