Training EfficientNet on Cloud TPU (TF 2.x)

---

This tutorial shows you how to train a Keras EfficientNet 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 Cloud TPU and 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

In this document, you use the following billable components of Google Cloud:

• Compute Engine
• Cloud TPU
• Cloud Storage

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

Before you begin

Important: You can use this tutorial 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 tutorial, 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 starting this tutorial, check that your Google Cloud project is correctly set up.

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

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 project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. 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 project selector

5. Make sure that billing is enabled for your Google Cloud 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.

Cloud TPU single device training

Note: Do you want to train the model using a Cloud TPU pod? See TPU Pod training.

This section describes how to configure Cloud TPU resources and train the EfficientNet model using a single Cloud TPU device.

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 Cloud 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 Google Cloud CLI 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 API calls with your credentials.

   For more information on the gcloud command, see the gcloud Reference.

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

   Service accounts allow the Cloud TPU service to access other Google Cloud services.

   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. Export TPU setup variables

   Set the zone where you will train the model and store any training-related data.

   $ export ZONE=europe-west4-a
   

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

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

   This Cloud Storage bucket stores the data you use to train your model and the training results. The gcloud compute tpus execution-groups command 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.

7. Prepare your dataset or use 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.

   If you want to use the full ImageNet dataset, see Downloading, preprocessing, and uploading the ImageNet dataset.

   Caution: For this tutorial, make sure you 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 cannot write to it. As a result, you cannot 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.

8. Launch TPU resources using the gcloud command. The command you use depends on whether you are using TPU VMs or TPU nodes. For more information on the two VM architecture, see System Architecture.

   TPU VM

   $ gcloud compute tpus tpu-vm create efficientnet-tutorial \
   --zone=${ZONE} \
   --accelerator-type=v3-8 \
   --version=tpu-vm-tf-2.15.0-pjrt
   

   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.

   TPU Node

   gcloud compute tpus execution-groups create \
   --name=efficientnet-tutorial \
   --zone=${ZONE} \
   --disk-size=300 \
   --machine-type=n1-standard-16 \
   --tf-version=2.12.0 \
   --accelerator-type=v3-8
   

   Command flag descriptions

   project

   Your Google Cloud 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.

   For more information on the gcloud command, see the gcloud Reference.

   Note: The first time you run gcloud compute tpus tpu-vm or gcloud compute tpus execution-groups on a project it takes approximately 5 minutes to perform startup tasks such as SSH key propagation and API turnup.

9. If you are not automatically logged in to the Compute Engine instance, log in by running the following ssh command. When you are logged into the VM, your shell prompt changes from username@projectname to username@vm-name:

   TPU VM

   gcloud compute tpus tpu-vm ssh efficientnet-tutorial --zone=${ZONE}
   

   TPU Node

   gcloud compute ssh efficientnet-tutorial --zone=${ZONE}
   

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

10. Set the Cloud TPU name variable.

    TPU VM

    (vm)$ export TPU_NAME=local
    

    TPU Node

    (vm)$ export TPU_NAME=efficientnet-tutorial
    

11. Set Cloud Storage bucket variables

    Replace bucket-name with the name of your Cloud Storage bucket:

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

    (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/efficientnet-2x
    (vm)$ export DATA_DIR=gs://cloud-tpu-test-datasets/fake_imagenet
    

    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.

12. If you set --version=tpu-vm-tf-2.15.0-pjrt when creating your TPU, set the following environment variables to enable the PJRT runtime:

      (vm)$ export NEXT_PLUGGABLE_DEVICE_USE_C_API=true
      (vm)$ export TF_PLUGGABLE_DEVICE_LIBRARY_PATH=/lib/libtpu.so
    

13. Install TensorFlow requirements.

    The command you use depends on whether you are using TPU VMs or TPU Nodes.

    TPU VM

    (vm)$ pip3 install -r /usr/share/tpu/models/official/requirements.txt
    

    TPU Node

    (vm)$ pip3 install --user -r /usr/share/models/official/requirements.txt
    

14. The EfficientNet training script requires extra packages (TPU VM only). Install them now:

    TPU VM

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

15. Set some required environment variables:

    TPU VM

    (vm)$ export PYTHONPATH="${PYTHONPATH}:/usr/share/tpu/models"
    

    TPU Node

    (vm)$ export PYTHONPATH="${PYTHONPATH}:/usr/share/models"
    

    The EfficientNet model is pre-installed on your Compute Engine VM.

16. Change to directory that stores the model:

    TPU VM

    (vm)$ cd /usr/share/tpu/models/official/legacy/image_classification
    

    TPU Node

    (vm)$ cd /usr/share/models/official/legacy/image_classification
    

17. Train the model. This uses a fake_imagenet dataset and trains EfficientNet for one epoch.

    (vm)$ python3 classifier_trainer.py \
      --mode=train_and_eval \
      --model_type=efficientnet \
      --dataset=imagenet \
      --tpu=${TPU_NAME} \
      --data_dir=${DATA_DIR} \
      --model_dir=${MODEL_DIR} \
      --config_file=configs/examples/efficientnet/imagenet/efficientnet-b0-tpu.yaml \
      --params_override="train.epochs=1, train_dataset.builder=records, validation_dataset.builder=records"
    

    Command flag descriptions

    mode

    One of train, eval, or train_and_eval.

    model_type

    The type of the model. For example, efficientnet.

    dataset

    The name of the dataset. For example, imagenet.

    tpu

    The name of the Cloud TPU to run training or evaluation.

    data_dir

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

    model_dir

    The Cloud Storage path where checkpoints and summaries are stored during model training. You can reuse an existing folder to load previously generated checkpoints and to store additional checkpoints as long as the previous checkpoints were created using a Cloud TPU of the same size and TensorFlow version.

    config_file

    The path to the json file containing the pre-trained EfficientNet model. This file contains the model architecture.

    params_override

    A JSON string that overrides default script parameters. For more information on script parameters, see /usr/share/models/official/legacy/detection/main.py.

    This will train EfficientNet for 1 epoch and will complete on a v3-8 Cloud TPU node in approximately 40 minutes. When the training script completes, output similar to the following appears:

    Run stats:
    {
      'accuracy_top_1': 0.0010172526817768812,
      'eval_loss': 7.104171276092529,
      'loss': 7.113735675811768,
      'training_accuracy_top_1': 0.0009773431811481714,
      'step_timestamp_log': [
        'BatchTimestamp<batch_index: 0,
        timestamp: 1604960724.2224622>',
        'BatchTimestamp<batch_index: 1251,
        timestamp: 1604961281.3745298>'
      ],
      'train_finish_time': 1604961342.6359076,
      'avg_exp_per_second': 2071.493269569079
    }
    

    To train the EfficientNet to convergence on the ImageNet dataset, 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 classifier_trainer.py \
         --mode=train_and_eval \
         --model_type=efficientnet \
         --dataset=imagenet \
         --tpu=${TPU_NAME} \
         --data_dir=${DATA_DIR} \
         --model_dir=${MODEL_DIR} \
         --config_file=configs/examples/efficientnet/imagenet/efficientnet-b0-tpu.yaml \
         --params_override="train_dataset.builder=records, validation_dataset.builder=records"
    

    Command flag descriptions

    mode

    One of train, eval, or train_and_eval.

    model_type

    The type of the model. For example, efficientnet, etc.

    dataset

    The name of the dataset. For example, imagenet.

    tpu

    The name of the Cloud TPU to run training or evaluation.

    data_dir

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

    model_dir

    The Cloud Storage path where checkpoints and summaries are stored during model training. You can reuse an existing folder to load previously generated checkpoints and to store additional checkpoints as long as the previous checkpoints were created using a Cloud TPU of the same size and TensorFlow version.

    config_file

    The path to the JSON file containing the pre-trained EfficientNet model. This file contains the model architecture.

    params_override

    A JSON string that overrides default script parameters. For more information on script parameters, see /usr/share/models/official/legacy/detection/main.py.

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

    You have now completed single-device training. Use the following steps to delete the current single-device TPU resources.

18. Disconnect from the Compute Engine instance:

    (vm)$ exit
    

    Your prompt should now be username@projectname, showing you are in the Cloud Shell.

19. Delete the TPU resource.

    TPU VM

    $ gcloud compute tpus tpu-vm delete efficientnet-tutorial \
    --zone=${ZONE}
    

    Command flag descriptions

    zone

    The zone where your Cloud TPU resided.

    TPU Node

    $ gcloud compute tpus execution-groups delete efficientnet-tutorial \
    --tpu-only \
    --zone=${ZONE}
    

    Command flag descriptions

    tpu-only

    Deletes only the Cloud TPU. The VM remains available.

    zone

    The zone that contains the TPU to delete.

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

Scale your model with Cloud TPU Pods

Important: You can scale your model with either the TPU VM or the TPU Node configuration. The two VM architectures are described in System Architecture. For Pod training, you only need to follow the instructions for your TPU configuration, TPU VM or TPU Node.

Training your model on Cloud TPU Pods may require some changes to your training script. For information, see Training on TPU Pods.

Cloud TPU Pod training

Important: If you have already set up a Cloud TPU project, Service Account, storage bucket, and dataset for single device training, you can skip to launching TPU resources.

This section provides information on setting up a Cloud Storage bucket and Cloud TPU resources for Pod training.

Important: Set up your Cloud TPU resources and your Cloud Storage bucket in the same region/zone to reduce network latency and network costs. Cloud TPUs 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 Google Cloud CLI 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 Google Cloud API calls with your credentials.

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

   Service accounts allow the Cloud TPU service to access other Google Cloud services.

   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 or use a bucket you created earlier for your project:

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

   This Cloud Storage bucket stores the data you use to train your model and the training results. The gcloud command 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 TPU VM.

6. Export TPU setup variables

   Set the zone where you will train the model and store any training-related data.

   $ export ZONE=europe-west4-a
   

7. Prepare your dataset or use 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.

   The default Pod training accesses a demonstration version of the full ImageNet dataset, referred to as fake_imagenet. This demonstration version allows you to test Pod training, while reducing the storage and time requirements typically associated with training a model against the full ImageNet database.

   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.

   If you want to use the full ImageNet dataset, see Downloading, preprocessing, and uploading the ImageNet dataset.

   Caution: Make sure you 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.

8. Launch your Cloud TPU resources 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. This tutorial specifies a v3-32 Pod. For other Pod options, see the available TPU types page.

   TPU VM

   Note: If there is not enough capacity currently available to create the TPU Pod, you can queue your request using queued resources. Queued resources allow you to receive capacity once it becomes available. To request your Cloud TPU resources as queued resources, use the gcloud alpha compute tpus queued-resources create command instead. For more information, see Manage Queued Resources.

   $ gcloud compute tpus tpu-vm create efficientnet-tutorial \
     --zone=${ZONE} \
     --accelerator-type=v3-32 \
     --version=tpu-vm-tf-2.15.0-pod-pjrt
   

   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.

   Note: The first time you run gcloud on a project it takes about 5 minutes to perform startup tasks such as SSH key propagation and API turnup.

   TPU Node

   (vm)$ gcloud compute tpus execution-groups  create --name=efficientnet-tutorial \
    --accelerator-type=v3-32  \
    --zone=${ZONE} \
    --tf-version=2.12.0
   

   Command flag descriptions

   name

   The name of the Cloud TPU to create.

   accelerator-type

   The type of the Cloud TPU to create.

   zone

   The zone where you plan to create your Cloud TPU.

   tf-version

   The version of Tensorflow gcloud installs on the VM.

   tpu-only

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

   Note: The first time you run gcloud on a project it takes about 5 minutes to perform startup tasks such as SSH key propagation and API turnup.

9. If you are not automatically logged in to the Compute Engine instance, log in by running the following ssh command. When you are logged into the VM, your shell prompt changes from username@projectname to username@vm-name:

   TPU VM

   $ gcloud compute tpus tpu-vm ssh efficientnet-tutorial --zone=${ZONE}
   

   TPU Node

   $ gcloud compute ssh efficientnet-tutorial --zone=${ZONE}
   

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

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

10. Export TPU setup variables:

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

    (vm)$ export TPU_NAME=efficientnet-tutorial
    (vm)$ export DATA_DIR=gs://cloud-tpu-test-datasets/fake_imagenet
    (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/efficientnet-2x-pod
    

    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.

11. Install TensorFlow requirements.

    TPU VM

    (vm)$ pip3 install -r /usr/share/tpu/models/official/requirements.txt
    

    TPU Node

    (vm)$ pip3 install -r /usr/share/models/official/requirements.txt
    

12. Set some required environment variables:

    TPU VM

    (vm)$ export PYTHONPATH="/usr/share/tpu/models:${PYTHONPATH}"
    (vm)$ export TPU_LOAD_LIBRARY=0
    

    TPU Node

    (vm)$ export PYTHONPATH="${PYTHONPATH}:/usr/share/models"
    

    The EfficientNet model is pre-installed on your Compute Engine VM.

13. Change to directory that stores the model:

    TPU VM

    (vm)$ cd /usr/share/tpu/models/official/legacy/image_classification/
    

    TPU Node

    (vm)$ cd /usr/share/models/official/legacy/image_classification/
    

14. Train the model.

    (vm)$ python3 classifier_trainer.py \
    --mode=train_and_eval \
    --model_type=efficientnet \
    --dataset=imagenet \
    --tpu=${TPU_NAME} \
    --data_dir=${DATA_DIR} \
    --model_dir=${MODEL_DIR} \
    --config_file=configs/examples/efficientnet/imagenet/efficientnet-b0-tpu.yaml \
    --params_override="train.epochs=1, train_dataset.builder=records, validation_dataset.builder=records"
    

    Command flag descriptions

    mode

    When set to train_and_eval this script trains and evaluates the model. When set to export_only this script exports a saved model.

    model_type

    The type of the model. For example, efficientnet, etc.

    dataset

    The name of the dataset. For example, imagenet.

    tpu

    Uses the name specified in the TPU_NAME variable.

    data_dir

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

    model_dir

    The Cloud Storage path where checkpoints and summaries are stored during model training. You can reuse an existing folder to load previously generated checkpoints and to store additional checkpoints as long as the previous checkpoints were created using a Cloud TPU of the same size and TensorFlow version.

    config_file

    The path to the json file containing the pre-trained EfficientNet model. This file contains the model architecture.

    params_override

    A JSON string that overrides default script parameters. For more information on script parameters, see /usr/share/models/official/legacy/detection/main.py.

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:

Run stats:
{
  'accuracy_top_1': 0.0009969075908884406,
  'eval_loss': 7.105168342590332,
  'loss': 7.114983081817627,
  'training_accuracy_top_1': 0.0010031675919890404,
  'step_timestamp_log': [
    'BatchTimestamp<batch_index: 0,
    timestamp: 1605041621.4997303>',
    'BatchTimestamp<batch_index: 312,
    timestamp: 1605041970.8633356>'
  ],
  'train_finish_time': 1605042032.2274444,
  'avg_exp_per_second': 3111.5120716536226
}

Clean 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. Delete your Cloud TPU and Compute Engine resources.

   TPU VM

   $ gcloud compute tpus tpu-vm delete efficientnet-tutorial \
   --zone=${ZONE}
   

   TPU Node

   $ gcloud compute tpus execution-groups delete efficientnet-tutorial \
   --zone=${ZONE}
   

3. Verify the resources have been deleted by running gcloud compute tpus execution-groups list. The deletion might take several minutes. The output from the following command should not include any of the TPU resources created in this tutorial:

   $ gcloud compute tpus execution-groups list --zone=${ZONE}
   

4. Delete your Cloud Storage bucket using gsutil as shown below. Replace bucket-name with the name of your Cloud Storage bucket.

   $ gsutil rm -r gs://bucket-name
   

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

What's next

The TensorFlow Cloud TPU tutorials generally train the model using a sample dataset. The results of this training are 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. TensorFlow models trained on Cloud TPUs generally 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.