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No memory leak
A huge amount of memory that prevents the optimization process
Create the folling class
class MyMemoryDumper(object): def __init__(self): self.every = 2000 self.count = 0 def __call__(self, res): if self.count != 0 and self.count % self.every == 0: obj_list = gc.get_objects() new_values = {} for obj in obj_list: size_obj = sys.getsizeof(obj) new_values[str(obj)] = size_obj new_values = sorted(new_values) print('end') self.count += 1
Add it to base_estimator
base_estimator
forest_minimize( base_estimator=ExtraTreesRegressor(n_estimators=20, min_samples_leaf=2), callback=[DeltaXStopper(9e-7), MyMemoryDumper()],
Add a breakpoint in MyMemoryDumper::print('end'). There are a lot of DataFrames from self.run
MyMemoryDumper::print('end')
self.run
Backtesting==0.3.3 OS: Windows
The text was updated successfully, but these errors were encountered:
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Expected Behavior
No memory leak
Actual Behavior
A huge amount of memory that prevents the optimization process
Steps to Reproduce
Create the folling class
Add it to
base_estimator
Additional info
Add a breakpoint in
MyMemoryDumper::print('end')
. There are a lot of DataFrames fromself.run
Backtesting==0.3.3
OS: Windows
The text was updated successfully, but these errors were encountered: