secretflow.ml.nn.fl.backend.torch.strategy package#

Submodules#

secretflow.ml.nn.fl.backend.torch.strategy.fed_avg_g module#

Classes:

`FedAvgG`(builder_base, *[, _ray_trace_ctx])	FedAvgG: An implementation of FedAvg, where the clients upload their accumulated gradients during the federated round to the server for averaging and update their local models using the aggregated gradients from the server in each federated round.
`PYUFedAvgG`	alias of `ActorProxy(PYUFedAvgG)`

class secretflow.ml.nn.fl.backend.torch.strategy.fed_avg_g.FedAvgG(builder_base: Callable[[], TorchModel], *, _ray_trace_ctx=None)[source]#

Bases: BaseTorchModel

FedAvgG: An implementation of FedAvg, where the clients upload their accumulated gradients during the federated round to the server for averaging and update their local models using the aggregated gradients from the server in each federated round.

Methods:

train_step(gradients, cur_steps, ...)

Accept ps model params, then do local train

train_step(gradients: ndarray, cur_steps: int, train_steps: int, **kwargs) → Tuple[ndarray, int][source]#

Accept ps model params, then do local train

Parameters

gradients – global gradients from params server
cur_steps – current train step
train_steps – local training steps
kwargs – strategy-specific parameters

Returns

Parameters after local training

secretflow.ml.nn.fl.backend.torch.strategy.fed_avg_g.PYUFedAvgG[source]#

alias of ActorProxy(PYUFedAvgG) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(gradients, cur_steps, train_steps, *)	Accept ps model params, then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.torch.strategy.fed_avg_u module#

Classes:

`FedAvgU`(builder_base, *[, _ray_trace_ctx])	FedAvgU: An implementation of FedAvg, where the clients upload their model updates to the server for averaging and update their local models with the aggregated updates from the server in each federated round.
`PYUFedAvgU`	alias of `ActorProxy(PYUFedAvgU)`

class secretflow.ml.nn.fl.backend.torch.strategy.fed_avg_u.FedAvgU(builder_base: Callable[[], TorchModel], *, _ray_trace_ctx=None)[source]#

Bases: BaseTorchModel

FedAvgU: An implementation of FedAvg, where the clients upload their model updates to the server for averaging and update their local models with the aggregated updates from the server in each federated round. This paradigm acts the same as FedAvgG when using the SGD optimizer, but may not for other optimizers (e.g., Adam).

Methods:

train_step(updates, cur_steps, train_steps, ...)

Accept ps model params, then do local train

train_step(updates: ndarray, cur_steps: int, train_steps: int, **kwargs) → Tuple[ndarray, int][source]#

Accept ps model params, then do local train

Parameters

updates – global updates from params server
cur_steps – current train step
train_steps – local training steps
kwargs – strategy-specific parameters

Returns

Parameters after local training

secretflow.ml.nn.fl.backend.torch.strategy.fed_avg_u.PYUFedAvgU[source]#

alias of ActorProxy(PYUFedAvgU) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(updates, cur_steps, train_steps, *)	Accept ps model params, then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.torch.strategy.fed_avg_w module#

Classes:

`FedAvgW`(builder_base, *[, _ray_trace_ctx])	FedAvgW: A naive implementation of FedAvg, where the clients upload their trained model weights to the server for averaging and update their local models via the aggregated weights from the server in each federated round.
`PYUFedAvgW`	alias of `ActorProxy(PYUFedAvgW)`

class secretflow.ml.nn.fl.backend.torch.strategy.fed_avg_w.FedAvgW(builder_base: Callable[[], TorchModel], *, _ray_trace_ctx=None)[source]#

Bases: BaseTorchModel

FedAvgW: A naive implementation of FedAvg, where the clients upload their trained model weights to the server for averaging and update their local models via the aggregated weights from the server in each federated round.

Methods:

train_step(weights, cur_steps, train_steps, ...)

Accept ps model params, then do local train

train_step(weights: ndarray, cur_steps: int, train_steps: int, **kwargs) → Tuple[ndarray, int][source]#

Accept ps model params, then do local train

Parameters

weights – global weight from params server
cur_steps – current train step
train_steps – local training steps
kwargs – strategy-specific parameters

Returns

Parameters after local training

secretflow.ml.nn.fl.backend.torch.strategy.fed_avg_w.PYUFedAvgW[source]#

alias of ActorProxy(PYUFedAvgW) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(weights, cur_steps, train_steps, *)	Accept ps model params, then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.torch.strategy.fed_prox module#

Classes:

`FedProx`(builder_base, *[, _ray_trace_ctx])	FedfProx: An FL optimization strategy that addresses the challenge of heterogeneity on data (non-IID) and devices, which adds a proximal term to the local objective function of each client, for better convergence.
`PYUFedProx`	alias of `ActorProxy(PYUFedProx)`

class secretflow.ml.nn.fl.backend.torch.strategy.fed_prox.FedProx(builder_base: Callable[[], TorchModel], *, _ray_trace_ctx=None)[source]#

Bases: BaseTorchModel

FedfProx: An FL optimization strategy that addresses the challenge of heterogeneity on data (non-IID) and devices, which adds a proximal term to the local objective function of each client, for better convergence. In the feature, this strategy will allow every client to train locally with a different Gamma-inexactness, for higher training efficiency.

Methods:

`w_norm`(w1, w2)
`train_step`(weights, cur_steps, train_steps, ...)	Accept ps model params,then do local train

w_norm(w1: List, w2: List)[source]#

train_step(weights: ndarray, cur_steps: int, train_steps: int, **kwargs) → Tuple[ndarray, int][source]#

Accept ps model params,then do local train

Parameters

weights – global weight from params server
cur_steps – current train step
train_steps – local training steps
kwargs – strategy-specific parameters mu: hyper-parameter for the proximal term, default is 0.0

Returns

Parameters after local training

secretflow.ml.nn.fl.backend.torch.strategy.fed_prox.PYUFedProx[source]#

alias of ActorProxy(PYUFedProx) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(weights, cur_steps, train_steps, *)	Accept ps model params,then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`w_norm`(w1, w2, *[, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.torch.strategy.fed_scr module#

Classes:

`FedSCR`(builder_base, *[, _ray_trace_ctx])	FedSCR: A structure-wise aggregation method to identify and remove redundant updates, it aggregates parameter updates over a particular structure (e.g., filters and channels).
`PYUFedSCR`	alias of `ActorProxy(PYUFedSCR)`

class secretflow.ml.nn.fl.backend.torch.strategy.fed_scr.FedSCR(builder_base: Callable[[], TorchModel], *, _ray_trace_ctx=None)[source]#

Bases: BaseTorchModel

FedSCR: A structure-wise aggregation method to identify and remove redundant updates, it aggregates parameter updates over a particular structure (e.g., filters and channels). If the sum of the absolute updates of a model structure is lower than a given threshold, FedSCR will treat the updates in this structure as less important and filter them out.

Methods:

`__init__`(builder_base, *[, _ray_trace_ctx])
`train_step`(updates, cur_steps, train_steps, ...)	Accept ps model params,then do local train

__init__(builder_base: Callable[[], TorchModel], *, _ray_trace_ctx=None)[source]#

train_step(updates: ndarray, cur_steps: int, train_steps: int, **kwargs) → Tuple[ndarray, int][source]#

Accept ps model params,then do local train

Parameters

updates – global updates from params server
cur_steps – current train step
train_steps – local training steps
kwargs – strategy-specific parameters threshold: user-defined threshold, controlling the selectivity of weight updates, filtering insignificant updates

Returns

Parameters after local training

secretflow.ml.nn.fl.backend.torch.strategy.fed_scr.PYUFedSCR[source]#

alias of ActorProxy(PYUFedSCR) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(updates, cur_steps, train_steps, *)	Accept ps model params,then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.torch.strategy.fed_stc module#

Classes:

`FedSTC`(builder_base, *[, _ray_trace_ctx])	FedSTC: Sparse Ternary Compression (STC), a new compression framework that is speciﬁcally designed to meet the requirements of the Federated Learning environment.
`PYUFedSTC`	alias of `ActorProxy(PYUFedSTC)`

class secretflow.ml.nn.fl.backend.torch.strategy.fed_stc.FedSTC(builder_base: Callable[[], TorchModel], *, _ray_trace_ctx=None)[source]#

Bases: BaseTorchModel

FedSTC: Sparse Ternary Compression (STC), a new compression framework that is speciﬁcally designed to meet the requirements of the Federated Learning environment. STC applies both sparsity and binarization in both upstream (client –> server) and downstream (server –> client) communication.

Methods:

`__init__`(builder_base, *[, _ray_trace_ctx])
`train_step`(updates, cur_steps, train_steps, ...)	Accept ps model params,then do local train

__init__(builder_base: Callable[[], TorchModel], *, _ray_trace_ctx=None)[source]#

train_step(updates: ndarray, cur_steps: int, train_steps: int, **kwargs) → Tuple[ndarray, int][source]#

Accept ps model params,then do local train

Parameters

updates – global updates from params server
cur_steps – current train step
train_steps – local training steps
kwargs – strategy-specific parameters sparsity: SparsityParameters,the ratio of masked elements, default is 0.0

Returns

Parameters after local training

secretflow.ml.nn.fl.backend.torch.strategy.fed_stc.PYUFedSTC[source]#

alias of ActorProxy(PYUFedSTC) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(updates, cur_steps, train_steps, *)	Accept ps model params,then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

Module contents#

Classes:

`PYUFedAvgW`	alias of `ActorProxy(PYUFedAvgW)`
`PYUFedAvgG`	alias of `ActorProxy(PYUFedAvgG)`
`PYUFedAvgU`	alias of `ActorProxy(PYUFedAvgU)`
`PYUFedProx`	alias of `ActorProxy(PYUFedProx)`
`PYUFedSCR`	alias of `ActorProxy(PYUFedSCR)`
`PYUFedSTC`	alias of `ActorProxy(PYUFedSTC)`

secretflow.ml.nn.fl.backend.torch.strategy.PYUFedAvgW[source]#

alias of ActorProxy(PYUFedAvgW) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(weights, cur_steps, train_steps, *)	Accept ps model params, then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.torch.strategy.PYUFedAvgG[source]#

alias of ActorProxy(PYUFedAvgG) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(gradients, cur_steps, train_steps, *)	Accept ps model params, then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.torch.strategy.PYUFedAvgU[source]#

alias of ActorProxy(PYUFedAvgU) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(updates, cur_steps, train_steps, *)	Accept ps model params, then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.torch.strategy.PYUFedProx[source]#

alias of ActorProxy(PYUFedProx) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(weights, cur_steps, train_steps, *)	Accept ps model params,then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`w_norm`(w1, w2, *[, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.torch.strategy.PYUFedSCR[source]#

alias of ActorProxy(PYUFedSCR) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(updates, cur_steps, train_steps, *)	Accept ps model params,then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.torch.strategy.PYUFedSTC[source]#

alias of ActorProxy(PYUFedSTC) Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build torch.dataloader
`build_dataset_from_csv`(csv_file_path, label)	build torch.dataloader
`evaluate`([evaluate_steps, _ray_trace_ctx])
`get_rows_count`(filename, *[, _ray_trace_ctx])
`get_stop_training`(*[, _ray_trace_ctx])
`get_weights`(*[, _ray_trace_ctx])
`init_training`(callbacks[, epochs, steps, ...])
`load_model`(model_path, *[, _ray_trace_ctx])
`on_epoch_begin`(epoch, *[, _ray_trace_ctx])
`on_epoch_end`(epoch, *[, _ray_trace_ctx])
`on_train_begin`(*[, _ray_trace_ctx])
`on_train_end`(*[, _ray_trace_ctx])
`predict`([predict_steps, _ray_trace_ctx])
`save_model`(model_path, *[, _ray_trace_ctx])
`set_validation_metrics`(global_metrics, *[, ...])
`set_weights`(weights, *[, _ray_trace_ctx])	set weights of client model
`train_step`(updates, cur_steps, train_steps, *)	Accept ps model params,then do local train
`transform_metrics`(logs[, stage, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])