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 Vertex AI except for data labeling tasks and any feature in experimental or preview launch 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 Vertex AI 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 Google Cloud console
When you use Google Cloud console, you specify the location by using the location dropdown menu:
Specifying the location using the Vertex AI API
You specify the location for a Vertex AI 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 list of supported service endpoints.
Available locations
Vertex AI regions
Vertex AI is available in the following regions. See also Vertex AI Workbench locations.
Americas
- Oregon (us-west1)
- Los Angeles (us-west2)
- Salt Lake City (us-west3)
- Las Vegas (us-west4)
- Iowa (us-central1)
- South Carolina (us-east1)
- N. Virginia (us-east4)
- Dallas (us-south1)
- Montréal (northamerica-northeast1)
- Toronto (northamerica-northeast2)
- São Paulo (southamerica-east1)
Europe
- London (europe-west2)
- Belgium (europe-west1)
- Netherlands (europe-west4)
- Zurich (europe-west6)
- Frankfurt (europe-west3)
- Warsaw (europe-central2)
- Paris (europe-west9)
Asia Pacific
- Mumbai (asia-south1)
- Singapore (asia-southeast1)
- Jakarta (asia-southeast2)
- Hong Kong (asia-east2)
- Taiwan (asia-east1)
- Tokyo (asia-northeast1)
- Sydney (australia-southeast1)
- Seoul (asia-northeast3)
Middle East
- Tel Aviv (me-west1)
Google Cloud also provides additional regions for products other than Vertex AI.
Feature availability
Some Vertex AI features are not available in all regions. The following table lists the features that are available in each region.
Americas
Region | Oregon us-west1 |
Los Angeles us-west2 |
Salt Lake City us-west3 |
Las Vegas us-west4 |
Iowa us-central1 |
South Carolina us-east1 |
N. Virginia us-east4 |
Dallas us-south1 |
Montréal northamerica-northeast1 |
Toronto northamerica-northeast2 |
São Paulo southamerica-east1 |
---|---|---|---|---|---|---|---|---|---|---|---|
AutoML for image data (training, online predictions, and batch predictions) | |||||||||||
AutoML for tabular data, classification and regression objectives (training, online and batch predictions, and explanations) | |||||||||||
AutoML for tabular data, forecasting objective (training, online and batch predictions, and explanations) | |||||||||||
AutoML for text data (training, online predictions, and batch predictions) | |||||||||||
AutoML for video data (training and batch predictions) | |||||||||||
Custom model training | |||||||||||
Interactive shell for custom training | |||||||||||
Custom model online predictions and explanations | |||||||||||
Custom model batch predictions and explanations | |||||||||||
Data Labeling | |||||||||||
Vertex AI Vizier | |||||||||||
Vertex AI Pipelines | |||||||||||
Vertex ML Metadata | |||||||||||
Vertex AI Experiments | |||||||||||
Vertex AI Feature Store | |||||||||||
Vertex AI Model Monitoring | |||||||||||
Vertex AI Matching Engine | |||||||||||
Vertex AI TensorBoard |
Europe
Region | London europe-west2 |
Belgium europe-west1 |
Netherlands europe-west4 |
Zurich europe-west6 |
Frankfurt europe-west3 |
Warsaw europe-central2 |
Paris europe-west9 |
---|---|---|---|---|---|---|---|
AutoML for image data (training, online predictions, and batch predictions) | |||||||
AutoML for tabular data, classification and regression objectives (training, online and batch predictions, and explanations) | |||||||
AutoML for tabular data, forecasting objective (training, online and batch predictions, and explanations) | |||||||
AutoML for text data (training, online predictions, and batch predictions) | |||||||
AutoML for video data (training and batch predictions) | |||||||
Custom model training | |||||||
Interactive shell for custom training | |||||||
Custom model online predictions and explanations | |||||||
Custom model batch predictions and explanations | |||||||
Data Labeling | |||||||
Vertex AI Vizier | |||||||
Vertex AI Pipelines | |||||||
Vertex ML Metadata | |||||||
Vertex AI Experiments | |||||||
Vertex AI Feature Store | |||||||
Vertex AI Model Monitoring | |||||||
Vertex AI Matching Engine | |||||||
Vertex AI TensorBoard |
Asia Pacific
Region | Mumbai asia-south1 |
Singapore asia-southeast1 |
Jakarta asia-southeast2 |
Hong Kong asia-east2 |
Taiwan asia-east1 |
Tokyo asia-northeast1 |
Sydney australia-southeast1 |
Seoul asia-northeast3 |
---|---|---|---|---|---|---|---|---|
AutoML for image data (training, online predictions, and batch predictions) | ||||||||
AutoML for tabular data, classification and regression objectives (training, online and batch predictions, and explanations) | ||||||||
AutoML for tabular data, forecasting objective (training, online and batch predictions, and explanations) | ||||||||
AutoML for text data (training, online predictions, and batch predictions) | ||||||||
AutoML for video data (training and batch predictions) | ||||||||
Custom model training | ||||||||
Interactive shell for custom training | ||||||||
Custom model online predictions and explanations | ||||||||
Custom model batch predictions and explanations | ||||||||
Data Labeling | ||||||||
Vertex AI Vizier | ||||||||
Vertex AI Pipelines | ||||||||
Vertex ML Metadata | ||||||||
Vertex AI Experiments | ||||||||
Vertex AI Feature Store | ||||||||
Vertex AI Model Monitoring | ||||||||
Vertex AI Matching Engine | ||||||||
Vertex AI TensorBoard |
Middle East
Region | Tel Aviv me-west1 |
---|---|
AutoML for image data (training, online predictions, and batch predictions) | |
AutoML for tabular data, classification and regression objectives (training, online and batch predictions, and explanations) | |
AutoML for tabular data, forecasting objective (training, online and batch predictions, and explanations) | |
AutoML for text data (training, online predictions, and batch predictions) | |
AutoML for video data (training and batch predictions) | |
Custom model training | |
Interactive shell for custom training | |
Custom model online predictions and explanations | |
Custom model batch predictions and explanations | |
Data Labeling | |
Vertex AI Vizier | |
Vertex AI Pipelines | |
Vertex ML Metadata | |
Vertex AI Experiments | |
Vertex AI Feature Store | |
Vertex AI Model Monitoring | |
Vertex AI Matching Engine | |
Vertex AI TensorBoard |
Vertex AI Workbench locations
Managed notebooks regions
Managed notebooks are available in the following regions.
Region description | Zone name | |
---|---|---|
Americas | ||
Oregon | us-west1 |
|
Las Vegas | us-west4 |
|
Iowa | us-central1 |
|
Montréal | northamerica-northeast1 |
|
São Paulo | southamerica-east1 |
|
Europe | ||
Belgium | europe-west1 |
|
Netherlands | europe-west4 |
|
Asia Pacific | ||
Mumbai | asia-south1 |
|
Singapore | asia-southeast1 |
|
Hong Kong | asia-east2 |
|
Tokyo | asia-northeast1 |
|
Sydney | australia-southeast1 |
|
Seoul | asia-northeast3 |
User-managed notebooks locations
User-managed notebooks are available in the following zones.
Region description | Zone name | |
---|---|---|
Americas | ||
Oregon | us-west1-a us-west1-b us-west1-c
|
|
Los Angeles | us-west2-a us-west2-b us-west2-c
|
|
Las Vegas | us-west4-a us-west4-b us-west4-c
|
|
Iowa | us-central1-a us-central1-b us-central1-c
|
|
South Carolina | us-east1-b us-east1-c us-east1-d
|
|
Northern Virginia | us-east4-a us-east4-b us-east4-c
|
|
Montréal | northamerica-northeast1-a northamerica-northeast1-b northamerica-northeast1-c
|
|
São Paulo | southamerica-east1-a southamerica-east1-b southamerica-east1-c
|
|
Europe | ||
London | europe-west2-a europe-west2-b europe-west2-c
|
|
Belgium | europe-west1-b europe-west1-c europe-west1-d
|
|
Netherlands | europe-west4-a europe-west4-b europe-west4-c
|
|
Zürich | europe-west6-a europe-west6-b europe-west6-c
|
|
Frankfurt | europe-west3-a europe-west3-b europe-west3-c
|
|
Asia Pacific | ||
Mumbai | asia-south1-a asia-south1-b asia-south1-c
|
|
Singapore | asia-southeast1-a asia-southeast1-b asia-southeast1-c
|
|
Jakarta | asia-southeast2-a asia-southeast2-b asia-southeast2-c
|
|
Hong Kong | asia-east2-a asia-east2-b asia-east2-c
|
|
Taiwan | asia-east1-a asia-east1-b asia-east1-c
|
|
Tokyo | asia-northeast1-a asia-northeast1-b asia-northeast1-c
|
|
Sydney | australia-southeast1-a australia-southeast1-b australia-southeast1-c
|
|
Seoul | asia-northeast3-a asia-northeast3-b asia-northeast3-c
|
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 |
Los Angeles us-west2 |
Salt Lake City us-west3 |
Las Vegas us-west4 |
Iowa us-central1 |
South Carolina us-east1 |
N. Virginia us-east4 |
Dallas us-south1 |
Montréal northamerica-northeast1 |
Toronto northamerica-northeast2 |
São Paulo southamerica-east1 |
---|---|---|---|---|---|---|---|---|---|---|---|
NVIDIA A100 | |||||||||||
NVIDIA Tesla K80 | |||||||||||
NVIDIA Tesla P4 | |||||||||||
NVIDIA Tesla P100 | |||||||||||
NVIDIA Tesla T4 | |||||||||||
NVIDIA Tesla V100 | * | ||||||||||
TPU V2 | * | ||||||||||
TPU V2 Pod | * | ||||||||||
TPU V3 | * | * | |||||||||
TPU V3 Pod |
Europe
Region | London europe-west2 |
Belgium europe-west1 |
Netherlands europe-west4 |
Zurich europe-west6 |
Frankfurt europe-west3 |
Warsaw europe-central2 |
Paris europe-west9 |
---|---|---|---|---|---|---|---|
NVIDIA A100 | |||||||
NVIDIA Tesla K80 | |||||||
NVIDIA Tesla P4 | |||||||
NVIDIA Tesla P100 | |||||||
NVIDIA Tesla T4 | * | ||||||
NVIDIA Tesla V100 | * | ||||||
TPU V2 | * | ||||||
TPU V2 Pod | * | ||||||
TPU V3 | * | ||||||
TPU V3 Pod | * |
Asia Pacific
Region | Mumbai asia-south1 |
Singapore asia-southeast1 |
Jakarta asia-southeast2 |
Hong Kong asia-east2 |
Taiwan asia-east1 |
Tokyo asia-northeast1 |
Sydney australia-southeast1 |
Seoul asia-northeast3 |
---|---|---|---|---|---|---|---|---|
NVIDIA A100 | † | |||||||
NVIDIA Tesla K80 | ||||||||
NVIDIA Tesla P4 | ||||||||
NVIDIA Tesla P100 | ||||||||
NVIDIA Tesla T4 | ||||||||
NVIDIA Tesla V100 | * | * | * | * | ||||
TPU V2 | * | |||||||
TPU V2 Pod | ||||||||
TPU V3 | ||||||||
TPU V3 Pod |
Middle East
Region | Tel Aviv me-west1 |
---|---|
NVIDIA A100 | |
NVIDIA Tesla K80 | |
NVIDIA Tesla P4 | |
NVIDIA Tesla P100 | |
NVIDIA Tesla T4 | |
NVIDIA Tesla V100 | |
TPU V2 | |
TPU V2 Pod | |
TPU V3 | |
TPU V3 Pod |
* Cells marked with asterisks represent regions where the specified accelerator is available for training but not for serving batch or online predictions.
† Cells marked with daggers represent regions where the specified accelerator is available for serving batch or online predictions but not for training.
If your job uses multiple types of GPUs, they must all be available in a single
zone in your region. For example, you can't 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 project that the Vertex AI job is running in, make sure that Vertex AI has the correct roles.
Europe
BigQuery tables and views must be regional (
europe-west4
).Location: The region that your Vertex AI job runs in, such as
us-central1
,europe-west4
, orasia-east1
.If the table or view is not in the same project that the Vertex AI job is running in, make sure that Vertex AI has the correct roles.
Cloud Storage bucket requirements
Some Vertex AI tasks, such as importing data, use a Cloud Storage bucket.
We recommend that you use the following settings when creating a Cloud Storage bucket to use with Vertex AI:
- Location type:
Region
. - Location: The region where you are using Vertex AI; for
example,
us-central1
,europe-west4
, orasia-east1
. - Storage class:
Standard
.
These settings are not strict requirements, but using these settings often improves performance. For example, it's possible to use a bucket in a multi-region with Vertex AI, but loading data from a bucket in the same region as your Vertex AI resource might reduce latency.
- Location type:
If the bucket is not in the same project that the Vertex AI job is running in, make sure Vertex AI has the correct roles.
Restricting resource locations
Organization policy administrators can restrict the regions available where you can use Vertex AI by creating a resource locations constraint. Read about how a resource locations constraint applies to Vertex AI
Resource locations constraints don't apply to
DataLabelingJob
resources.