nvflare.recipe.fed_task module

class FedTaskRecipe(*, name: str = 'fed_task', task_name: str = 'task', min_clients: int, num_clients: int | None = None, min_responses: int | None = None, timeout: int = 0, task_data: dict | None = None, task_meta: dict | None = None, task_script: str, task_args: str = '', launch_external_process: bool = False, command: str = 'python3 -u', framework: FrameworkType = FrameworkType.RAW, server_expected_format: ExchangeFormat = ExchangeFormat.RAW, params_transfer_type: TransferType = TransferType.FULL, launch_once: bool = True, shutdown_timeout: float = 0.0, client_memory_gc_rounds: int = 0, cuda_empty_cache: bool = False)[source]

Bases: Recipe

A model-free recipe for running one federated task on participating clients.

This recipe is intended for one-round workflows that do not have a global model lifecycle, such as embedding extraction, preprocessing, feature generation, local evaluation, or other client-side jobs coordinated by the server.

Users are responsible for ensuring that task_script accepts the supplied task_args and any task_data or task_meta payloads it consumes.

Parameters:

  • name – Name of the federated job. Defaults to “fed_task”.

  • task_name – Name of the task sent to clients. Defaults to “task”.

  • min_clients – Minimum number of clients required to start the job.

  • num_clients – Number of clients to sample for the task. If None, all available clients are used.

  • min_responses – Minimum number of task results to wait for. If None, waits for all selected clients.

  • timeout – Task timeout in seconds. Defaults to 0, meaning no timeout.

  • task_data – Optional params dict sent to each client as FLModel.params.

  • task_meta – Optional metadata dict sent to each client as FLModel.meta.

  • task_script – Path to the client script.

  • task_args – Command line arguments passed to the client script.

  • launch_external_process – Whether to launch the script in an external process.

  • command – Command used when launch_external_process is True.

  • framework – Framework used by ScriptRunner for parameter exchange. Defaults to RAW.

  • server_expected_format – Server-side expected parameter format. Defaults to RAW.

  • params_transfer_type – Parameter transfer type. Defaults to FULL.

  • launch_once – Whether an external process is launched once for the whole job.

  • shutdown_timeout – Seconds to wait before external process shutdown.

  • client_memory_gc_rounds – Run client memory cleanup every N rounds. Set 0 to disable.

  • cuda_empty_cache – Whether client memory cleanup also empties the CUDA cache.

Example

>>> from nvflare.recipe import FedTaskRecipe, SimEnv
>>>
>>> recipe = FedTaskRecipe(
...     name="extract_embeddings",
...     task_name="embed",
...     min_clients=2,
...     task_script="client.py",
...     task_args="--data-root /data --out /tmp/embeddings",
... )
>>> run = recipe.execute(SimEnv(num_clients=2))

This is base class of a recipe. Recipes are implemented by jobs. A concrete recipe must provide the job for recipe implementation.

Parameters:

job – the job that implements the recipe.