I train Catboost models on various datasets and face a repetitive issue: once the last tree is fitted the RAM consumption starts to grow far more than during the training often leading to crashing the jupyter kernel. Is there any kind of finalization jobs that I can disable to prevent the memory overflow? I tried passing the used_ram_limit parameter but it seems to have no effect on the RAM consumption after fitting the last tree Continue reading...
