Documentation: Loading pipelines for inference

Reading about saving and loading, I find it hard to understand how to save and load a model in order to use it for inference. In particular, it’s not clear to me how the setup() phase relates to saving and loading.

This page gives an overview of the lifecycle of a model.
Here it is implied that ‘setup()’ occurs after load. However, it does not seem like the setup method is being called anywhere, except when fitting a model.

It would be nice with documentation on how the lifecycle works for inference. For example:

• am i supposed to call setup() manually, after load? If so,

  • how do i recursively setup a pipeline without implementing it myself?
  • is setup supposed to be run on a loaded state? this requires care, as one could easily overwrite lodaded state in setup. this should be documented.

• am i supposed to run setup() before load?

  • this seems unlikely looking at the flowchart
  • does not work with the ‘load()’ method in ExecutionContext which returns a new instance

• am i supposed to not run setup() before inference?

  • im suspecting this is the idea
  • it would be nice with documentation on this, with an example. It has implications on how both saving, loading and setup needs to be written.
  • i cant make sense of the flowchart if this is the case.

[vincent-antaki]

Hello Joel!

Here is some useful information with regards to your question:

• You usually shouldn’t have to to call setup manually, although you can if want to.
• You are not supposed to run setup before load.
• It is expected that, on the first call of setup, the attribute self.is_initialized (which is False after the constructor call) is set to True. If you want to avoid overwriting behaviour on a setup call, all you need to do is add a condition over self.is_initialized before the sensitive block of code.
• All steps that have childrens (MetaSteps and TruncableSteps) are supposed to recursively call setup on their children, with the notable exception of Pipeline instances.
• Pipeline instances are a special case of setup, where the call to setup its children is delayed right before the fit call is performed on its children. This is important for steps that can’t be setup right away (e.g. some intermediate value needs to be computed in a previous setup method is called).
• In case Pipeline is not the root of your ML pipeline, then calling setup manually (or adding a call to it in your fit function) is expected. Regardless, setup will be called on fit when a Pipeline instance is reached.
• In the current setup of saving and loading, all steps are supposed to have setup called before saving. In case self.is_initialized is False (i.e. the setup method has not been called) for a given step of the pipeline at the moment of saving, a call to the setup method will be forced. Note that this may change at some point, as we intend that self.is_initialized==true will no longer be required to save a step thus allowing save even if the pipeline hasn’t had a fit call yet (see #470).

side note : I think the flowchart may be getting old a bit.

Specifically with regards to your 3 options, the third one is the intended usage. It is expected that setup is only called through pipeline fit calls. From there, here are a couple of options you have :

• You could write a custom Saver (see BaseSaver) for your step that needs setupping after load.
• You could add a call to setup in your transform function.
• You could use a apply call (e.g. pipeline.apply(‘setup’, context=context)) to force call setup on all on every steps in the pipeline. I’d recommend avoiding this option though as a step’s setup function might be called multiple time (once through the apply, and multiple other time through its parents apply calls).

Overall, I agree with you that setup is poorly documented and might need to be revisited eventually.

Feel free to ask more questions if you have any, I’ll be glad to help you. Cheers!

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