Locations

Google Cloud uses regions, subdivided into zones, to define the geographic location of physical computing resources. Google stores and processes your data only in the region you specify for all features of AI Platform (Unified) except for data labeling tasks and any feature in experimental status.

Choosing your location

You can choose any supported location when you create a dataset, train a custom-trained model that does not use a managed dataset, or when you import an existing model. You should typically use the region closest to your physical location or the physical location of your intended users, but check that the AI Platform feature you want to use is supported in your region. There is no global location.

For operations other than creating a dataset or importing a model, you must use the location of the resources you are operating on. For example, when you create a training pipeline that uses a managed dataset, you must use the region where the dataset is located.

Specifying the location using Cloud Console

When you use Google Cloud Console, you specify the location by using the location dropdown menu:

locations dropdown menu

Specifying the location using the AI Platform (Unified) API

You specify the location for an AI Platform API request by using the appropriate regional endpoint.

For example, to make a request in the europe-west4 region, use the following endpoint:

https://europe-west4-aiplatform.googleapis.com

To make a request in the us-central1 region, use the following endpoint:

https://us-central1-aiplatform.googleapis.com

When you specify a resource, you use the name of the resource's region as the location. For example, a dataset in the us-central1 region would be specified using the following path:

projects/PROJECT/locations/us-central1/datasets/DATASET_ID

See the following list of supported endpoints:

  • https://us-west1-aiplatform.googleapis.com
  • https://us-central1-aiplatform.googleapis.com
  • https://us-east1-aiplatform.googleapis.com
  • https://us-east4-aiplatform.googleapis.com
  • https://northamerica-northeast1-aiplatform.googleapis.com
  • https://europe-west2-aiplatform.googleapis.com
  • https://europe-west1-aiplatform.googleapis.com
  • https://europe-west4-aiplatform.googleapis.com
  • https://asia-southeast1-aiplatform.googleapis.com
  • https://asia-east1-aiplatform.googleapis.com
  • https://asia-northeast1-aiplatform.googleapis.com
  • https://australia-southeast1-aiplatform.googleapis.com
  • https://asia-northeast3-aiplatform.googleapis.com

See Feature availability for the supported features for each region.

Available regions

AI Platform is available in the following regions:

Americas

  • Oregon (us-west1)
  • Iowa (us-central1)
  • South Carolina (us-east1)
  • N. Virginia (us-east4)
  • Montréal (northamerica-northeast1)

Europe

  • London (europe-west2)
  • Belgium (europe-west1)
  • Netherlands (europe-west4)

Asia Pacific

  • Singapore (asia-southeast1)
  • Taiwan (asia-east1)
  • Tokyo (asia-northeast1)
  • Sydney (australia-southeast1)
  • Seoul (asia-northeast3)

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

Feature availability

Some AI Platform features are not available in all regions. The following table lists the features that are available in each region:

Americas

Region Oregon
us-west1
Iowa
us-central1
South Carolina
us-east1
N. Virginia
us-east4
Montréal
northamerica-northeast1
Creating and using Dataset resources
AutoML for image data (training, online predictions, and batch predictions)
AutoML for tabular data (training, online predictions, and batch predictions)
AutoML for text data (training, online predictions, and batch predictions)
AutoML for video data (training and batch predictions)
Custom model training
Custom model online predictions
Custom model batch predictions
Data Labeling
Vizier

Europe

Region London
europe-west2
Belgium
europe-west1
Netherlands
europe-west4
Creating and using Dataset resources
AutoML for image data (training, online predictions, and batch predictions)
AutoML for tabular data (training, online predictions, and batch predictions)
AutoML for text data (training, online predictions, and batch predictions)
AutoML for video data (training and batch predictions)
Custom model training
Custom model online predictions
Custom model batch predictions
Data Labeling
Vizier

Asia Pacific

Region Singapore
asia-southeast1
Taiwan
asia-east1
Tokyo
asia-northeast1
Sydney
australia-southeast1
Seoul
asia-northeast3
Creating and using Dataset resources
AutoML for image data (training, online predictions, and batch predictions)
AutoML for tabular data (training, online predictions, and batch predictions)
AutoML for text data (training, online predictions, and batch predictions)
AutoML for video data (training and batch predictions)
Custom model training
Custom model online predictions
Custom model batch predictions
Data Labeling
Vizier

Region considerations

Using accelerators

Accelerators are available on a region basis. The following table lists all the available accelerators for each region:

Americas

Region Oregon
us-west1
Iowa
us-central1
South Carolina
us-east1
N. Virginia
us-east4
Montréal
northamerica-northeast1
NVIDIA Tesla K80 *
NVIDIA Tesla P4
NVIDIA Tesla P100
NVIDIA Tesla T4
NVIDIA Tesla V100

Europe

Region London
europe-west2
Belgium
europe-west1
Netherlands
europe-west4
NVIDIA Tesla K80
NVIDIA Tesla P4
NVIDIA Tesla P100
NVIDIA Tesla T4
NVIDIA Tesla V100

Asia Pacific

Region Singapore
asia-southeast1
Taiwan
asia-east1
Tokyo
asia-northeast1
Sydney
australia-southeast1
Seoul
asia-northeast3
NVIDIA Tesla K80
NVIDIA Tesla P4
NVIDIA Tesla P100 *
NVIDIA Tesla T4
NVIDIA Tesla V100 *

* Cells marked with asterisks represent regions where the specified GPU is available for training but not for serving batch or online predictions.

If your 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 using NVIDIA Tesla T4 GPUs, NVIDIA Tesla K80 GPUs, and NVIDIA Tesla P100 GPUs. While all of these GPUs are available for 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.

BigQuery location requirements

When you use a BigQuery table as a source for a managed tabular dataset or tabular prediction data, it must conform to the following location requirements:

Americas

  • BigQuery tables can be either multi-regional (US) or regional (us-central1).

  • BigQuery views must be regional (us-central1).

  • If the table or view is not in the same region that the AI Platform job is running in, make sure that AI Platform has the correct roles.

Europe

  • BigQuery tables and views must be regional (europe-west4).
  • Location: The region that your AI Platform (Unified) job runs in, such as us-central1, europe-west4, or asia-east1.

  • If the table or view is not in the same project that the AI Platform job is running in, make sure that AI Platform has the correct roles.

Cloud Storage bucket requirements

Some AI Platform tasks, such as importing data, use a Cloud Storage bucket.

  • Use the following settings when creating Cloud Storage a bucket to read and write data for your AI Platform job.

    • Location type: Region.
    • Location: The region that your AI Platform job runs in, such as us-central1, europe-west4, or asia-east1.
    • Storage class: Standard (sometimes displayed in the Cloud Storage browser as Regional).
  • If the bucket is not in the same project that the AI Platform job is running in, make sure AI Platform has the correct roles.

Restricting resource locations

Organization policy administrators can restrict the regions available where you can use AI Platform by creating a resource locations constraint. Read about how a resource locations constraint applies to AI Platform

Resource locations constraints don't apply to DataLabelingJob resources.