THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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Here's
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a
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proposed
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approach
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to
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do
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this
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:
- Create simple models on earlier versions of MXNet, initialize them with randomly generated weights, perform a forward pass on them. Save the model and model parameters and upload them in an S3 bucket.
- As a continuation of previous step, perform a simple inference on randomly generated input and save the randomly generated input as well as the inference output along with the model files on S3.
- The inference script running on the latest master branch of MXNet repository, would pull the model files + data and would try to load the models back into memory. The tests would fail if the models fail to load into memory or they give a different inference output. The different inference output could indicate or flag a potential change in an underlying operator.
- Use the same seed values to ensure we have the same environment for both training and inference files.
- These tests could be a part of nightly tests and would help in flagging out the above mentioned issues.
- Primarily the model backwards compatibility checker would cover the following APIs to save/load models :
- Declarative Models load_checkpoint() from Model API
- Gluon Models load_parameters/save_parameters API from Gluon Package
- Gluon Models load_params/save_params API from Gluon Package
- TheHybridized models import/export API from Gluon Package
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