Training Mask RCNN on Cloud TPU


This tutorial demonstrates how to run the Mask RCNN model using Cloud TPU with the COCO dataset.

Mask RCNN is a deep neural network designed to address object detection and image segmentation, one of the more difficult computer vision challenges.

The Mask RCNN model generates bounding boxes and segmentation masks for each instance of an object in the image. The model is based on the Feature Pyramid Network (FPN) and a ResNet50 neural network.

This tutorial uses tf.contrib.tpu.TPUEstimator to train the model. The TPUEstimator API is a high-level TensorFlow API and is the recommended way to build and run a machine learning model on Cloud TPU. The API simplifies the model development process by hiding most of the low-level implementation, which makes it easier to switch between TPU and other platforms such as GPU or CPU.


  • Create a Cloud Storage bucket to hold your dataset and model output
  • Prepare the COCO dataset
  • Set up a Compute Engine VM and Cloud TPU node for training and evaluation
  • Run training and evaluation on a single Cloud TPU or a Cloud TPU Pod


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.

    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.

  4. 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.

If you plan to train on a TPU Pod slice, review Training on TPU Pods to understand parameter changes required for Pod slices.

Set up your resources

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

  1. Open a Cloud Shell window.

    Open Cloud Shell

  2. Create an environment 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 the 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 --project $PROJECT_ID

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

  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 a Compute Engine VM using the ctpu up command.

    (vm)$ 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 \

    Command flag descriptions

    Your GCP project ID
    The zone where you plan to create your Cloud TPU.
    Creates the VM without creating a Cloud TPU. By default the ctpu up command creates a VM and a Cloud TPU.
    The size of the hard disk in GB of the VM created by the ctpu up command.
    The machine type of the Compute Engine VM to create.
    The version of Tensorflow ctpu installs on the VM.
    The name of the Cloud TPU to create.
  7. The configuration you specified appears. Enter y to approve or n to cancel.

  8. 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 mask-rcnn-tutorial --zone=europe-west4-a

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

Install extra packages

The Mask RCNN training application requires several extra packages. Install them now:

(vm)$ sudo apt-get install -y python3-tk && \
  pip3 install --user Cython matplotlib opencv-python-headless pyyaml Pillow && \
  pip3 install --user 'git+' && \
  pip3 install --user -U gast==0.2.2

Update the keepalive values of your VM connection

This tutorial requires a long-lived connection to the Compute Engine instance. To ensure you aren't disconnected from the instance, run the following command:

(vm)$ sudo /sbin/sysctl \
  -w net.ipv4.tcp_keepalive_time=120 \
  net.ipv4.tcp_keepalive_intvl=120 \

Prepare the data

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

    (vm)$ export STORAGE_BUCKET=gs://bucket-name
  2. Add an environment variable for the data directory.

    (vm)$ export DATA_DIR=${STORAGE_BUCKET}/coco
  3. Add an environment variable for the model directory.

    (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/mask-rcnn
  4. Run the script to convert the COCO dataset into a set of TFRecords (*.tfrecord) that the training application expects.

    (vm)$ sudo bash /usr/share/tpu/tools/datasets/ ./data/dir/coco

    This installs the required libraries and then runs the preprocessing script. It outputs a number of *.tfrecord files in your local data directory.

  5. Copy the data to your Cloud Storage bucket

    After you convert the data into TFRecords, copy them from local storage to your Cloud Storage bucket using the gsutil command. You must also copy the annotation files. These files help validate the model's performance.

    (vm)$ gsutil -m cp ./data/dir/coco/*.tfrecord ${DATA_DIR}
    (vm)$ gsutil cp ./data/dir/coco/raw-data/annotations/*.json ${DATA_DIR}

Set up and start the Cloud TPU

  1. Run the following command to create your Cloud TPU.

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

    Command flag descriptions

    Your GCP project ID

      <dd>Create a Cloud TPU only. By default the <code>ctpu up</code>
        command creates a VM and a Cloud TPU. </dd>
      <dd>The <a href="">zone</a>
        where you plan to create your Cloud TPU.</dd>
      <dd>The name of the Cloud TPU to create.</dd>
      <dd>The version of Tensorflow <code>ctpu</code> installs on the VM.</dd>

  2. The configuration you specified appears. Enter y to approve or n to cancel.

    You will see a message: Operation success; not ssh-ing to Compute Engine VM due to --tpu-only flag. Since you previously completed SSH key propagation, you can ignore this message.

  3. Add an environment variable for your Cloud TPU's name.

    (vm)$ export TPU_NAME=mask-rcnn-tutorial

Run the training and evaluation

  1. Add some required environment variables:

    (vm)$ export PYTHONPATH="${PYTHONPATH}:/usr/share/tpu/models"
    (vm)$ export RESNET_CHECKPOINT=gs://cloud-tpu-checkpoints/retinanet/resnet50-checkpoint-2018-02-07
    (vm)$ export TRAIN_FILE_PATTERN=${DATA_DIR}/train-*
    (vm)$ export EVAL_FILE_PATTERN=${DATA_DIR}/val-*
    (vm)$ export VAL_JSON_FILE=${DATA_DIR}/instances_val2017.json
    (vm)$ export ACCELERATOR_TYPE=v3-8
  2. Navigate to the /usr/share directory.

    (vm)$ cd /usr/share
  3. Run the following command to run both the training and evaluation.

    (vm)$ python3 tpu/models/official/mask_rcnn/ \
    --use_tpu=True \
    --tpu=${TPU_NAME} \
    --model_dir=${MODEL_DIR} \
    --num_cores=8 \
    --mode="train_and_eval" \
    --config_file="/usr/share/tpu/models/official/mask_rcnn/configs/cloud/${ACCELERATOR_TYPE}.yaml" \
    --params_override="checkpoint=${RESNET_CHECKPOINT}, training_file_pattern=${TRAIN_FILE_PATTERN}, validation_file_pattern=${EVAL_FILE_PATTERN}, val_json_file=${VAL_JSON_FILE}"

From here, you can either conclude this tutorial and clean up your GCP resources, or you can further explore running the model on a Cloud TPU Pod.

Scaling your model with Cloud TPU Pods

You can get results faster by scaling your model with Cloud TPU Pods. The fully supported Mask RCNN 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

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 Cloud TPU device.

    (vm)$ ctpu delete --project=${PROJECT_ID} \
     --tpu-only \
     --zone=europe-west4-a \
  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 v3-32 Pod slice.

    (vm)$ ctpu up --project=${PROJECT_ID} \
      --tpu-only \
      --tpu-size=v3-32 \
      --zone=europe-west4-a \
      --name=mask-rcnn-pods \
  3. Update the TPU_NAME, MODEL_DIR, and ACCELERATOR_TYPE environment variables.

    (vm)$ export TPU_NAME=mask-rcnn-pods
    (vm)$ export ACCELERATOR_TYPE=v3-32
    (vm)$ export MODEL_DIR=${STORAGE_BUCKET}/mask-rcnn-pods
  4. Start the training script.

    (vm)$ python3 tpu/models/official/mask_rcnn/ \
      --use_tpu=True \
      --tpu=${TPU_NAME} \
      --iterations_per_loop=500 \
      --model_dir=${MODEL_DIR} \
      --num_cores=32 \
      --mode="train" \
      --config_file="/usr/share/tpu/models/official/mask_rcnn/configs/cloud/${ACCELERATOR_TYPE}.yaml" \
      --params_override="checkpoint=${RESNET_CHECKPOINT}, training_file_pattern=${TRAIN_FILE_PATTERN}, validation_file_pattern=${EVAL_FILE_PATTERN}, val_json_file=${VAL_JSON_FILE}"

Evaluating the model

In this step, you use a single Cloud TPU node to evaluate the above trained model against the COCO dataset. The evaluation takes about 10 minutes.

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

    (vm)$ ctpu delete --project=${PROJECT_ID} \
     --tpu-only \
     --zone=europe-west4-a \
  2. Launch a new TPU device to run evaluation.

    (vm)$ ctpu up --project=${PROJECT_ID} \
      --tpu-only \
      --tpu-size=v3-8 \
      --zone=europe-west4-a \
      --name=mask-rcnn-eval \
  3. Update the TPU_NAME environment variable.

    (vm)$ export TPU_NAME=mask-rcnn-eval
  4. Start the evaluation.

    (vm)$ python3 tpu/models/official/mask_rcnn/ \
      --use_tpu=True \
      --tpu=${TPU_NAME} \
      --iterations_per_loop=500 \
      --mode=eval \
      --model_dir=${MODEL_DIR} \
      --config_file="/usr/share/tpu/models/official/mask_rcnn/configs/cloud/${ACCELERATOR_TYPE}.yaml" \
      --params_override="checkpoint=${CHECKPOINT},training_file_pattern=${PATH_GCS_MASKRCNN}/train-*,val_json_file=${PATH_GCS_MASKRCNN}/instances_val2017.json,validation_file_pattern=${PATH_GCS_MASKRCNN}/val-*,init_learning_rate=0.28,learning_rate_levels=[0.028, 0.0028, 0.00028],learning_rate_steps=[6000, 8000, 10000],momentum=0.95,num_batch_norm_group=1,num_steps_per_eval=500,global_gradient_clip_ratio=0.02,total_steps=11250,train_batch_size=512,warmup_steps=1864"

Cleaning up

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

Clean up the Compute Engine VM instance and Cloud TPU 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 Cloud TPU to delete your Compute Engine VM and your Cloud TPU:

    $ ctpu delete --project=${PROJECT_ID} \
      --name=mask-rcnn-tutorial \
  3. Run the following command to verify the Compute Engine VM and Cloud TPU have been shut down:

    $ ctpu status --project=${PROJECT_ID} \

    The deletion might 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:             --
  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

In this tutorial you have trained the Mask-RCNN 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.


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.