nvflare.client.in_process.api module

class InProcessClientAPI(task_metadata: dict, result_check_interval: float = 2.0)

Bases: APISpec

Initializes the InProcessClientAPI.

Parameters:

• task_metadata (dict) – task metadata, added to client_config.

• result_check_interval (float) – how often to check if result is availabe.

clear()

Clears the cache.

Example

nvflare.client.clear()

get_config() → Dict

Gets the ClientConfig dictionary.

Returns:

A dict of the configuration used in Client API.

Example

config = nvflare.client.get_config()

get_job_id() → str

Gets job id.

Returns:

The current job id.

Example

job_id = nvflare.client.get_job_id()

get_site_name() → str

Gets site name.

Returns:

The site name of this client.

Example

site_name = nvflare.client.get_site_name()

get_task_name() → str

Gets task name.

Returns:

The task name.

Example

task_name = nvflare.client.get_task_name()

init(rank: str | None = None, config: Dict | None = None)

Initializes NVFlare Client API environment.

Parameters:

• config (Union[str, Dict]) – config dictionary.

• rank (str) – local rank of the process. It is only useful when the training script has multiple worker processes. (for example multi GPU)

is_evaluate() → bool

Returns whether the current task is an evaluate task.

Returns:

True, if the current task is an evaluate task. False, otherwise.

Example

if nvflare.client.is_evaluate():
# perform evaluate task on received model
    ...

is_running() → bool

Returns whether the NVFlare system is up and running.

Returns:

True, if the system is up and running. False, otherwise.

Example

while nvflare.client.is_running():
    # receive model, perform task, send model, etc.
    ...

is_submit_model() → bool

Returns whether the current task is a submit_model task.

Returns:

True, if the current task is a submit_model. False, otherwise.

Example

if nvflare.client.is_submit_model():
# perform submit_model task to obtain the best local model
    ...

is_train() → bool

Returns whether the current task is a training task.

Returns:

True, if the current task is a training task. False, otherwise.

Example

if nvflare.client.is_train():
# perform train task on received model
    ...

log(key: str, value: Any, data_type: AnalyticsDataType, **kwargs)

Logs a key value pair.

We suggest users use the high-level APIs in nvflare/client/tracking.py

Parameters:

• key (str) – key string.

• value (Any) – value to log.

• data_type (AnalyticsDataType) – the data type of the “value”.

• kwargs – additional arguments to be included.

Returns:

whether the key value pair is logged successfully

Example

log(
    key=tag,
    value=scalar,
    data_type=AnalyticsDataType.SCALAR,
    global_step=global_step,
    writer=LogWriterName.TORCH_TB,
    **kwargs,
)

prepare_client_config(config)

receive(timeout: float | None = None) → FLModel | None

Receives model from NVFlare side.

Returns:

An FLModel received.

Example

nvflare.client.receive()

send(model: FLModel, clear_cache: bool = True) → None

Sends the model to NVFlare side.

Parameters:

• fl_model (FLModel) – Sends a FLModel object.

• clear_cache (bool) – clear cache after send.

Example

nvflare.client.send(fl_model=FLModel(...))

set_meta(meta: dict)

system_info() → Dict

Gets NVFlare system information.

System information will be available after a valid FLModel is received. It does not retrieve information actively.

Note

system information includes job id and site name.

Returns: A dict of system information.

Example

sys_info = nvflare.client.system_info()