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Hi, thanks for pointing this out. Indeed, all g4dn instances are currently not supported by the forecasting-deepar algorithm, but as you rightly point out, this is currently not documented. I will raise this with the service team to include in in the documentation.
In the meantime, you can try out the P3 instances instead - these are also powerful GPU instances and should help you speed up the training time.
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I appreciate the quick response @Heiko! I see that for training there are 3 P3 instance options available, i.e. - 2xlarge, 8xlarge and 16xlarge. It would be super helpful if you could confirm which of these are configured for deepar.
Additionally, I was hoping you could help me understand how the parameter 'instance_count' in the sagemaker Estimator class affects training time. The way I understand it is that the number attributed to this parameter results in the number of EC2 instances with the specified instance type to be allocated. For example with an instance_count = '3', we would have 3 EC2 instances, each with a p3.2xlarge (for example) launched to parallelize training.
If so, which would you say is better in terms of improving training speed - using a higher instance_count / a single higher compute capacity instance? Thank you!