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.
AI Platform Prediction is available in the following regions:
|Salt Lake City
Google Cloud also provides additional regions for products other than AI Platform Prediction.
Demand is high for GPUs and for compute resources in the
You may get an error message in your job logs that says:
insufficient in region: <region>. Please try a different region..
To resolve this, try using a different region or try again later.
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.
When you deploy a model for online prediction, you specify the region that you want prediction to run in. Online predictions are always served from the default region specified for the model.
Compute Engine (N1) machine types for online prediction (beta) are only available in the
Using GPUs for online prediction (beta) is only available in the
You can only deploy models and model versions in the following regions:
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.