Regions

Google Cloud uses regions, subdivided into zones, to define the geographic location of physical computing resources. When you run a job on AI Platform Prediction, 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

AI Platform Prediction is available in the following regions:

Americas

Region Oregon
us-west1
Los Angeles
us-west2
Salt Lake City
us-west3
Iowa
us-central1
South Carolina
us-east1
N. Virginia
us-east4
Online prediction
Batch prediction * * *

Europe

Region London
europe-west2
Belgium
europe-west1
Netherlands
europe-west4
Zurich
europe-west6
Frankfurt
europe-west3
Finland
europe-north1
Online prediction
Batch prediction * * * * *

Asia Pacific

Region Mumbai
asia-south1
Singapore
asia-southeast1
Hong Kong
asia-east2
Taiwan
asia-east1
Tokyo
asia-northeast1
Osaka
asia-northeast2
Seoul
asia-northeast3
Online prediction
Batch prediction * * * * * *

Google Cloud also provides additional regions for products other than AI Platform Prediction.

Region considerations

Insufficient resources

Demand is high for GPUs and for compute resources in the us-central1 region. You may get an error message in your job logs that says: Resources are insufficient in region: <region>. Please try a different region..

To resolve this, try using a different region or try again later.

Cloud Storage

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

  • You should use the Standard Storage class for any Cloud Storage buckets that you're using to read and write data for your AI Platform Prediction job.

Online prediction

Batch prediction

  • You can only deploy models and model versions in the following regions:

    • us-central1
    • us-east1
    • us-east4
    • europe-west1
    • asia-northeast1

    To perform batch prediction in other available regions, which are marked with asterisks in the Available regions table, you must use a TensorFlow SavedModel stored in Cloud Storage.

  • 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.