Regions

Google Cloud Platform uses regions, subdivided into zones, to define the geographic location of physical computing resources. When you run a job on Cloud ML Engine, you specify the region that you want it to run in.

You should typically use the region closest to your physical location or the physical location of your intended users, but note the available regions for each service as listed below.

Available regions

Cloud ML Engine services for TensorFlow are available in the following regions:

US

Region us-west1 us-central1 us-east1 us-east4 southamerica-east1
Training
Training with GPUs
Online prediction
Batch prediction

Europe

Region europe-west2 europe-west1 europe-west3
Training
Training with GPUs
Online prediction
Batch prediction

Asia Pacific

Region asia-south1 asia-southeast1 asia-east1 asia-northeast1 australia-southeast1
Training
Training with GPUs
Online prediction
Batch prediction

Region considerations

Cloud Storage

  • You should run your Cloud ML Engine job in the same region as the Cloud Storage bucket that you're using to read and write data for the job.

  • You must use the regional storage type for any Cloud Storage buckets that you're using to read and write data for your Cloud ML Engine job.

Online prediction

  • When you deploy a model for online prediction, you specify the region that you want prediction to run in. Online predictions are always served from the default region specified for the model.

Batch prediction

  • For best performance in batch prediction, you should run your prediction job and store your input and output data in the same region, especially for very large datasets.
  • When you deploy a model for batch prediction, you specify the default region that you want prediction to run in. When you start a batch prediction job, you can specify a region to run the job in, overriding the default region.

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Cloud ML Engine for TensorFlow