amp.initialize
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- Python
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Description
First thanks for the Apex library. It is an excellent help for FP16 training.
I am trying to load a pre-trained network again, and my concern is that if I want to use amp.initialize function, I also have to define an optimizer since it is a mandatory argument to the function. If I wish to not to use this function, I should manually cast all input to FP16 and the model itself. I am wondering if there is an API, so I can load the pre-trained network in respect to opt-level, without defining an optimizer during inference part.
Thanks.
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the amp.initialize entry point and trace how its mandatory optimizer and opt-level arguments are handled for pre-trained models. Done means inference can use the requested opt-level without defining an optimizer, with the expected FP16 behavior preserved.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100