refactor(examples) Refactor quickstart-sklearn-tabular to use the new Flower #3893
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adam-narozniak
requested review from
jafermarq,
tanertopal and
danieljanes
as code owners
examples/quickstart-sklearn-tabular/sklearnexample/server_app.py
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…earn-tabular-example
Co-authored-by: Javier <[email protected]>
…m:adap/flower into update-quickstart-sklearn-tabular-example
Co-authored-by: Javier <[email protected]>
…m:adap/flower into update-quickstart-sklearn-tabular-example
Co-authored-by: Javier <[email protected]>
examples/quickstart-sklearn-tabular/sklearnexample/client_app.py
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examples/quickstart-sklearn-tabular/sklearnexample/server_app.py
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| def get_parameters(self, config): | ||
| return get_model_parameters(self.model) |
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We are trying to update the examples in such a way that this method is no longer needed. For this, we need to initialize the global model when constructing the ServerApp.
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Sorry, I don't quite get it. This function, either directly or not (so either using self.get_parameters or get_model_parameters), is needed since we need to get the model parameters each round, not only the global one once. Also, in the mlx example it exists.
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yes. we want to retain the functionality to extract the parameters (in this case the get_model_parameters() function) but having the get_parameters() method seems in many cases redundant since the only reason why it exists is so, if the strategy doesn't initialise global model, one random client can be sampled and call the get_parameters() method. But it seems to be better to always initialise the global model at the strategy so this get_parameters() is not needed (i.e. fit() can directly call the get_model_parameters() function when returning).
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But it seems to be better to always initialise the global mode what is the reason for that?
For me, the discussion seems like a potato-potAto kind of thing. What difference does it really make? It's either the get_model_weights is implemented as the external function and passed to the method or directly as a method, right? So it does not really matter which function/method is used in the fit?
examples/quickstart-sklearn-tabular/sklearnexample/client_app.py
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Co-authored-by: Javier <[email protected]>
Co-authored-by: Javier <[email protected]>
Co-authored-by: Javier <[email protected]>
Co-authored-by: Javier <[email protected]>
examples/quickstart-sklearn-tabular/sklearnexample/client_app.py
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examples/quickstart-sklearn-tabular/sklearnexample/client_app.py
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| ```shell | ||
| poetry run python3 client.py --partition-id 0 # partition-id should be any of {0,1,2} | ||
| ```bash | ||
| flwr run . --run-config penalty="l1" |
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| flwr run . --run-config penalty="l1" | |
| flwr run . --run-config num-server-rounds =10 |
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it seems "l1" penalty is not supported, so i'd suggest showing the overriding functionality with another setting.
ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.
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I've changed the solver to "saga" now which supports also l1.
Though I cannot run the
flwr run . --run-config penalty="l1"
I get:
CalledProcessError: Command '['flower-simulation', '--app', '.', '--num-supernodes', '3', '--run-config', 'penalty=l1']' returned non-zero exit status 1.
Co-authored-by: Javier <[email protected]>
Co-authored-by: Javier <[email protected]>
… Flower (#3893) Co-authored-by: jafermarq <[email protected]>
Issue
The example does not use the new Flower API.
Proposal
Update the example to follow the structure and code of the new Flower.