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()
````
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