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brainsteam/content/posts/2021/01/2021-01-14-mlflow-pickle5-madness/index.md

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+---
+title: Pickle 5 Madness with MLFlow and Python 3.6/3.7
+author: James
+type: post
+resources:
+ - name: feature
+   src: images/feature.jpg
+date: 2021-01-14T11:42:28+00:00
+url: /2021/01/14/pickle-5-madness-with-mlflow/
+description: "Solving 'unsupported pickle protocol: 5' when trying to load mlflow models"
+categories:
+  - Work
+  - Open Source
+tags:
+  - machine-learning
+  - python
+  - ai
+  - devops
+  - mlops
+
+---
+
+{{<figure src="images/feature.jpg" caption="A jar of pickles by <a href='https://www.pexels.com/photo/crop-unrecognizable-person-with-jar-of-pickled-zucchini-3952045/'>Ksenia Charnaya</a>">}}
+
+I recently came across an infuriating problem where an [MLFlow python model](https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html) I had trained on one system using Python `3.6` would not load on another system with an identical version of Python. 
+
+The exact problem was that when I ran `mlflow models serve -m <url/to/model/in/bucket>` the service would crash saying that the model could not be unserialized because `ValueError: unsupported pickle protocol: 5`.
+
+A quick bit of searching shows that this error happens when something is pickled in Python 3.8 which uses pickle protocol 5 by default and loaded by a system running an earlier version of Python 3 (3.6 or 3.7) which only support pickle protocol up to v4. 
+
+Under the covers mlflow uses [cloudpickle](https://github.com/cloudpipe/cloudpickle), a library that provides extended pickle support including the ability to pickle lambda functions and functions/classes defined interactively in the `__main__` module of your program or in a Jupyter notebook. By default `cloudpickle` uses the highest version of pickle protocol available in your python implementation (by checking [pickle.HIGHEST_PROTOCOL](https://docs.python.org/3/library/pickle.html#pickle.HIGHEST_PROTOCOL) constant) - this makes sense for most use cases where you want to serialize objects and pass them around within the same Python setup - as a rule of thumb, more recent protocols are better performing/more efficient.
+
+However this is a mystery because I'm running Python `3.6.12` on both systems which does not support protocol 5, so how is it that cloudpickle is using this version to write the models? I still haven't worked this out and if anyone knows please get in touch because it is driving me mad!
+
+Luckily for us, although the use of v5 is puzzling, there is a solution. The [pickle5](https://pypi.org/project/pickle5/) library provides version 5 support that is backwards compatible with Python 3.6 and 3.7. Furthermore, [cloudpickle will automatically detect and load this library if it is available](https://github.com/dask/distributed/pull/3849). Therefore all we need to do is install `pickle5` in our MLFLow serving environment to make this issue go away. 
+
+The easiest way to make sure pickle5 is available to your server is by adding it to your conda env when you save your model to MLFlow:
+
+```python
+
+model = SomeScikitLearnModel()
+model.fit(X,y)
+
+conda_env = mlflow.pyfunc.get_default_conda_env()
+conda_env['dependencies'].append({'pip': [
+    'pickle5'
+    'scikit-learn==0.23.2'
+    #... some other dependencies
+    ]})
+
+mlflow.sklearn.log_model(model, "model", conda_env=conda_env)
+```
+
+Note: I already checked and `pickle5` is not installed in the first environment but the Conda base version of Python on that system is `3.8.3` so I think there must be some weird leakage of the conda paths going on when I train my model.
+