Hey @smth,
please post here/let me know, when Uber or anyone else make public some sort of Black-Box variational inference engine public for PyTorch.
Tensorflow is driving me nuts - once you’ve used PyTorch it’s painful to go back to TF!
It doesn’t look too unfriendly porting over some of the code from, Edward 34.
For example, the main KL inference routines, are well written and there’s not much TF dependency, see

  
      github.com
  
  
    blei-lab/edward/blob/master/edward/inferences/klpq.py 85
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import six
import tensorflow as tf

from edward.inferences.variational_inference import VariationalInference
from edward.models import RandomVariable
from edward.util import copy, get_descendants

try:
  from edward.models import Normal
except Exception as e:
  raise ImportError("{0}. Your TensorFlow version is not supported.".format(e))


class KLpq(VariationalInference):
  """Variational inference with the KL divergence



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