Run RawKNNClassifier._predict_fc as parallel to avoid memory issues#9
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grovduck wants to merge 5 commits into
Open
Run RawKNNClassifier._predict_fc as parallel to avoid memory issues#9grovduck wants to merge 5 commits into
RawKNNClassifier._predict_fc as parallel to avoid memory issues#9grovduck wants to merge 5 commits into
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Closes #8 by creating a
joblib.Paralleljob to retrieve predictions for a feature collection. Options are provided for specifying the size of the batch (chunk_size) and the number of threads to use (num_threads). Once all neighbors are retrieved, the result is stitched back together into anee.FeatureCollection.Note that this is a way to do this server-side, but that may not be the best workflow for this use case. Typically, one wants to run the feature collection mode to do cross-validation on the plots used to fit the model or run a new set of targets. We are investigating the possibility of: 1) converting the feature collection client-side; and 2) using
sknnrto run the prediction locally.We will keep this PR open as we decide on the best path forward.