Hi, did you try to do something along these lines already?: https://stackoverflow.com/questions/7089386/pickling-boost-python-functions
Peter [https://cdn.sstatic.net/Sites/stackoverflow/Img/apple-touch-i...@2.png?v=73d79a89bded]<https://stackoverflow.com/questions/7089386/pickling-boost-python-functions> pickle - Pickling boost python functions - Stack Overflow<https://stackoverflow.com/questions/7089386/pickling-boost-python-functions> For the use with joblib.Parallel, I need to be able to pickle a boost::python function. When I try to do so, I get a TypeError: can't pickle builtin_function_or_method objects As far as I underst... stackoverflow.com ________________________________ From: Patrick Walters <wpwalt...@gmail.com> Sent: Monday, March 22, 2021 13:49 To: rdkit-discuss <rdkit-discuss@lists.sourceforge.net> Subject: Re: [Rdkit-discuss] Using the RDKit with Dask Apologies, there was a bug in the code I sent in my previous message. The problem is the same. Here is the corrected code in a gist. https://gist.github.com/PatWalters/ca41289a6990ebf7af1e5c44e188fccd On Mon, Mar 22, 2021 at 8:16 AM Patrick Walters <wpwalt...@gmail.com<mailto:wpwalt...@gmail.com>> wrote: Hi All, I've been trying to calculate BCUT2D descriptors in parallel with Dask and get this error with the code below. TypeError: cannot pickle 'Boost.Python.function' object Everything works if I call mw_df, which calculates molecular weight, but I get the error above if I call bcut_df. Does anyone have a workaround? Thanks, Pat #!/usr/bin/env python import sys import dask.dataframe as dd import pandas as pd from rdkit import Chem from rdkit.Chem.Descriptors import MolWt from rdkit.Chem.rdMolDescriptors import BCUT2D import time # -- molecular weight functions def calc_mw(smi): mol = Chem.MolFromSmiles(smi) return MolWt(mol) def mw_df(df): return df.SMILES.apply(calc_mw) # -- bcut functions def bcut_df(df): return df.apply(calc_bcut) def calc_bcut(smi): mol = Chem.MolFromSmiles(smi) return BCUT2D(mol) def main(): start = time.time() df = pd.read_csv(sys.argv[1],sep=" ",names=["SMILES","Name"]) ddf = dd.from_pandas(df,npartitions=16) ddf['MW'] = ddf.map_partitions(mw_df,meta='float').compute(scheduler='processes') ddf['BCUT'] = ddf.map_partitions(bcut_df,meta='float').compute(scheduler='processes') print(time.time()-start) print(ddf.head()) if __name__ == "__main__": main()
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