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[MNT] maintenance & handover items for integration with sktime org #1592

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14 of 21 tasks
fkiraly opened this issue Aug 22, 2024 · 8 comments
Open
14 of 21 tasks

[MNT] maintenance & handover items for integration with sktime org #1592

fkiraly opened this issue Aug 22, 2024 · 8 comments
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maintenance Continuous integration, unit testing & package distribution

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@fkiraly
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fkiraly commented Aug 22, 2024

We are currently planning a maintenance handover of pytorch-forecasting to sktime. Both packages will remain separate, with pytorch-forecasting on the "models" level, and sktime providing framework integration.

This issue is to plan urgent maintenance items and handover (to sktime maintenance model).

Community input on todos and wishlist is also appreciated, e.g., what are "burning" items, suggested priorities.

Maintenance items

handover items

  • issue triage
  • stale PR review and triage - all
  • operational handover
  • pointers to dev channels
  • digital assets
  • release pipeline

roadmap items for consideration or wishlist

@fkiraly
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fkiraly commented Aug 22, 2024

FYI sktime developers who have worked on the integration, @benHeid, @fnhirwa, @geetu040, @XinyuWuu, @yarnabrina

@fkiraly fkiraly added the maintenance Continuous integration, unit testing & package distribution label Aug 22, 2024
@fkiraly fkiraly changed the title [MNT] maintenance & handover roadmap [MNT] operational integration with sktime - maintenance & handover items Aug 22, 2024
@fkiraly fkiraly pinned this issue Aug 22, 2024
@fkiraly fkiraly changed the title [MNT] operational integration with sktime - maintenance & handover items [MNT] op integration with sktime - maintenance & handover items Aug 22, 2024
@fkiraly fkiraly changed the title [MNT] op integration with sktime - maintenance & handover items [MNT] maintenance & handover items for integration with sktime org Aug 22, 2024
@fkiraly
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fkiraly commented Aug 22, 2024

At the very start, we need to get the CI run again.
Reason for failures:

Suggested measures:

Once this is done, release 1.1.0.

Next, dependency management:

  • support python 3.11, 3.12
  • widen version bounds for numpy, then optuna, fix incompatibilities
  • isolate dependencies that could be soft dependencies. Minimize core dep set.
  • manage and refactor developer dep sets

@XinyuWuu
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Fixing tutorial notebooks is also very important: #1599

@fkiraly
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fkiraly commented Aug 23, 2024

of course - we won't know whether we fixed them though if the CI does not run

@XinyuWuu
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Regarding the dependency management, maybe we should not minimize core dep set.

Comparing to torch, lightning, optuna, other packages are too small to make a difference to installation time.

It could be a disaster if a lot a time has been spent to train the model, and then some soft dependency error comes out.

So maybe we should make sure that users have almost all the functionalities with the core dep set.

@fkiraly
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fkiraly commented Aug 23, 2024

Agree, though even small dependencies pose a risk of reducing the degree of interoperability on the level of dependencies. Suppose you have small_package which has a very restrictive dependency set, or small_package2 which is the only dependency that never upgrades to numpy 2 or python 3.12.

Further, I wonder about how central optuna is here. It is important for tuning, but its purpose seems extraneous to the primary defining topic of deep learning based forecasters.

@XinyuWuu
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Further, I wonder about how central optuna is here. It is important for tuning, but its purpose seems extraneous to the primary defining topic of deep learning based forecasters.

It's only used in pytorch_forecasting/models/temporal_fusion_transformer/tuning.py to tune TemporalFusionTransformer. So I guess it's not so central.

@XinyuWuu
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Agree, though even small dependencies pose a risk of reducing the degree of interoperability on the level of dependencies. Suppose you have small_package which has a very restrictive dependency set, or small_package2 which is the only dependency that never upgrades to numpy 2 or python 3.12.

Yeah, that could be a problem. We could have two dep sets core and compatible and put those packages that don't fit in well in compatible set.

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