nvflare.app_opt.tensor_stream.client module

class TensorClientStreamer(format: str = 'pytorch', tasks: list[str] | None = None, tensor_send_timeout=30.0)

Bases: FLComponent

TensorClientSender handles receiving task data and sending task results tensors from/to server.

It uses a StreamableEngine, TensorReceiver, and TensorSender to manage tensor streaming on the client side. .. attribute:: format

The format of the tensors to send. Default is “pytorch”.

type:

str

tensor_send_timeout

Timeout for tensor entry transfer operations. Default is 30.0 seconds.

Type:

float

engine

The StreamableEngine used for tensor streaming.

Type:

StreamableEngine

sender

The TensorSender used to send tensors to the server.

Type:

TensorSender

receiver

The TensorReceiver used to receive tensors from the server.

Type:

TensorReceiver

initialize(fl_ctx)

Initializes the TensorClientStreamer component.

handle_event(event_type, fl_ctx)

Handles events for the TensorSender component.

send_tensors_to_server(fl_ctx)

Sends tensors to the server before sending the task result.

Initialize the TensorClientStreamer component.

Parameters:

• format (str) – The format of the tensors to send. Default is ExchangeFormat.TORCH.

• tasks (list[str]) – The list of tasks to send tensors for. Default is None, which means the “train” task.

• tensor_send_timeout (float) – Timeout for tensor entry transfer operations. Default is 30.0 seconds.

handle_event(event_type: str, fl_ctx: FLContext)

Handle events for the TensorSender component.

Parameters:

• event_type (str) – The type of event to handle.

• fl_ctx (FLContext) – The FLContext for the current operation.

initialize(fl_ctx: FLContext)

Initialize the TensorClientStreamer component. :param fl_ctx: The FLContext for the current operation. :type fl_ctx: FLContext

send_tensors_to_server(fl_ctx: FLContext)

Sends tensors to the server before sending the task result.

Parameters:

fl_ctx (FLContext) – The FLContext for the current operation.