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License: MIT License
Tools for Deep Learning
License: MIT License
@stateful
decorator can be useful with objects whose state is large, e.g. a neural network model with millions of parameters. For such cases, it is useful to save the state in compressed form. In addition to reducing the save on disk, it is also useful to decrease the time over the wire if the state is being saved on a remote location.
Although this package is very early in development, to make it usable in platforms like Kaggle or Colab so it can be tested properly, I need to publish it to PyPI so I could install it via pip
on such platforms.
At the moment, the idea I have in mind is to create a release
branch and a GitHub workflow such that it automatically publish the package upon pushing to the release
branch.
The @stateful
decorator currently either initialize the object normally (if a state is not saved in a file) or load the state from a file and completely ignore initialization. However, at times it might be desired to do post-load processing.
It is useful to be able to have the @stateful
decorator work with functions as well. For example:
@stateful
def my_expensive_method(arg1, arg2):
# Implementation
The __name__
attribute along with the positional and keyword arguments can be used to generate a signature, and the return value of the function is stored in the file.
Saving the state on disk can sometimes be lost, for example using the library on a Kaggle kernel. For such cases, it is desired to save the state on a remote location, e.g. the cloud. To make this achievable, we need to support customization of where states are saved.
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