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Is your feature request related to a problem? Please describe.
I am sampling each layer of a tree consecutively and then downsampling to a every complete iteration of the tree. The issue I am facing is that to take a decent number of samples I first need to store each vertex and every sample for it before downsampling. This uses a lot of memory and once I hit about 80GB memory pressure rises and the system becomes unusable.
Describe the solution you'd like
I would like to be able to downsample as we go. E.g Take around 140 samples (one sample for each tree layer), then drop all but the last one and repeat for the desired sample count. One option would be to add downsampling as an optional parameter to the getPosteriorSamples method.
Describe alternatives you've considered
Other possible solutions would be to allow users to create their own drop selector and pass it in, or to use streams to filter in real time (although this might be more complex).
The text was updated successfully, but these errors were encountered:
Is your feature request related to a problem? Please describe.
I am sampling each layer of a tree consecutively and then downsampling to a every complete iteration of the tree. The issue I am facing is that to take a decent number of samples I first need to store each vertex and every sample for it before downsampling. This uses a lot of memory and once I hit about 80GB memory pressure rises and the system becomes unusable.
Describe the solution you'd like
I would like to be able to downsample as we go. E.g Take around 140 samples (one sample for each tree layer), then drop all but the last one and repeat for the desired sample count. One option would be to add downsampling as an optional parameter to the getPosteriorSamples method.
Describe alternatives you've considered
Other possible solutions would be to allow users to create their own drop selector and pass it in, or to use streams to filter in real time (although this might be more complex).
The text was updated successfully, but these errors were encountered: