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Experimental Tracking APIs

fedml.log()​

Usage

fedml.log(
    metrics: dict,
    step: int = None,
    customized_step_key: str = None,
    commit: bool = True) -> None

Arguments

  • metrics (dict): A dictionary object for metrics, e.g., {"accuracy": 0.3, "loss": 2.0}.
  • step (int=None): Set the index for current metric. If this value is None, then step will be the current global step counter.
  • customized_step_key (str=None): Specify the customized step key, which must be one of the keys in the metrics dictionary.
  • commit (bool=True): If commit is False, the metrics dictionary will be saved to memory and won't be committed until commit is True.

Returns
None

log dictionary of metric data to the TensorOpera AI Platform.

Examples

fedml.log({"ACC": 0.1})
fedml.log({"acc": 0.11})
fedml.log({"acc": 0.2})
fedml.log({"acc": 0.3})

fedml.log({"acc": 0.31}, step=1)
fedml.log({"acc": 0.32, "x_index": 2}, step=2, customized_step_key="x_index")
fedml.log({"loss": 0.33}, customized_step_key="x_index", commit=False)
fedml.log({"acc": 0.34}, step=4, customized_step_key="x_index", commit=True)

fedml.log_artifact()​

Usage

fedml.log_artifact(
    artifact: Artifact,
    version=None,
    run_id=None,
    edge_id=None) -> None

Arguments

  • artifact (Artifact): An artifact object, e.g., file, log, model, etc.
  • version (str=None): The version of TensorOpera AI Platform, options: dev, test, release. Default is release (fedml.ai).
  • run_id (str=None): Run id for the artifact object. Default is None, which will be filled automatically.
  • edge_id (str=None): Edge id for current device. Default is None, which will be filled automatically.

Returns
None

log artifacts to the TensorOpera AI Platform (fedml.ai), such as file, log, model, etc.

Examples

artifact = fedml.Artifact(name="general-file", type=fedml.ARTIFACT_TYPE_NAME_GENERAL)
artifact.add_file("./requirements.txt")
artifact.add_dir("./config")
fedml.log_artifact(artifact)

artifact = fedml.Artifact(name="log-file", type=fedml.ARTIFACT_TYPE_NAME_LOG)
artifact.add_file("./log_file")
artifact.add_dir("./log_dir")
fedml.log_artifact(artifact)

artifact = fedml.Artifact(name="source-file", type=fedml.ARTIFACT_TYPE_NAME_SOURCE)
artifact.add_file("./run.py")
artifact.add_dir("./src")
fedml.log_artifact(artifact)

artifact = fedml.Artifact(name="dataset-file", type=fedml.ARTIFACT_TYPE_NAME_DATASET)
artifact.add_file("./mnist.dataset")
artifact.add_dir("./dataset")
fedml.log_artifact(artifact)

artifact = fedml.Artifact(name="model-file", type=fedml.ARTIFACT_TYPE_NAME_MODEL)
artifact.add_file("./cv-model")
artifact.add_dir("./model_dir")
fedml.log_artifact(artifact)

fedml.log_model()​

Usage

fedml.log_model(
    model_name, 
    model_file_path, 
    version=None) -> None

Arguments

  • model_name (str): model name.
  • model_file_path (str): The file path of model name.
  • version (str=None): The version of TensorOpera AI Platform, options: dev, test, release. Default is release (fedml.ai).

Returns
None

log model to the TensorOpera AI Platform (fedml.ai).

Examples

fedml.log_model("cv-model", "./cv-model.bin")