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[WIP] Enable Skorch+Dask-ML #5748

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[WIP] Enable Skorch+Dask-ML #5748

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c13ef1c
Working skorch compatibilty
VibhuJawa Feb 1, 2022
2b083d4
Addressed reviews
VibhuJawa Feb 9, 2022
123ca52
fix style problems with __init__.py
VibhuJawa Feb 9, 2022
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6 changes: 6 additions & 0 deletions distributed/protocol/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -119,3 +119,9 @@ def _register_cudf():
def _register_cuml():
with suppress(ImportError):
from cuml.comm import serialize


@dask_serialize.register_lazy("skorch")
@dask_deserialize.register_lazy("skorch")
def _register_skorch():
from . import skorch
28 changes: 28 additions & 0 deletions distributed/protocol/skorch.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,28 @@
import cloudpickle
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import skorch
from .serialize import dask_serialize, dask_deserialize

@dask_serialize.register(skorch.NeuralNet)
def serialize_skorch(x):
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has_module = hasattr(x, "module_")
headers = {"has_module": has_module}
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if has_module:
module = x.__dict__.pop("module_")
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Curious why module_ can't be pickled on its own. Is there any more info on the issues encountered by leaving this?

Also any downside to (temporarily) modifying a user-provided object here?

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Curious why module_ can't be pickled on its own. Is there any more info on the issues encountered by leaving this?

So module's is an interactively defined class on client so its namespace is often __main__ . For eg. __main__. MyModule.

Pickle has problems pickling when interactively defined classes when they are set as an attributes of another object. as it tries to look up the class in the namespace. See eg for trace.
By pickling it on its own we are able to serialize successfully

Also any downside to (temporarily) modifying a user-provided object here?

The only side effect i can think of is if the class is redefined in the worker's name-space causing undefined behavior while de-serializing on the worker. I doubt that will really happen in real workflows.

FWIW, I have added a test to verify that at-least for the class is the same after deserialization.

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Is this just an issue with pickle? Does cloudpickle run into this issue or does it work ok?

try:
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frames = [cloudpickle.dumps(x)]
frames = frames + [cloudpickle.dumps(module)]
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finally:
x.__dict__["module_"] = module
else:
frames = [cloudpickle.dumps(x)]
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return headers, frames


@dask_deserialize.register(skorch.NeuralNet)
def deserialize_skorch(header, frames):
model = cloudpickle.loads(frames[0])
if header["has_module"]:
module = cloudpickle.loads(frames[1])
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model.module_ = module
return model