[graph-tool] Re: memory handling when using graph-tool's MCMCs and multiprocessing on large networks

thanks for your reply. here's an example running minimize_blockmodel_dl() 10 times on 10 cores. when i run this on a large network (2GB, 2M vertices, 20M edges) i get a MemoryError. ``` import graph_tool.all as gt import multiprocessign as mp import numpy as np g = gt.load_graph("large_graph", fmt="graphml") N_iter = 10 N_core = 10 def fit_sbm(): state = gt.minimize_blockmodel_dl(g) b = state.get_blocks() return b def _parallel_sbm(iter = N_iter): pool = mp.Pool(N_core) future_res = [pool.apply_async(fit_sbm) for m in range(iter)] res = [f.get() for f in future_res] return res def parallel_fit_sbm(iter = M_iter): results = parallel_sbm(iter) return results results = parallel_fit_sbm() ````