ibm_aigov_facts_client.utils.runs.runs_utils module

class Runs(root_directory=None)

Bases: object

Utilities to explore runs within any experiment.

list_runs_by_experiment(experiment_id: str, order_by: List[str] | None = None) → DataFrame

List all runs under any experiment

Parameters:

  • experiment_id (str) – ID of the experiment.

  • order_by – List of order_by clauses. Currently supported values are metric.key, parameter.key, tag.key.For example, order_by=["tag.release ASC", "metric.training_score DESC"]

Returns:

DataFrame object that satisfy the search expressions.

Return type:

Pandas.DataFrame

A way you might use me is:

>>> client.runs.list_runs_by_experiment("1")

>>> client.runs.list_runs_by_experiment("1", order_by=["metric.training_score DESC"]))
get_current_run_id()

Shows current active run id.

Returns:

str

A way you might use me is:

>>> client.runs.get_current_run_id()
log_metric(run_id: str, key: str, value: float, step: int | None = None) → None

Log a metric against the run ID.

Parameters:

  • run_id (str) – The unique id for run.

  • key (str) – Metric name.

  • value (float) – Metric value (float).

  • step (int) – Integer training step (iteration) at which was the metric calculated. Defaults to 0.

Returns:

None

A way you might use me is:

>>> client.runs.log_metric(run_id, "mae", .77)
log_data(run_id, data, folder)
log_metrics(run_id: str, metrics: Dict[str, float], step: int | None = None) → None

Log multiple metrics for the given run.

Parameters:

  • run_id (str) – The unique id for run.

  • metrics (dict) – Dictionary of metric_name: String -> value: Float.

  • step (int) – Integer training step (iteration) at which was the metric calculated. Defaults to 0.

Returns:

None

A way you might use me is:

>>> client.runs.log_metrics(run_id, {"mse": 2000.00, "rmse": 50.00})
log_param(run_id: str, key: str, value: Any) → None

Log a param against the run ID.

Parameters:

  • run_id (str) – The unique id for run.

  • key (str) – Param name.

  • value – Param value.Value is converted to a string.

Returns:

None

A way you might use me is:

>>> client.runs.log_param(run_id, "c", 1)
log_params(run_id, params: Dict[str, Any]) → None

Log multiple params for the given run.

Parameters:

  • run_id (str) – The unique id for run.

  • params (dict) – Dictionary of String -> value: (String, but will be string-ified if not)

Returns:

None

A way you might use me is:

>>> client.runs.log_params(run_id, {"n_estimators": 3, "random_state": 42})
set_tags(run_id: str, tags: Dict[str, Any]) → None

Log multiple tags for the given run.

Parameters:

  • run_id (str) – The unique id for run.

  • tags (dict) – Dictionary of tags names: String -> value: (String, but will be string-ified if not)

Returns:

None

A way you might use me is:

>>> client.runs.set_tags(run_id, {"engineering": "ML Platform",
"release.candidate": "RC1"})