Training EfficientNet on Cloud TPU

Objective: Train the Tensorflow EfficientNet model using a Cloud TPU device or Cloud TPU Pod slice (multiple TPU devices).

The EfficientNet models are a family of image classification models, which achieve state-of-the-art accuracy, while also being smaller and faster than other models. EfficientNet-EdgeTpu are models customized to run efficiently on the Google EdgeTPU devices.

The model in this tutorial is based on EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Researchers developed a new technique to improve model performance: carefully balancing network depth, width, and resolution, using a simple yet highly effective compound coefficient.

The family of models from efficientnet-b0 to efficientnet-b7, can achieve decent image classification accuracy given the resource constrained Google EdgeTPU devices.

efficientnet-b0, the model used in this tutorial, corresponds to the smallest base model, whereas efficientnet-b7 corresponds to the most power but computation-expensive model. The tutorial demonstrates training the model using TPUEstimator.

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 test version of the ImageNet dataset, referred to as the fake_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 Cloud Console, on the project selector page, select or create a 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 Google Cloud project. Learn how to confirm 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, VM, and Cloud TPU resources for tutorials.

Important: Set up all resources in the same region/zone to reduce network latency and network costs.

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. If you want finer-grain permissions, review the access level permissions.

   The bucket location must be in the same region as your virtual machine (VM) and your TPU node. VMs and TPU nodes are located in specific zones, which are subdivisions within a region.

6. Launch the Compute Engine and Cloud TPU resources required for this using the ctpu up command.

   ctpu up --project=${PROJECT_ID} \
   --zone=europe-west4-a \
   --vm-only \
   --disk-size-gb=300 \
   --machine-type=n1-standard-8 \
   --tf-version=1.15.4 \
   --name=efficientnet-tutorial
   

   Command flag descriptions

   project

   Your GCP project ID

   zone

   The zone where you plan to create your Cloud TPU.

   vm-only

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

   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.

   name

   The name of the Cloud TPU to create.

   For more information on the CTPU utility, see 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.

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

   From this point on, a prefix of (vm)$ means you should run the command on the Compute Engine VM instance.

Prepare the data

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

1. Create an environment variable for your bucket name. Replace bucket-name with your bucket name.

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

2. Create some additional environment variables.

   (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/efficientnet
   (vm)$ export DATA_DIR=gs://cloud-tpu-test-datasets/fake_imagenet
   (vm)$ export TPU_NAME=efficientnet-tutorial
   (vm)$ export PYTHONPATH=$PYTHONPATH:/usr/share/tpu/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 EfficientNet 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, do not 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=${PROJECT_ID} \
    --tpu-only \
    --tf-version=1.15.4 \
    --name=${TPU_NAME}
   

2. Navigate to the model directory:

   (vm)$ cd /usr/share/tpu/models/official/efficientnet/
   

3. Run the training script.

   (vm)$ python3 main.py \
     --tpu=${TPU_NAME} \
     --data_dir=${DATA_DIR} \
     --model_dir=${MODEL_DIR} \
     --model_name='efficientnet-b0' \
     --skip_host_call=true \
     --train_batch_size=2048 \
     --train_steps=218948
   

   Parameter	Description
   tpu	Specifies the name of the Cloud TPU. Note that ctpu passes this name to the Compute Engine VM as an environment variable (TPU_NAME).
   data_dir	Specifies the Cloud Storage path for training input. It is set to the fake_imagenet dataset in this example.
   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.

This procedure trains the EfficientNet model (efficientnet-b0 variant) for 350 epochs and evaluates every fixed number of steps. Using the specified flags, the model should train in about 23 hours. With real imagenet data, the settings should obtain ~76.5% top-1 accuracy on ImageNet validation dataset. The best model checkpoint and corresponding evaluation result is inside the archive folder in the model directory: ${STORAGE_BUCKET}/efficientnet/archive.

Scaling your model with Cloud TPU Pods

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

• v2-32
• v3-32

When working with Cloud TPU Pods, you first train the model using a Pod, then use a single Cloud TPU device to evaluate the model.

Training with Cloud TPU Pods

Note: If you have already deleted your Compute Engine instance, create a new one following the steps in Set up your resources.

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

   (vm)$ ctpu delete --project=${PROJECT_ID} \
    --tpu-only \
    --name=efficientnet-tutorial

2. 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 v2-32 Pod slice.

   (vm)$ ctpu up --project=${PROJECT_ID} \
     --tpu-only \
     --tf-version=1.15.4 \
     --tpu-size=v2-32 \
     --name=efficientnet-tutorial-pod
   

3. Create an environment variable for your TPU name.

   (vm)$ export TPU_NAME=efficientnet-tutorial-pod
   

4. Update the MODEL_DIR directory to store the training data.

   (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/efficientnet-tutorial-pod
   

5. Train the model.

   (vm)$ python3 main.py \
     --tpu=${TPU_NAME} \
     --data_dir=${DATA_DIR} \
     --model_dir=${MODEL_DIR} \
     --model_name='efficientnet-b3' \
     --skip_host_call=true \
     --mode=train \
     --train_steps=109474 \
     --train_batch_size=4096 \
     --iterations_per_loop=100
   

   Parameter	Description
   tpu	Specifies the name of the Cloud TPU. Note that ctpu passes this name to the Compute Engine VM as an environment variable (TPU_NAME).
   data_dir	Specifies the Cloud Storage path for training input. It is set to the fake_imagenet dataset in this example.
   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.

The procedure trains the EfficientNet model (efficientnet-b3 variant) for 350 epochs. The model should get 81.1% accuracy on ImageNet dev set, which should finish in around 20 hours. The best model checkpoint and corresponding evaluation result is inside the archive folder in the model directory: ${STORAGE_BUCKET}/efficientnet/archive.

Note: The edge TPU efficientnets comes in three sizes: S, M, and L. For training the edgetpu versions of efficientnet, simply provide the model_name as efficientnet-edgetpu-{S,M,L}

Evaluating the model

In this set of steps, you use Cloud TPU to evaluate the above trained model against the fake_imagenet validation data.

1. Delete the Cloud TPU resource you created to train the model on a Pod.

   (vm)$ ctpu delete --project=${PROJECT_ID} \
    --tpu-only \
    --name=efficientnet-tutorial-pod

2. Start a v2-8 Cloud TPU. Use the same name that you used for the Compute Engine VM, which should still be running.

   (vm)$ ctpu up --project=${PROJECT_ID} \
    --tpu-only \
    --tf-version=1.15.4 \
    --name=efficientnet-tutorial-eval
   

3. Create an environment variable for your TPU name.

   (vm)$ export TPU_NAME=efficientnet-tutorial-eval
   

4. Run the model evaluation. This time, add the mode flag and set it to eval.

   (vm)$ python3 main.py \
      --tpu=${TPU_NAME} \
      --data_dir=${DATA_DIR} \
      --model_dir=${MODEL_DIR} \
      --model_name='efficientnet-b3' \
      --skip_host_call=true \
      --mode=eval
   

   This generates output similar to the following:

      Eval results: {'loss': 7.532023,
      'top_1_accuracy': 0.0010172526,
      'global_step': 100,
      'top_5_accuracy': 0.005065918}.
      Elapsed seconds: 88
   

Cleaning up

To avoid incurring charges to your Google Cloud Platform account for the resources used in this tutorial:

1. Delete your Cloud TPU

   $ ctpu delete --project=${PROJECT_ID} \
     --tpu-only \
     --name=efficientnet-tutorial-eval \
     --zone=europe-west4-a
   

2. 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.

3. 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 --project=${PROJECT_ID} \
     --name=efficientnet-tutorial \
     --zone=europe-west4-a
   

   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.

4. Verify that you have no instances allocated to avoid unnecessary charges for TPU usage.

   $ ctpu status --project=${PROJECT_ID} \
     --name=efficientnet-tutorial \
     --zone=europe-west4-a
   

   Deleting resources can take several minutes. A response like the one below indicates there are no more allocated instances:

   2018/04/28 16:16:23 WARNING: Setting zone to "europe-west4-a"
   No instances currently exist.
           Compute Engine VM:     --
           Cloud TPU:             --
   

5. 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 EfficientNet 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 more about ctpu, including how to install it on a local machine.
• Explore the TPU tools in TensorBoard.