The AmoebaNet-D model is one of the image classifier architectures discovered using Evolutionary AutoML. The model is based on results from the AmoebaNet paper: Real, E., Aggarwal, A., Huang, Y. and Le, Q.V., 2018, Regularized Evolution for Image Classifier Architecture Search, arXiv preprint arXiv:1802.01548.
This model uses TPUEstimator —a high-level TensorFlow API—which is the recommended way to build and run a machine learning model on a Cloud TPU.
The API simplifies the model development process by hiding most of the low-level implementation, making it easier to switch between TPU and other platforms such as GPU or CPU.
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.
-
Sign in to your Google Account.
If you don't already have one, sign up for a new account.
-
In the Google Cloud Console, on the project selector page, select or create a Google Cloud project.
-
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.
Open a Cloud Shell window.
Create a variable for your project's ID.
export PROJECT_ID=project-id
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. ClickAuthorize
at the bottom of the page to allowgcloud
to make GCP API calls with your credentials.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:
service-PROJECT_NUMBER@cloud-tpu.iam.gserviceaccount.com
Create a Cloud Storage bucket using the following command:
gsutil mb -p ${PROJECT_ID} -c standard -l europe-west4 -b on gs://bucket-name
This Cloud Storage bucket stores the data you use to train your model and the training results. The
ctpu up
tool used in this tutorial sets up default permissions for the Cloud TPU Service Account. If you want finer-grain permissions, review the access level permissions.The bucket location must be in the same region as your virtual machine (VM) and your TPU node. VMs and TPU nodes are located in specific zones, which are subdivisions within a region.
Launch the Compute Engine and Cloud TPU resources required for this using the
ctpu up
command.ctpu up --project=${PROJECT_ID} \ --zone=europe-west4-a \ --vm-only \ --disk-size-gb=300 \ --machine-type=n1-standard-8 \ --tf-version=1.15.5 \ --name=amoebanet-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.
When prompted, press y to create your Cloud TPU resources.
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. If you
are not connected to the Compute Engine instance, you can do so by running the
following command:
gcloud compute ssh amoebanet-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
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 AmoebaNet-D 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 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.
In the following steps, a prefix of (vm)$
means you should run the command
on your Compute Engine VM.
Launch a Cloud TPU resource using the ctpu utility.
(vm)$ ctpu up --project=${PROJECT_ID} \ --tpu-only \ --name=amoebanet-tutorial \ --tf-version=1.15.5
Set up the following environment variables, replacing bucket-name with the name of your Cloud Storage bucket:
(vm)$ export STORAGE_BUCKET=gs://bucket-name
(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/amoebanet (vm)$ export DATA_DIR=gs://cloud-tpu-test-datasets/fake_imagenet (vm)$ export PYTHONPATH="${PYTHONPATH}:/usr/share/tpu/models"
(vm)$ export TPU_NAME=amoebanet-tutorial
Navigate to the directory:
(vm)$ cd /usr/share/tpu/models/official/amoeba_net/
Run the training script.
(vm)$ python3 amoeba_net.py \ --tpu=${TPU_NAME} \ --data_dir=${DATA_DIR} \ --model_dir=${MODEL_DIR} \ --num_epochs=1
Parameter Description tpu
Specifies the name of the Cloud TPU. Note that ctpu
passes this name to the Compute Engine VM as an environment variable (TPU_NAME
).data_dir
Specifies the Cloud Storage path for training input. It is set to the fake_imagenet dataset in this example. model_dir
Specifies the directory where checkpoints and summaries are stored during model training. If the folder is missing, the program creates one. When using a Cloud TPU, the model_dir
must be a Cloud Storage path (`gs://...`). You can reuse an existing folder to load current checkpoint data and to store additional checkpoints as long as the previous checkpoints were created using TPU of the same size and TensorFlow version.
For a single Cloud TPU device, this procedure trains the AmoebaNet-D model for 1 epoch and evaluates every fixed number of steps. Using the specified flags, the model should train in about 30 minutes on a v3-8 Cloud TPU. To train to convergence takes about 90 epochs and approximately 10 hours.
Since the training and evaluation was done on the fake_imagenet dataset, the output results do not reflect actual output that would appear if the training and evaluation was performed on a real dataset.
At this point, you can either conclude this tutorial and clean up your GCP resources, or you can further explore running the model on Cloud TPU Pods.
Scaling your model with Cloud TPU Pods
You can get results faster by scaling your model with Cloud TPU Pods. The fully supported AmoebaNet model can work with the following Pod slices:
- v2-32
- v3-32
When working with Cloud TPU Pods, you first train the model using a Pod, then use a single Cloud TPU device to evaluate the model.
Training with Cloud TPU Pods
Delete the Cloud TPU resource you created for training the model on a single device.
(vm)$ ctpu delete --project=${PROJECT_ID} \ --tpu-only \ --name=amoebanet-tutorial \ --zone=europe-west4-a
Run the
ctpu up
command, using thetpu-size
parameter to specify the Pod slice you want to use. For example, the following command uses a v2-32 Pod slice.(vm)$ ctpu up --project=${PROJECT_ID} \ --tpu-only \ --tf-version=1.15.5 \ --tpu-size=v2-32 \ --name=amoebanet-tutorial-pod \ --zone=europe-west4-a
Update the
MODEL_BUCKET
directory to store the Pod training data.(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/amoebanet-tutorial-pod (vm)$ export TPU_NAME=amoebanet-tutorial-pod
Train the model on your Compute Engine VM with the following flags:
(vm)$ python3 amoeba_net.py \ --tpu=${TPU_NAME} \ --data_dir=${DATA_DIR} \ --model_dir=${MODEL_DIR} \ --num_cells=6 \ --image_size=224 \ --num_epochs=1 \ --train_batch_size=4096 \ --eval_batch_size=1000 \ --lr=10.24 \ --lr_decay_value=0.88 \ --num_shards=32 \ --lr_warmup_epochs=0.35 \ --mode=train \ --iterations_per_loop=1000
Parameter Description tpu
Specifies the name of the Cloud TPU. Note that ctpu
passes this name to the Compute Engine VM as an environment variable (TPU_NAME
).data_dir
Specifies the Cloud Storage path for training input. It is set to the fake_imagenet dataset in this example. model_dir
Specifies the directory where checkpoints and summaries are stored during model training. If the folder is missing, the program creates one. When using a Cloud TPU, the `model_dir` must be a Cloud Storage path (`gs://...`). You can reuse an existing folder to load current checkpoint data and to store additional checkpoints as long as the previous checkpoints were created using TPU of the same size and TensorFlow version.
The procedure trains AmoebaNet-D model on the fake_imagnet dataset to 1 epoch. This training takes approximately 10 minutes on a v2-32 Cloud TPU. It will train to convergence in approximately 35 epochs.
Evaluating the model
Evaluation must be run on a single Cloud TPU device so you need to delete the Pod TPU and create a new single Cloud TPU device. You then use this Cloud TPU to evaluate the above trained model against the fake_imagenet validation data.
Delete the Cloud TPU resource you created for training the model on a single device.
(vm)$ ctpu delete --project=${PROJECT_ID} \ --tpu-only \ --name=amoebanet-tutorial-pod \ --zone=europe-west4-a
Run the
ctpu up
command, using thetpu-size
parameter to specify the Cloud TPU you want to use. For example, the following command uses a v3-8 TPU type.(vm)$ ctpu up --project=${PROJECT_ID} \ --tpu-only \ --tf-version=1.15.5 \ --tpu-size=v3-8 \ --name=amoebanet-tutorial-eval \ --zone=europe-west4-a
Update the
MODEL_BUCKET
directory to point to the Pod training data.(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/amoebanet-tutorial-pod (vm)$ export TPU_NAME=amoebanet-tutorial-eval
Run the model evaluation with the following flags:
(vm)$ python3 amoeba_net.py \ --tpu=${TPU_NAME} \ --data_dir=${DATA_DIR} \ --model_dir=${MODEL_DIR} \ --num_cells=6 \ --image_size=224 \ --train_batch_size=4096 \ --eval_batch_size=1000 \ --mode=eval \ --iterations_per_loop=1000
This generates output similar to the following:
Evaluation results: {'loss': 6.908725, 'top_1_accuracy': 0.001, 'global_step': 10955, 'top_5_accuracy': 0.005}
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.
Using larger Cloud TPU Pod slices
You can also try the model using larger Cloud TPU Pod slices. The following table shows the recommended values for these slices.
Parameter | Description | 128 cores | 256 cores |
---|---|---|---|
num_cells |
Total number of cells | 6 | 6 |
image_size |
Size of image, assuming image height and width | 224 | 224 |
num_epochs |
Number of steps use for training | 60 | 90 |
train_batch_size |
Global (not per-shard) batch size for training | 8192 | 16384 |
lr |
Learning rate | 20.48 | 40.96 |
lr_decay_value |
Exponential decay rate used in learning rate adjustment | 0.91 | 0.94 |
num_shards |
Number of shards (TPU cores) | 128 | 256 |
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.
Delete your Cloud TPU:
$ ctpu delete --project=${PROJECT_ID} \ --tpu-only \ --name=amoebanet-tutorial-eval \ --zone=europe-west4-a
Disconnect from the Compute Engine instance, if you have not already done so:
(vm)$ exit
Your prompt should now be
username@projectname
, showing you are in the Cloud Shell.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=${PROJECT_ID} \ --vm-only \ --name=amoebanet-tutorial \ --zone=europe-west4-a
Run
ctpu status
to make sure you have no instances allocated to avoid unnecessary charges for TPU usage. The deletion might take several minutes. A response like the one below indicates there are no more allocated instances:$ ctpu status --project=${PROJECT_ID} \ --zone=europe-west4-a
2018/04/28 16:16:23 WARNING: Setting zone to "europe-west4-a" No instances currently exist. Compute Engine VM: -- Cloud TPU: --
Run
gsutil
as shown, replacing bucket-name with the name of the Cloud Storage bucket you created for this tutorial:$ gsutil rm -r gs://bucket-name
What's next
In this tutorial you have trained the AmoebaNet-D 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.