Comments (3)
Good question - we've never defined and documented the style we want to use for Flower, but you're right, we should pick one and declare it as the official style. It should be one that plays well with Sphinx to enable us to link to other parts of the codebase from the docstring itself. We've also started to use docformatter, so it would be good to pick a style that docformatter understands.
Perhaps it's best if we build a very small demo that showcases how a good docstrin in the format we pick looks like, how it can link to / mention other code fragments, and how docformatter itneracts with it. Would you like to take this topic @pedropgusmao ?
from flower.
Are we sticking to a specific docstring style? I found a few here that we could choose from. I particularly like the Numpydoc.
from flower.
Btw, I like the Numpydoc style as well. We sometimes have rather involved type signatures, so it helps to have a dedicated line for that. We should also pick a style that makes it clear which parameters are optional and what their default values are.
from flower.
Related Issues (20)
- example run failed HOT 3
- Add Flower Baseline: FedGen HOT 1
- Add Flower Baseline: FedDebug HOT 3
- I cant run code on the Flower Tutorial : Get started with Flower HOT 4
- Run simulation_pytorch example, there are some questions HOT 6
- How to add data augmentation in clients' local datasets? HOT 1
- pip install flwr, but there are some ImportErrors HOT 4
- how to load the mnist dataset locally HOT 3
- Inplace update error in loading state_dict for YOLOV8 HOT 5
- Unable to start federated training on CUDA when using YOLOv8 model
- Can a client call a server-side function?
- cryptography 42
- Arduino Client for federated learning with Flower HOT 1
- Label and feature skew Partitioner HOT 1
- Does the Flower support decentralized federated learning?
- Memory error in Flower simulation with 10 clients MNIST task HOT 2
- Problem when running examples/llm-flowertune/main.py HOT 3
- “evaluate_fn” appearance of test results nan issues HOT 1
- After passing the TRAIN function, the model parameters were not updated, resulting in the final evaluation criteria output of the TEST function being the same as the initial stage HOT 4
- Fatal Python error: Aborted HOT 1
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from flower.