secretflow.ml.nn.sl#

secretflow.ml.nn.sl.sl_model#

SLModel

Classes:

SLModel([base_model_dict, device_y, ...])

class secretflow.ml.nn.sl.sl_model.SLModel(base_model_dict: Dict[Device, Callable[[], tensorflow.keras.Model]] = {}, device_y: PYU = None, model_fuse: Callable[[], tensorflow.keras.Model] = None, compressor: Compressor = None, dp_strategy_dict: Dict[Device, DPStrategy] = None, random_seed: int = None, strategy='split_nn', **kwargs)[源代码]#

基类：object

Methods:

__init__([base_model_dict, device_y, ...])

Interface for vertical split learning .

handle_data(x[, y, sample_weight, ...])

fit(x, y[, batch_size, epochs, verbose, ...])

Vertical split learning training interface

predict(x[, batch_size, verbose, ...])

Vertical split learning offline prediction interface

evaluate(x, y[, batch_size, sample_weight, ...])

Vertical split learning evaluate interface

save_model([base_model_path, ...])

Vertical split learning save model interface

load_model([base_model_path, ...])

Vertical split learning load model interface

export_model([base_model_path, ...])

Vertical split learning export model interface

get_cpus()

__init__(base_model_dict: Dict[Device, Callable[[], tensorflow.keras.Model]] = {}, device_y: PYU = None, model_fuse: Callable[[], tensorflow.keras.Model] = None, compressor: Compressor = None, dp_strategy_dict: Dict[Device, DPStrategy] = None, random_seed: int = None, strategy='split_nn', **kwargs)[源代码]#

Interface for vertical split learning .. attribute:: base_model_dict

Basemodel dictionary, key is PYU, value is the Basemodel defined by party.

device_y#

Define which model have label.

model_fuse#

Fuse model definition.

compressor#

Define strategy tensor compression algorithms to speed up transmission.

dp_strategy_dict#

Dp strategy dictionary.

random_seed#

If specified, the initial value of the model will remain the same, which ensures reproducible.

strategy#

Strategy of split learning.

handle_data(x: Union[VDataFrame, FedNdarray, List[Union[HDataFrame, VDataFrame, FedNdarray]]], y: Optional[Union[FedNdarray, VDataFrame, PYUObject]] = None, sample_weight: Optional[Union[FedNdarray, VDataFrame]] = None, batch_size=32, shuffle=False, epochs=1, stage='train', random_seed=1234, dataset_builder: Optional[Dict] = None)[源代码]#
fit(x: Union[VDataFrame, FedNdarray, List[Union[HDataFrame, VDataFrame, FedNdarray]]], y: Union[VDataFrame, FedNdarray, PYUObject], batch_size=32, epochs=1, verbose=1, callbacks=None, validation_data=None, shuffle=False, sample_weight=None, validation_freq=1, dp_spent_step_freq=None, dataset_builder: Optional[Callable[[List], Tuple[int, Iterable]]] = None, audit_log_dir: Optional[str] = None, audit_log_params: dict = {}, random_seed: Optional[int] = None)[源代码]#

Vertical split learning training interface

参数:

  • x – Input data. It could be:

  • VDataFrame (-) – a vertically aligned dataframe.

  • FedNdArray (-) – a vertically aligned ndarray.

  • List[Union[HDataFrame (-) – list of dataframe or ndarray.

  • VDataFrame – list of dataframe or ndarray.

  • FedNdarray]] – list of dataframe or ndarray.

  • y – Target data. It could be a VDataFrame or FedNdarray which has only one partition, or a PYUObject.

  • batch_size – Number of samples per gradient update.

  • epochs – Number of epochs to train the model

  • verbose – 0, 1. Verbosity mode

  • callbacks – List of keras.callbacks.Callback instances.

  • validation_data – Data on which to validate

  • shuffle – Whether shuffle dataset or not

  • validation_freq – specifies how many training epochs to run before a new validation run is performed

  • sample_weight – weights for the training samples

  • dp_spent_step_freq – specifies how many training steps to check the budget of dp

  • dataset_builder – Callable function, its input is x or [x, y] if y is set, it should return a dataset.

  • audit_log_dir – If audit_log_dir is set, audit model will be enabled

  • audit_log_params – Kwargs for saving audit model, eg: {‘save_traces’=True, ‘save_format’=’h5’}

  • random_seed – seed for prg, will only affect dataset shuffle

predict(x: Union[VDataFrame, FedNdarray, List[Union[HDataFrame, VDataFrame, FedNdarray]]], batch_size=32, verbose=0, dataset_builder: Optional[Callable[[List], Tuple[int, Iterable]]] = None, compress: bool = False)[源代码]#

Vertical split learning offline prediction interface

参数:

  • x – Input data. It could be:

  • VDataFrame (-) – a vertically aligned dataframe.

  • FedNdArray (-) – a vertically aligned ndarray.

  • List[Union[HDataFrame (-) – list of dataframe or ndarray.

  • VDataFrame – list of dataframe or ndarray.

  • FedNdarray]] – list of dataframe or ndarray.

  • batch_size – Number of samples per gradient update, Int

  • verbose – 0, 1. Verbosity mode

  • dataset_builder – Callable function, its input is x or [x, y] if y is set, it should return steps_per_epoch and iterable dataset. Dataset builder is mainly for building graph dataset.

  • compress – Whether to use compressor to compress cross device data.

evaluate(x: Union[VDataFrame, FedNdarray, List[Union[HDataFrame, VDataFrame, FedNdarray]]], y: Union[VDataFrame, FedNdarray, PYUObject], batch_size: int = 32, sample_weight=None, verbose=1, dataset_builder: Dict = None, random_seed: int = None, compress: bool = False)[源代码]#

Vertical split learning evaluate interface

参数:

  • x – Input data. It could be:

  • VDataFrame (-) – a vertically aligned dataframe.

  • FedNdArray (-) – a vertically aligned ndarray.

  • List[Union[HDataFrame (-) – list of dataframe or ndarray.

  • VDataFrame – list of dataframe or ndarray.

  • FedNdarray]] – list of dataframe or ndarray.

  • y – Target data. It could be a VDataFrame or FedNdarray which has only one partition, or a PYUObject.

  • batch_size – Integer or Dict. Number of samples per batch of computation. If unspecified, batch_size will default to 32.

  • sample_weight – Optional Numpy array of weights for the test samples, used for weighting the loss function.

  • verbose – Verbosity mode. 0 = silent, 1 = progress bar.

  • dataset_builder – Callable function, its input is x or [x, y] if y is set, it should return dataset.

  • random_seed – Seed for prgs, will only affect shuffle

  • compress – Whether to use compressor to compress cross device data.

返回:

federate evaluate result

返回类型:

metrics

save_model(base_model_path: Optional[Union[str, Dict[PYU, str]]] = None, fuse_model_path: Optional[str] = None, is_test=False, **kwargs)[源代码]#

Vertical split learning save model interface

参数:

  • base_model_path – base model path,only support format like ‘a/b/c’, where c is the model name

  • fuse_model_path – fuse model path

  • is_test – whether is test mode

  • kwargs – other argument inherit from tf or torch

示例

>>> save_params = {'save_traces' : True,
>>>                'save_format' : 'h5',}
>>> slmodel.save_model(base_model_path,
>>>                    fuse_model_path,)
>>>                    is_test=True,)
>>> # just passing params in
>>> slmodel.save_model(base_model_path,
>>>                    fuse_model_path,)
>>>                    is_test=True,
>>>                    save_traces=True,
>>>                    save_format='h5')
load_model(base_model_path: Optional[Union[str, Dict[PYU, str]]] = None, fuse_model_path: Optional[str] = None, is_test=False, base_custom_objects=None, fuse_custom_objects=None)[源代码]#

Vertical split learning load model interface

参数:

  • base_model_path – base model path

  • fuse_model_path – fuse model path

  • is_test – whether is test mode

  • base_custom_objects – Optional dictionary mapping names (strings) to custom classes or functions of the base model to be considered during deserialization

  • fuse_custom_objects – Optional dictionary mapping names (strings) to custom classes or functions of the base model to be considered during deserialization.

export_model(base_model_path: Optional[Union[str, Dict[PYU, str]]] = None, fuse_model_path: Optional[str] = None, save_format='tf', is_test=False, **kwargs)[源代码]#

Vertical split learning export model interface

参数:

  • base_model_path – base model path,only support format like ‘a/b/c’, where c is the model name

  • fuse_model_path – fuse model path

  • save_format – what format to export

  • kwargs – other argument inherit from onnx safer

get_cpus() → List[int][源代码]#

secretflow.ml.nn.sl.strategy_dispatcher#

Classes:

Dispatcher()

Functions:

register_strategy([_cls, strategy_name, ...])

register new strategy

dispatch_strategy(name, backend, *args, **kwargs)

strategy dispatcher
class secretflow.ml.nn.sl.strategy_dispatcher.Dispatcher[源代码]#

基类：object

Methods:

__init__()

register(name, check_skip_grad, cls)

dispatch(name, backend, *args, **kwargs)

__init__()[源代码]#
register(name, check_skip_grad, cls)[源代码]#
dispatch(name, backend, *args, **kwargs)[源代码]#
secretflow.ml.nn.sl.strategy_dispatcher.register_strategy(_cls=None, *, strategy_name=None, backend=None, check_skip_grad=False)[源代码]#

register new strategy

参数:

  • _cls –

  • strategy_name – name of strategy

  • backend – backend of strategy(tensorflow/torch)

  • check_skip_grad – whether this strategy need check skip gradient

Returns:

secretflow.ml.nn.sl.strategy_dispatcher.dispatch_strategy(name, backend, *args, **kwargs)[源代码]#

strategy dispatcher

参数:

  • name – name of strategy, str

  • *args –

  • **kwargs –

Returns: