Questions tagged with AWS Deep Learning Containers
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Which GPU instances are supported by the sagemaker algorithm forecasting-deepar?
I previously ran a hyperparameter tuning job for SageMaker DeepAR with the instance type ml.c5.18xlarge but it seems insufficient to complete the tuning job within the max_run time specified in my account. Now, having tried to use the accelerated GPU instance ml.g4dn.16xlarge, I am prompted with an error - "Instance type ml.g4dn.16xlarge is not supported by algorithm forecasting-deepar." I cannot find any documentation that indicates the list of instance types supported by deepar. What GPU/CPU instances have more compute capacity than ml.c5.18xlarge which I could leverage for my tuning job? If there isn't, I would appreciate any recommendations as to how I could hasten the run time of the job. I require the tuning job to complete within the max run time of 432000 seconds. Thank you in advance!
Setting up data for DeepAR, targets and categories for simultaneous data?
I would like to try out DeepAR for an engineering problem that I have some sensor datasets for, but I am unsure how to set it up for ingestion into DeepAR to get a predictive model. The data is essentially the positions, orientations, and a few other timeseries sensor readings of an assortment of objects (animals, in this case, actually) over time. Data is both noisy and sometimes missing. So, in this case, there are N individuals and for each individual, there are Z variables of interest per individual. None of the variables are "static" (color, size, etc), they are all expected to be time-varying on the same time scale. Ultimately, I would like to try and predict all Z targets for all N individuals. How do I set up the timeseries to feed into DeepAR? The premise is that all these individuals are implicitly interacting in the observed space, so all the target values have some interdependence on each other, which is what I would like to see if DeepAR can take into account to make predictions. Should I be using a category vector of length 2, such that the first cat variable corresponds to the individual, and the second corresponds to one of the variables associated with the individual? Then there would be N*Z targets in my input dataset, each with `cat = [ n , z ]`, where there are N distinct values for n, and z for Z?