tff.learning.templates.build_model_delta_client_work
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Creates a ClientWorkProcess for federated averaging.
tff.learning.templates.build_model_delta_client_work(
model_fn: Callable[[], tff.learning.models.VariableModel],
optimizer: tff.learning.optimizers.Optimizer,
client_weighting: tff.learning.ClientWeighting,
metrics_aggregator: Optional[tff.learning.metrics.MetricsAggregatorType] = None,
*,
loop_implementation: tff.learning.LoopImplementation = tff.learning.LoopImplementation.DATASET_REDUCE
) -> tff.learning.templates.ClientWorkProcess
Returns |
|---|
A ClientWorkProcess.
|
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Last updated 2024-09-20 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-09-20 UTC."],[],[],null,["# tff.learning.templates.build_model_delta_client_work\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/federated/blob/v0.87.0 Version 2.0, January 2004 Licensed under the Apache License, Version 2.0 (the) |\n\nCreates a `ClientWorkProcess` for federated averaging. \n\n tff.learning.templates.build_model_delta_client_work(\n model_fn: Callable[[], ../../../tff/learning/models/VariableModel],\n optimizer: ../../../tff/learning/optimizers/Optimizer,\n client_weighting: ../../../tff/learning/ClientWeighting,\n metrics_aggregator: Optional[../../../tff/learning/metrics/MetricsAggregatorType] = None,\n *,\n loop_implementation: ../../../tff/learning/LoopImplementation = ../../../tff/learning/LoopImplementation#DATASET_REDUCE\n ) -\u003e ../../../tff/learning/templates/ClientWorkProcess\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `model_fn` | A no-arg function that returns a [`tff.learning.models.VariableModel`](../../../tff/learning/models/VariableModel). This method must *not* capture TensorFlow tensors or variables and use them. The model must be constructed entirely from scratch on each invocation, returning the same pre-constructed model each call will result in an error. |\n| `optimizer` | A [`tff.learning.optimizers.Optimizer`](../../../tff/learning/optimizers/Optimizer). |\n| `client_weighting` | A [`tff.learning.ClientWeighting`](../../../tff/learning/ClientWeighting) value. |\n| `metrics_aggregator` | A function that takes in the metric finalizers (i.e., [`tff.learning.models.VariableModel.metric_finalizers()`](../../../tff/learning/models/VariableModel#metric_finalizers)) returns a [`tff.Computation`](../../../tff/Computation) for aggregating the unfinalized metrics. If `None`, this is set to [`tff.learning.metrics.sum_then_finalize`](../../../tff/learning/metrics/sum_then_finalize). |\n| `loop_implementation` | Changes the implementation of the training loop generated. See [`tff.learning.LoopImplementation`](../../../tff/learning/LoopImplementation) for more details. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `ClientWorkProcess`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub