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

Submodules#

secretflow.ml.nn.fl.backend.tensorflow.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`	`ActorProxy(PYUFedAvgG)` 的别名

class secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_avg_g.FedAvgG(builder_base: Callable[[], Model], *, _ray_trace_ctx=None)[源代码]#

基类：BaseTFModel

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][源代码]#

Accept ps model params,then do local train

参数

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

返回

Parameters after local training

secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_avg_g.PYUFedAvgG[源代码]#

ActorProxy(PYUFedAvgG) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.tensorflow.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`	`ActorProxy(PYUFedAvgU)` 的别名

class secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_avg_u.FedAvgU(builder_base: Callable[[], Model], *, _ray_trace_ctx=None)[源代码]#

基类：BaseTFModel

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][源代码]#

Accept ps model params, then do local train

参数

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

返回

Parameters after local training

secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_avg_u.PYUFedAvgU[源代码]#

ActorProxy(PYUFedAvgU) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.tensorflow.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`	`ActorProxy(PYUFedAvgW)` 的别名

class secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_avg_w.FedAvgW(builder_base: Callable[[], Model], *, _ray_trace_ctx=None)[源代码]#

基类：BaseTFModel

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][源代码]#

Accept ps model params, then do local train

参数

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

返回

Parameters after local training

secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_avg_w.PYUFedAvgW[源代码]#

ActorProxy(PYUFedAvgW) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.tensorflow.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`	`ActorProxy(PYUFedProx)` 的别名

class secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_prox.FedProx(builder_base: Callable[[], Model], *, _ray_trace_ctx=None)[源代码]#

基类：BaseTFModel

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)[源代码]#

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

Accept ps model params,then do local train

参数

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

返回

Parameters after local training

secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_prox.PYUFedProx[源代码]#

ActorProxy(PYUFedProx) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`w_norm`(w1, w2, *[, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.tensorflow.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`	`ActorProxy(PYUFedSCR)` 的别名

class secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_scr.FedSCR(builder_base: Callable[[], Model], *, _ray_trace_ctx=None)[源代码]#

基类：BaseTFModel

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[[], Model], *, _ray_trace_ctx=None)[源代码]#

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

Accept ps model params,then do local train

参数

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

返回

Parameters after local training

secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_scr.PYUFedSCR[源代码]#

ActorProxy(PYUFedSCR) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.tensorflow.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`	`ActorProxy(PYUFedSTC)` 的别名

class secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_stc.FedSTC(builder_base: Callable[[], Model], *, _ray_trace_ctx=None)[源代码]#

基类：BaseTFModel

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[[], Model], *, _ray_trace_ctx=None)[源代码]#

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

Accept ps model params,then do local train

参数

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

返回

Parameters after local training

secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_stc.PYUFedSTC[源代码]#

ActorProxy(PYUFedSTC) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`wrap_local_metrics`(*[, _ray_trace_ctx])

Module contents#

Classes:

`PYUFedAvgG`	`ActorProxy(PYUFedAvgG)` 的别名
`PYUFedAvgW`	`ActorProxy(PYUFedAvgW)` 的别名
`PYUFedAvgU`	`ActorProxy(PYUFedAvgU)` 的别名
`PYUFedSCR`	`ActorProxy(PYUFedSCR)` 的别名
`PYUFedSTC`	`ActorProxy(PYUFedSTC)` 的别名
`PYUFedProx`	`ActorProxy(PYUFedProx)` 的别名

secretflow.ml.nn.fl.backend.tensorflow.strategy.PYUFedAvgG[源代码]#

ActorProxy(PYUFedAvgG) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.tensorflow.strategy.PYUFedAvgW[源代码]#

ActorProxy(PYUFedAvgW) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.tensorflow.strategy.PYUFedAvgU[源代码]#

ActorProxy(PYUFedAvgU) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.tensorflow.strategy.PYUFedSCR[源代码]#

ActorProxy(PYUFedSCR) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.tensorflow.strategy.PYUFedSTC[源代码]#

ActorProxy(PYUFedSTC) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`wrap_local_metrics`(*[, _ray_trace_ctx])

secretflow.ml.nn.fl.backend.tensorflow.strategy.PYUFedProx[源代码]#

ActorProxy(PYUFedProx) 的别名 Methods:

`__init__`(args, *kwargs)	Abstraction device object base class.
`build_dataset`(x[, y, s_w, sampling_rate, ...])	build tf.data.Dataset
`build_dataset_from_csv`(csv_file_path, label)	build tf.data.Dataset
`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
`w_norm`(w1, w2, *[, _ray_trace_ctx])
`wrap_local_metrics`(*[, _ray_trace_ctx])