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, 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 is available in the following regions:

Americas

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

Europe

Region Belgium
europe-west1
Netherlands
europe-west4
Finland
europe-north1
Training
Online prediction
Batch prediction * *

Asia Pacific

Region Singapore
asia-southeast1
Taiwan
asia-east1
Tokyo
asia-northeast1
Training
Online prediction
Batch prediction * *

Region considerations

Training with accelerators

Accelerators are available on a region basis. Below is a table that lists all the available accelerators for each region:

Americas

Region Oregon
us-west1
Los Angeles
us-west2
Iowa
us-central1
South Carolina
us-east1
N. Virginia
us-east4
NVIDIA Tesla K80
NVIDIA Tesla P4
NVIDIA Tesla P100
NVIDIA Tesla T4
NVIDIA Tesla V100
TPU v2
TPU v3 (Beta)

Europe

Region Belgium
europe-west1
Netherlands
europe-west4
NVIDIA Tesla K80
NVIDIA Tesla P4
NVIDIA Tesla P100
NVIDIA Tesla T4
NVIDIA Tesla V100
TPU v2
TPU v3 (Beta)

Asia Pacific

Region Singapore
asia-southeast1
Taiwan
asia-east1
NVIDIA Tesla K80
NVIDIA Tesla P4
NVIDIA Tesla P100
NVIDIA Tesla T4
NVIDIA Tesla V100
TPU v2
TPU v3 (Beta)

If your training job uses multiple types of GPUs, they must all be available in a single zone in your region. For example, you cannot run a job in us-central1 with a master worker using NVIDIA Tesla V100 GPUs, parameter servers using NVIDIA Tesla K80 GPUs, and workers using NVIDIA Tesla P100 GPUs. While all of these GPUs are available for training jobs in us-central1, no single zone in that region provides all three types of GPU. To learn more about the zone availability of GPUs, see the comparison of GPUs for compute workloads.

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 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 job.

Online prediction

Batch prediction

  • You cannot deploy a model or model version in us-west1, us-west2, europe-west4, europe-north1, asia-east1, or asia-southeast1, but you can perform batch prediction in these regions using 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.

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