nvflare.app_opt.pt.recipes.fedeval module

class EvalController(persistor_id: str, timeout: int)[source]

Bases: ModelController

Model Controller API for FLModel-based Controller.

Parameters:

persistor_id (str, optional) – ID of the persistor component. Defaults to AppConstants.DEFAULT_PERSISTOR_ID (“persistor”).

run()[source]

Main run routine for the controller workflow.

class FedEvalRecipe(*, name: str = 'eval', model: Any | Dict[str, Any], eval_ckpt: str, min_clients: int, eval_script: str, eval_args: str = '', launch_external_process: bool = False, command: str = 'python3 -u', server_expected_format: ExchangeFormat = ExchangeFormat.NUMPY, validation_timeout: int = 6000, per_site_config: Dict[str, Dict] | None = None, client_memory_gc_rounds: int = 0, cuda_empty_cache: bool = False)[source]

Bases: Recipe

A recipe for federated evaluation of a PyTorch model across multiple sites.

This recipe sets up a federated evaluation workflow where a global model from the server is sent to multiple clients for evaluation. Each client evaluates the model on their local data and reports metrics back to the server.

The recipe configures: - A federated job with an initial model to evaluate - EvalController for coordinating federated evaluation across clients - Script runners for client-side evaluation execution

Parameters:

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

  • model – Model structure to evaluate. Can be: - An instantiated nn.Module (e.g., Net()) - A dict config: {“class_path”: “module.ClassName”, “args”: {…}}

  • eval_ckpt – Absolute path to pre-trained checkpoint file (.pt, .pth, etc.). Required for evaluation - specifies which weights to evaluate. The file may not exist locally (server-side path).

  • min_clients – Minimum number of clients required to start evaluation.

  • eval_script – Path to the evaluation script that will be executed on each client.

  • eval_args – Command line arguments to pass to the evaluation script. Defaults to “”.

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

  • command – If launch_external_process=True, command to run script (prepended to script). Defaults to “python3 -u”.

  • server_expected_format – What format to exchange the parameters between server and client. Defaults to ExchangeFormat.NUMPY.

  • validation_timeout – Timeout for evaluation task in seconds. Defaults to 6000.

  • per_site_config – Per-site configuration for the evaluation job. Dictionary mapping site names to configuration dicts. Each config dict can contain optional overrides: eval_script, eval_args, launch_external_process, command, server_expected_format. If not provided, the same configuration will be used for all clients. Defaults to None.

Example

Basic usage with model instance:

```python from nvflare.app_opt.pt.recipes.fedeval import FedEvalRecipe from model import Net

recipe = FedEvalRecipe(

name=”eval_job”, model=Net(), eval_ckpt=”/path/to/pretrained_model.pt”, min_clients=2, eval_script=”client.py”, eval_args=”–batch_size 32”,

)

Using dict config:

```python recipe = FedEvalRecipe(

name=”eval_job”, model={“class_path”: “my_module.Net”, “args”: {“num_classes”: 10}}, eval_ckpt=”/path/to/pretrained_model.pt”, min_clients=2, eval_script=”client.py”,

)

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

param job:

the job that implements the recipe.