The model in this tutorial is based on Deep Residual Learning for Image Recognition, which first introduces the residual network (ResNet) architecture. The tutorial uses the 50-layer variant, ResNet-50, and demonstrates training the model using TPUEstimator. The ResNet-50 model is pre-installed on your Compute Engine VM.
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.
-
Verifica che la fatturazione sia attivata per il tuo progetto.
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.
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 ${PROJECT_ID} -c standard -l europe-west4 -b on gs://bucket-name
This Cloud Storage bucket stores the data you use to train your model and the training results. The
ctpu up
tool used in this tutorial sets up default permissions for the Cloud TPU Service Account. If you want finer-grain permissions, review the access level permissions.The bucket location must be in the same region as your virtual machine (VM) and your TPU node. VMs and TPU nodes are located in specific zones, which are subdivisions within a region.
Launch the Compute Engine resources required for this using the
ctpu up
command.ctpu up --project=${PROJECT_ID} \ --zone=europe-west4-a \ --vm-only \ --name=resnet-tutorial \ --disk-size-gb=300 \ --machine-type=n1-standard-8 \ --tf-version=1.15.5
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 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@project
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 resnet-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.
Configure storage, model, and data paths
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=${STORAGE_BUCKET}/resnet (vm)$ export DATA_DIR=gs://cloud-tpu-test-datasets/fake_imagenet (vm)$ export PYTHONPATH="${PYTHONPATH}:/usr/share/tpu/models"
The training application expects your training data to be accessible in Cloud Storage. The training application uses your Cloud Storage bucket to store checkpoints during training.
Train and evaluate the ResNet model with fake_imagenet
ImageNet is an image database. The images in the database are organized into a hierarchy, each node of the hierarchy contains hundreds and thousands of images.
This tutorial uses a demonstration version of the full ImageNet dataset, referred to as fake_imagenet. This demonstration version allows you to test the tutorial, while reducing the storage and time requirements typically associated with running a model against the full ImageNet database.
The fake_imagenet dataset is at this location on Cloud Storage:
gs://cloud-tpu-test-datasets/fake_imagenet
The fake_imagenet dataset is only useful for understanding how to use a Cloud TPU and validating end-to-end performance. The accuracy numbers and saved model will not be meaningful.
For information on how to download and process the full ImageNet dataset, see Downloading, preprocessing, and uploading the ImageNet dataset.
Launch a Cloud TPU resource using the ctpu utility and set some environment variables used later on.
(vm)$ ctpu up --project=${PROJECT_ID} \ --tpu-only \ --tf-version=1.15.5 \ --name=resnet-tutorial
(vm)$ export TPU_NAME=resnet-tutorial (vm)$ export ACCELERATOR_TYPE=v3-8
Navigate to the model directory:
(vm)$ cd /usr/share/tpu/models/official/resnet/
Run the training script.
For a single Cloud TPU device, the script trains the ResNet-50 model for 90 epochs and evaluates the results after each training step. The number of training steps is set with the
train_steps
flag. Using the script command line below, the model should train in about 15 minutes.Since the training and evaluation is done on the fake_imagenet dataset, the training and evaluation results do not reflect the results that would be generated if training and evaluation was performed on a real dataset.
If you run this script on a real dataset, use the
train_steps
flag to specify the number of training steps. See the.yaml
files in the/usr/share/tpu/models/official/resnet/configs/cloud
directory to get an idea about how many training steps to use.(vm)$ python3 resnet_main.py \ --tpu=${TPU_NAME} \ --data_dir=${DATA_DIR} \ --model_dir=${MODEL_DIR} \ --train_steps=500 \ --config_file=configs/cloud/${ACCELERATOR_TYPE}.yaml
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.config_file
Specifies the YAML configuration file to use during training. The name of this file corresponds to the type of TPU used. For example, v2-8.yaml
.
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
- v2-128
- v2-256
- v2-512
- v3-32
- v3-128
- v3-256
- v3-512
- v3-1024
- v3-2048
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=${PROJECT_ID} \ --tpu-only \ --name=resnet-tutorial
Run the
ctpu up
command, using thetpu-size
parameter to specify the Pod slice you want to use. For example, the following command uses a v2-32 Pod slice.(vm)$ ctpu up --project=${PROJECT_ID} \ --tpu-only \ --tpu-size=v2-32 \ --tf-version=1.15.5 \ --name=resnet-tutorial
Update the
TPU_NAME
andACCELERATOR_TYPE
environment variables to specify a TPU pod name an accelerator type.(vm)$ export TPU_NAME=resnet-tutorial (vm)$ export ACCELERATOR_TYPE=v2-32
Update the
MODEL_DIR
directory to store the training data.(vm)$ export MODEL_DIR=${STORAGE_BUCKET}/resnet-tutorial
Train the model, updating the
config_file
parameter to use the configuration file that corresponds with the Pod slice you want to use. For example, the training script uses thev2-32.yaml
configuration file.The script trains the model on the fake_imagnet dataset to 35 epochs. This takes approximately 90 minutes to run on a v3-128 Cloud TPU.
(vm)$ python3 resnet_main.py \ --tpu=${TPU_NAME} \ --data_dir=${DATA_DIR} \ --model_dir=${MODEL_DIR} \ --train_steps=500 \ --mode=train \ --config_file=configs/cloud/${ACCELERATOR_TYPE}.yaml
Evaluating the model
In this step, you use Cloud TPU to evaluate the above trained model against the fake_imagenet validation data.
Delete the Cloud TPU resource you created to train the model.
(vm)$ ctpu delete --project=${PROJECT_ID} \ --tpu-only \ --name=resnet-tutorial
Start a v2-8 Cloud TPU.
(vm)$ ctpu up --project=${PROJECT_ID} \ --tpu-only \ --tf-version=1.15.5 \ --name=resnet-eval
Update the
TPU_NAME
environment variable.(vm)$ export TPU_NAME=resnet-eval
Run the model evaluation. This time, add the
mode
flag and set it toeval
.(vm)$ python3 resnet_main.py \ --tpu=${TPU_NAME} \ --data_dir=${DATA_DIR} \ --model_dir=${MODEL_DIR} \ --mode=eval \ --config_file=configs/cloud/${ACCELERATOR_TYPE}.yaml
This generates output similar to the following:
Eval results: {'loss': 8.255788, 'top_1_accuracy': 0.0009969076, 'global_step': 0, 'top_5_accuracy': 0.005126953}. Elapsed seconds: 76
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.
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.
Disconnect from the Compute Engine instance, if you have not already done so:
(vm)$ exit
Your prompt should now be
username@project
, 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=${PROJECT_ID} \ --zone=europe-west4-a
Run
ctpu status
to make sure you have no instances allocated to avoid unnecessary charges for TPU usage. The deletion might take several minutes. A response like the one below indicates there are no more allocated instances:$ ctpu status --project=${PROJECT_ID}} \ --name=resnet-tutorial \ --zone=europe-west4-a
2018/04/28 16:16:23 WARNING: Setting zone to "europe-west4-a" No instances currently exist. Compute Engine VM: -- Cloud TPU: --
Run
gsutil
as shown, replacing bucket-name with the name of the Cloud Storage bucket you created for this tutorial:$ gsutil rm -r gs://bucket-name
What's next
In this tutorial you have trained the 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 more about
ctpu
, including how to install it on a local machine. - Explore the TPU tools in TensorBoard.
- See how to train ResNet with Cloud TPU and GKE.
- Walk through the tutorial for the RetinaNet object detection model.
- Run the TensorFlow SqueezeNet model on Cloud TPU, using the above instructions as your starting point. The model architectures for SqueezeNet and ResNet-50 are similar. You can use the same data and the same command-line flags to train the model.