Pickling a class definition

Question

Is there a way to pickle a class definition?

What I'd like to do is pickle the definition (which may created dynamically), and then send it over a TCP connection so that an instance can be created on the other end.

I understand that there may be dependencies, like modules and global variables that the class relies on. I'd like to bundle these in the pickling process as well, but I'm not concerned about automatically detecting the dependencies because it's okay if the onus is on the user to specify them.

Answer 1

If you use dill, it enables you to treat __main__ as if it were a python module (for the most part). Hence, you can serialize interactively defined classes, and the like. dill also (by default) can transport the class definition as part of the pickle.

>>> class MyTest(object):
...   def foo(self, x):
...     return self.x * x
...   x = 4
... 
>>> f = MyTest() 
>>> import dill
>>>
>>> with open('test.pkl', 'wb') as s:
...   dill.dump(f, s)
... 
>>> 

Then shut down the interpreter, and send the file test.pkl over TCP. On your remote machine, now you can get the class instance.

Python 2.7.9 (default, Dec 11 2014, 01:21:43) 
[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import dill
>>> with open('test.pkl', 'rb') as s:
...   f = dill.load(s)
... 
>>> f
<__main__.MyTest object at 0x1069348d0>
>>> f.x
4
>>> f.foo(2)
8
>>>             

But how to get the class definition? So this is not exactly what you wanted. The following is, however.

>>> class MyTest2(object):
...   def bar(self, x):
...     return x*x + self.x
...   x = 1
... 
>>> import dill
>>> with open('test2.pkl', 'wb') as s:
...   dill.dump(MyTest2, s)
... 
>>>

Then after sending the file… you can get the class definition.

Python 2.7.9 (default, Dec 11 2014, 01:21:43) 
[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import dill
>>> with open('test2.pkl', 'rb') as s:
...   MyTest2 = dill.load(s)
... 
>>> print dill.source.getsource(MyTest2)
class MyTest2(object):
  def bar(self, x):
    return x*x + self.x
  x = 1

>>> f = MyTest2()
>>> f.x
1
>>> f.bar(4)
17

So, within dill, there's dill.source, and that has methods that can detect dependencies of functions and classes, and take them along with the pickle (for the most part).

>>> def foo(x):
...   return x*x
... 
>>> class Bar(object):
...   def zap(self, x):
...     return foo(x) * self.x
...   x = 3
... 
>>> print dill.source.importable(Bar.zap, source=True)
def foo(x):
  return x*x
def zap(self, x):
  return foo(x) * self.x

So that's not "perfect" (or maybe not what's expected)… but it does serialize the code for a dynamically built method and it's dependencies. You just don't get the rest of the class -- but the rest of the class is not needed in this case.

If you wanted to get everything, you could just pickle the entire session.

>>> import dill
>>> def foo(x):
...   return x*x
... 
>>> class Blah(object):
...   def bar(self, x):
...     self.x = (lambda x:foo(x)+self.x)(x)
...   x = 2
... 
>>> b = Blah()
>>> b.x
2
>>> b.bar(3)
>>> b.x
11
>>> dill.dump_session('foo.pkl')
>>> 

Then on the remote machine...

Python 2.7.9 (default, Dec 11 2014, 01:21:43) 
[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import dill
>>> dill.load_session('foo.pkl')
>>> b.x
11
>>> b.bar(2)
>>> b.x
15
>>> foo(3)
9

Lastly, if you want the transport to be "done" for you transparently, you could use pathos.pp or ppft, which provide the ability to ship objects to a second python server (on a remote machine) or python process. They use dill under the hood, and just pass the code across the wire.

>>> class More(object):
...   def squared(self, x):
...     return x*x
... 
>>> import pathos
>>> 
>>> p = pathos.pp.ParallelPythonPool(servers=('localhost,1234',))
>>> 
>>> m = More()
>>> p.map(m.squared, range(5))
[0, 1, 4, 9, 16]

The servers argument is optional, and here is just connecting to the local machine on port 1234… but if you use the remote machine name and port instead (or as well), you'll fire off to the remote machine -- "effortlessly".

Get dill, pathos, and ppft here: https://github.com/uqfoundation

Answer 2

Alas, not directly. You can send the string form of the class statement, or a bytecode form, and "rehydrate" it with an exec on the receiving end.