Hey all - I'm running into some memory issues with GridSearchCV and I
wonder if anyone can give an intuition as to why.
I'm cross-validating alpha parameters for Ridge regression. I'm trying 8
different parameters. My inputs are 2400x1900 (~370MB) in size.
When I run
%memit model.fit(X, y)
alone, then I get a peak memory usage of ~370, just double the size of
inputs I gave the function.
However, when I run
grid = GridSearchCV(model, {'alpha':[list of 6 values]}, cv=5)
> grid.fit(X, y)
I get a whopping 8120MB increment in memory.
Is this normal behavior? It seems that keeping n_jobs == 1 should prevent
any data copying from happening, but it clearly something is being copied
many many times.
Any idea what's going on?
Chris
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