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Over one year ago I published a blog how to use Python seamlessly inside ABAP. Now shivamshukla asked whether it is possible to realize ML on this way. So I tried the last days the possibilities to use TensorFlow seamlessly inside ABAP. TensorFlow is an end-to-end open source platform for machine learning. And what should I say, it worked without any problems.

I started the installation of Python, but here I used version 3.7.2, and PyWin32, here I used version 224, as I described in my blog. Now I installed TensorFlow 1.13. In a normal case you can do it easily with PIP. I used the manually way - more complicated but I saw in detail what happens.

In the next step I added in the security configuration of the SAP Logon a user rule to allow the access to the Python.Demo control.

Now I changed my Python COM server program, I added two functions TensorFlow1 and TensorFlow2 - not very imaginative but enough for testing. These functions contains test routines to check the installation of TensorFlow. Both delivers a result back.
# -*- coding: iso-8859-15 -*-
import sys, win32com.server.register
import platform, struct
import tensorflow as tf

class PythonDemo:
#To create a new GUID use the commands:
#>>> import pythoncom
#>>> print(pythoncom.CreateGuid())
_reg_clsid_ = "{D1B0A23F-0B6E-46D6-8880-744DBFFB86CD}"
_reg_progid_ = "Python.Demo"
_reg_desc_ = "Python Demo COM Server"
_public_methods_ = ["HelloWorld", "HelloYou", "TensorFlow1", "TensorFlow2"]

#-Function HelloWorld-------------------------------------------------
def HelloWorld(self):
return "Hello World from Python " + platform.python_version() + \
" on " + platform.system() + " (" + platform.architecture()[0] + \

#-Function HelloYou---------------------------------------------------
def HelloYou(self, name="Anybody"):
return "Hello " + str(name) + " from Python"

#-Function TensorFlow1------------------------------------------------
def TensorFlow1(self):
hello = tf.constant('Hello World from TensorFlow')
sess = tf.Session()
rc =
return str(rc)

#-Function TensorFlow2------------------------------------------------
def TensorFlow2(self):
a = tf.constant(10)
b = tf.constant(32)
sess = tf.Session()
rc = + b)
return str(rc)

#-Sub Main--------------------------------------------------------------
def main():
if sys.argv[1].lower() == "--register":
print("Registering COM server...")
if sys.argv[1].lower() == "--unregister":
print("Unregistering COM server...")

if __name__=="__main__":


Then I registered the COM server and tried the following ABAP report...

lo_python TYPE OLE2_OBJECT,
lv_return TYPE STRING,

CREATE OBJECT lo_python 'Python.Demo'.
CHECK sy-subrc = 0 AND lo_python-Handle > 0 AND lo_python-Type = 'OLE2'.

CALL METHOD OF lo_python 'HelloWorld' = lv_return.
WRITE: / lv_return.

CALL METHOD OF lo_python 'HelloYou' = lv_return
#1 = 'Stefan'.
WRITE: / lv_return.

CALL METHOD OF lo_python 'TensorFlow1' = lv_return.
WRITE: / `Method TensorFlow1: ` && lv_return.

CALL METHOD OF lo_python 'TensorFlow2' = lv_return.
WRITE: / `Method TensorFlow2: ` && lv_return.


...with this result.

As I said above, it works so far without any problems. On this way it seems to possible to use ML seamlessly inside ABAP, in this case TensorFlow.
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