Introduction

This sample shows how to run hyperparameter tuning jobs on Cloud Machine Learning Engine with Cloud TPUs using the cloudml-hypertune package.

This sample is adapted from the official samples for training ResNet-50 with Cloud TPUs to run on Cloud Machine Learning Engine.

Requirements

Install Google Cloud Platform SDK. The SDK includes the commandline tools gcloud for submitting training jobs to Cloud Machine Learning Engine.
Enable Cloud Storage.
Follow the steps here to authorize Cloud TPU to access your project.

Clone the repository.

git clone https://github.com/GoogleCloudPlatform/cloudml-samples.git

If you do not already have a Cloud Storage bucket, create one to be used for the training job.
```
gsutil mb gs://[YOUR_GCS_BUCKET]
export GCS_BUCKET="gs://[YOUR_GCS_BUCKET]"
```
Run the sample. The included script will train ResNet-50 for 1024 steps using a fake dataset.
```
cd cloudml-samples/tpu/hptuning/resnet-hypertune
bash submit_resnet_hypertune.sh
```