THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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1. Problem
Currently, there is no mechanism to verify consistency and integrity of models trained on older MXNet versions. Any unwarranted change in an underlying model saving/loading API could potentially break the backwards compatibility across MXNet versions.
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Model Backward compatibility aims to check whether models trained on earlier versions of MXNet are loading fine on the latest version or the latest release candidate. It also aims to do a sanity check for consistency in the inference on these trained models.
3. Approach
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Here's a proposed approach to do this :
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