typeerror: can't pickle pycapsule objects

Error:

  File "/Users/anna/anaconda3/lib/python3.6/site-packages/dill/_dill.py", line 1055, in save_builtin_method
  pickler.save_reduce(_get_attr, (module, obj.__name__), obj = obj)
  File "/Users/anna/anaconda3/lib/python3.6/pickle.py", line 610, in save_reduce
  save(args)
  File "/Users/anna/anaconda3/lib/python3.6/pickle.py", line 476, in save
  f(self, obj) # Call unbound method with explicit self
  File "/Users/anna/anaconda3/lib/python3.6/pickle.py", line 736, in save_tuple
  save(element)
  File "/Users/anna/anaconda3/lib/python3.6/pickle.py", line 476, in save
  f(self, obj) # Call unbound method with explicit self
  File "/Users/anna/anaconda3/lib/python3.6/site-packages/dill/_dill.py", line 1260, in save_module
  state = _main_dict)
  File "/Users/anna/anaconda3/lib/python3.6/pickle.py", line 634, in save_reduce
  save(state)
  File "/Users/anna/anaconda3/lib/python3.6/pickle.py", line 476, in save
  f(self, obj) # Call unbound method with explicit self
  File "/Users/anna/anaconda3/lib/python3.6/site-packages/dill/_dill.py", line 893, in save_module_dict
  StockPickler.save_dict(pickler, obj)
  File "/Users/anna/anaconda3/lib/python3.6/pickle.py", line 821, in save_dict
  self._batch_setitems(obj.items())
  File "/Users/anna/anaconda3/lib/python3.6/pickle.py", line 847, in _batch_setitems
  save(v)
  File "/Users/anna/anaconda3/lib/python3.6/pickle.py", line 496, in save
  rv = reduce(self.proto)
  TypeError: can 't pickle PyCapsule objects

Suggestion : 2

I am new to Apache Beam and am trying to run anycodings_python the following code:,However when trying to use the DirectRunner anycodings_python I keep running into the following error:,How strictly do you follow the n-tier architecture and separation of concerns between the layers in your projects? ,Where in an Eclipse workspace is the list of projects stored?

However when trying to use the DirectRunner anycodings_python I keep running into the following error:

TypeError: can 't pickle PyCapsule objects

Suggestion : 3

First, define functions which cannot be pickled with the standard pickle protocol. They cannot be serialized with pickle because they are defined in the __main__ module. They can however be serialized with cloudpickle. With the default behavior, loky is to use cloudpickle to serialize the objects that are sent to the workers.,If you are on a UNIX system, it is possible to fallback to the old multiprocessing backend, which can pickle interactively defined functions with the default pickle module, which is faster for such large objects.,For most use-cases, using cloudpickle is efficient enough. However, this solution can be very slow to serialize large python objects, such as dict or list, compared to the standard pickle serialization.,However, the function and objects defined in __main__ are not serializable anymore using pickle and it is not possible to call func_async using this pickler.

# Code source: Thomas Moreau
# License: BSD 3 clause

import sys
import time
import traceback
from joblib.externals.loky
import set_loky_pickler
from joblib
import parallel_backend
from joblib
import Parallel, delayed
from joblib
import wrap_non_picklable_objects

def func_async(i, * args):
   return 2 * i

print(Parallel(n_jobs = 2)(delayed(func_async)(21) for _ in range(1))[0])

def func_async(i, * args):
   return 2 * i

# We have to pass an extra argument with a large list(or another large python # object).
large_list = list(range(1000000))

t_start = time.time()
Parallel(n_jobs = 2)(delayed(func_async)(21, large_list) for _ in range(1))
print("With loky backend and cloudpickle serialization: {:.3f}s"
   .format(time.time() - t_start))

With loky backend and cloudpickle serialization: 1.586 s

if sys.platform != 'win32':
   def func_async(i, * args):
   return 2 * i

with parallel_backend('multiprocessing'):
   t_start = time.time()
Parallel(n_jobs = 2)(
   delayed(func_async)(21, large_list) for _ in range(1))
print("With multiprocessing backend and pickle serialization: {:.3f}s"
   .format(time.time() - t_start))