Status on cloudpickle non-determinism

[feizerl]

Hello,

@ogrisel mentioned in this comment (#385 (comment)):

Anyway, having deterministic pickles is probably out of scope for cloudpickle so I would be in favor of closing this issue.

However, @ogrisel also pushed a PR (#428) which was released as part of cloudpickle 2.0.0 that tried to address non determinism owing to dictionary ordering.

I wanted to confirm what is the official status of the project regarding non determinism because I am still seeing non deterministic pickles in cloudpickle 2.0.0

Here is the pickletools.dis outputs of a function:

...
 2539: (                MARK  
 2540: \x8c                 SHORT_BINUNICODE 'correlation_matrix'                                                                           
 2560: \x94                 MEMOIZE    (as 115)
 2561: \x8c                 SHORT_BINUNICODE 'drifts_annual'                                                                                
 2576: \x94                 MEMOIZE    (as 116)
 2577: \x8c                 SHORT_BINUNICODE 'initial_prices'                                                                               
 2593: \x94                 MEMOIZE    (as 117)                                                                                             
 2594: \x8c                 SHORT_BINUNICODE 'volatilities_annual'                                                                          
 2615: \x94                 MEMOIZE    (as 118)                                                                                             
 2616: \x91                 FROZENSET  (MARK at 2539)
 2617: \x94             MEMOIZE    (as 119)  
...

pickle of a function on second attempt:

...
 2539: (                MARK  
 2540: \x8c                 SHORT_BINUNICODE 'volatilities_annual'                                                                          
 2561: \x94                 MEMOIZE    (as 115)
 2562: \x8c                 SHORT_BINUNICODE 'initial_prices'                                                                               
 2578: \x94                 MEMOIZE    (as 116)
 2579: \x8c                 SHORT_BINUNICODE 'drifts_annual'
 2594: \x94                 MEMOIZE    (as 117)                                                                                             
 2595: \x8c                 SHORT_BINUNICODE 'correlation_matrix'
 2615: \x94                 MEMOIZE    (as 118)                                                                                             
 2616: \x91                 FROZENSET  (MARK at 2539)
 2617: \x94             MEMOIZE    (as 119)
...       

As you can see, the entries are all the same, but shuffled around.

This function is part of a large project, so unfortunately I can't produce a short test case right now.

Notice that kubeflow pipelines implement caching by making sure that pickle of the function hasn't changed. (there is an option to not use pickle as well, but it has its own problems). Having a non deterministic cloudpickle invalidates the cache every time making that feature useless.

Thanks.

[pierreglaser]

The pickle instructions you're displaying seems like reconstruction instruction for set (more precisely, FrozenSet) instances, which are not ordered (unlike dicts in Python > 3.7). I would imagine that pickles of set objects created by pickle (and not cloudpickle) are also non-deterministic. Not sure if we want to override this behavior (which would in any way work only for the CloudPickler inheriting from the pure-python Pickler, and not the fast C-backed Pickler).

[ogrisel]

We could introduce a constructor parameter to implement a slower, deterministic version of cloudpickle. But, indeed I am not sure how we could do that with the subclass of the C-implementation of the CPython pickle.Pickler class.

Note that joblib.hash can do that already, but it does not analyse the content of dynamically defined functions.

[AdrS]

Apache Beam uses either cloudpickle or dill to save code as part of a workflow graph specification. Google uses Apache Beam with a best-effort cache to avoid starting workflows from scratch after they get interrupted. Increasing the determinism of pickling increases the cache hit rate, saves resources, and avoids delays from starting workflows again from scratch. Apache Beam is transitioning from dill to cloudpickle and would like to contribute two changes to increase the pickling determinism:

1. Allow cloudpickle users to customize set serialization (Support customizing how built-in types are pickled #563). Then Apache Beam can register a function to serialize sets by sorting the elements before saving them.
2. Avoid UUIDs in classes. (I have not investigated this yet)

Ideally these changes can be part of cloudpickle so Apache Beam can minimize the changes to its vendored copy.

Note: the goal is not to guarantee complete determinism. Mostly-deterministic pickling is useful enough.