Google Cloud uses regions, subdivided into zones, to define the geographic location of physical computing resources. When you run a job on AI Platform Training, 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 Training is available in the following regions:
Americas
- Oregon (us-west1)
- Los Angeles (us-west2)
- Salt Lake City (us-west3)
- Iowa (us-central1)
- South Carolina (us-east1)
- N. Virginia (us-east4)
- Montréal (northamerica-northeast1)
- São Paulo (southamerica-east1)
Europe
- London (europe-west2)
- Belgium (europe-west1)
- Netherlands (europe-west4)
- Zurich (europe-west6)
- Frankfurt (europe-west3)
- Finland (europe-north1)
Asia Pacific
- Mumbai (asia-south1)
- Singapore (asia-southeast1)
- Hong Kong (asia-east2)
- Taiwan (asia-east1)
- Tokyo (asia-northeast1)
- Osaka (asia-northeast2)
- Sydney (australia-southeast1)
- Seoul (asia-northeast3)
Google Cloud also provides additional regions for products other than AI Platform Training.
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 |
Salt Lake City us-west3 |
Iowa us-central1 |
South Carolina us-east1 |
N. Virginia us-east4 |
Montréal northamerica-northeast1 |
São Paulo southamerica-east1 |
|---|---|---|---|---|---|---|---|---|
| NVIDIA A100 | ||||||||
| NVIDIA Tesla P4 | ||||||||
| NVIDIA Tesla P100 | ||||||||
| NVIDIA Tesla T4 | ||||||||
| NVIDIA Tesla V100 | ||||||||
| TPU v2 | ||||||||
| TPU v3 (Beta) | ||||||||
| TPU v2 Pods (Preview) | ||||||||
| TPU v3 Pods (Preview) |
Europe
| Region | London europe-west2 |
Belgium europe-west1 |
Netherlands europe-west4 |
Zurich europe-west6 |
Frankfurt europe-west3 |
Finland europe-north1 |
|---|---|---|---|---|---|---|
| NVIDIA A100 | ||||||
| NVIDIA Tesla P4 | ||||||
| NVIDIA Tesla P100 | ||||||
| NVIDIA Tesla T4 | ||||||
| NVIDIA Tesla V100 | ||||||
| TPU v2 | ||||||
| TPU v3 (Beta) | ||||||
| TPU v2 Pods (Preview) | ||||||
| TPU v3 Pods (Preview) |
Asia Pacific
| Region | Mumbai asia-south1 |
Singapore asia-southeast1 |
Hong Kong asia-east2 |
Taiwan asia-east1 |
Tokyo asia-northeast1 |
Osaka asia-northeast2 |
Sydney australia-southeast1 |
Seoul asia-northeast3 |
|---|---|---|---|---|---|---|---|---|
| NVIDIA A100 | ||||||||
| NVIDIA Tesla P4 | ||||||||
| NVIDIA Tesla P100 | ||||||||
| NVIDIA Tesla T4 | ||||||||
| NVIDIA Tesla V100 | ||||||||
| TPU v2 | ||||||||
| TPU v3 (Beta) | ||||||||
| TPU v2 Pods (Preview) | ||||||||
| TPU v3 Pods (Preview) |
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 T4 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 Training 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 Training job.
Restricting resource locations
Organization policy administrators can restrict the regions available for training jobs by creating a resource locations constraint. Read about how a resource locations constraint applies to AI Platform Training