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Traceback (most recent call last):
File "/Users/guha/src/tmap/lsh_query.py", line 27, in <module>
fps = Parallel(n_jobs=num_cores)(delayed(fp_function)(input_pair) for input_pair in molcsv)
File "/anaconda3/envs/rdkit/lib/python3.6/site-packages/joblib/parallel.py", line 996, in __call__
self.retrieve()
File "/anaconda3/envs/rdkit/lib/python3.6/site-packages/joblib/parallel.py", line 899, in retrieve
self._output.extend(job.get(timeout=self.timeout))
File "/anaconda3/envs/rdkit/lib/python3.6/site-packages/joblib/_parallel_backends.py", line 517, in wrap_future_result
return future.result(timeout=timeout)
File "/anaconda3/envs/rdkit/lib/python3.6/concurrent/futures/_base.py", line 432, in result
return self.__get_result()
File "/anaconda3/envs/rdkit/lib/python3.6/concurrent/futures/_base.py", line 384, in __get_result
raise self._exception
TypeError: can't pickle tmap.VectorUint objects
Are there plans to make VectorUint pickleable? Or is there an alternative approach to parallelizing this type of computation?
The text was updated successfully, but these errors were encountered:
I had asked @daenuprobst this question a while back (not on github, since I can't seem to find it on here) and the answer was to write them to a txt file. However, I've found writing/reading the fingerprints from text files can be quite slow. Would json be a better option? I have not tried this.
Hi, I'm trying to compute MHFP's in parallel and index them in a parallel fashion. The code looks like
However on running this I get
Are there plans to make
VectorUint
pickleable? Or is there an alternative approach to parallelizing this type of computation?The text was updated successfully, but these errors were encountered: